Multiphase Return Trajectory Optimization Based on Hybrid Algorithm
Directory of Open Access Journals (Sweden)
Yi Yang
2016-01-01
Full Text Available A hybrid trajectory optimization method consisting of Gauss pseudospectral method (GPM and natural computation algorithm has been developed and utilized to solve multiphase return trajectory optimization problem, where a phase is defined as a subinterval in which the right-hand side of the differential equation is continuous. GPM converts the optimal control problem to a nonlinear programming problem (NLP, which helps to improve calculation accuracy and speed of natural computation algorithm. Through numerical simulations, it is found that the multiphase optimal control problem could be solved perfectly.
Velocity trajectory optimization in Hybrid Electric trucks
Keulen, T. van; Jager, B. de; Foster, D.L.; Steinbuch, M.
2010-01-01
Hybrid Electric Vehicles (HEVs) enable fuel savings by re-using kinetic and potential energy that was recovered and stored in a battery during braking or driving down hill. Besides, the vehicle itself can be seen as a storage device, where kinetic energy can be stored and retrieved by changing the
Optimal Lunar Landing Trajectory Design for Hybrid Engine
Directory of Open Access Journals (Sweden)
Dong-Hyun Cho
2015-01-01
Full Text Available The lunar landing stage is usually divided into two parts: deorbit burn and powered descent phases. The optimal lunar landing problem is likely to be transformed to the trajectory design problem on the powered descent phase by using continuous thrusters. The optimal lunar landing trajectories in general have variety in shape, and the lunar lander frequently increases its altitude at the initial time to obtain enough time to reduce the horizontal velocity. Due to the increment in the altitude, the lunar lander requires more fuel for lunar landing missions. In this work, a hybrid engine for the lunar landing mission is introduced, and an optimal lunar landing strategy for the hybrid engine is suggested. For this approach, it is assumed that the lunar lander retrofired the impulsive thruster to reduce the horizontal velocity rapidly at the initiated time on the powered descent phase. Then, the lunar lander reduced the total velocity and altitude for the lunar landing by using the continuous thruster. In contradistinction to other formal optimal lunar landing problems, the initial horizontal velocity and mass are not fixed at the start time. The initial free optimal control theory is applied, and the optimal initial value and lunar landing trajectory are obtained by simulation studies.
Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control
Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.
2015-01-01
The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.
Collins, P.J.
2005-01-01
In this paper, we present a general framework for describing and studying hybrid systems. We represent the trajectories of the system as functions on a hybrid time domain, and the system itself by its trajectory space, which is the set of all possible trajectories. The trajectory space is given a na
Aircraft Trajectory Optimization Using Parametric Optimization Theory
Valenzuela Romero, Alfonso
2012-01-01
In this thesis, a study of the optimization of aircraft trajectories using parametric optimization theory is presented. To that end, an approach based on the use of predefined trajectory patterns and parametric optimization is proposed. The trajectory pat
Moon Landing Trajectory Optimization
Directory of Open Access Journals (Sweden)
Ibrahim Mustafa MEHEDI
2016-03-01
Full Text Available Trajectory optimization is a crucial process during the planning phase of a spacecraft landing mission. Once a trajectory is determined, guidance algorithms are created to guide the vehicle along the given trajectory. Because fuel mass is a major driver of the total vehicle mass, and thus mission cost, the objective of most guidance algorithms is to minimize the required fuel consumption. Most of the existing algorithms are termed as “near-optimal” regarding fuel expenditure. The question arises as to how close to optimal are these guidance algorithms. To answer this question, numerical trajectory optimization techniques are often required. With the emergence of improved processing power and the application of new methods, more direct approaches may be employed to achieve high accuracy without the associated difficulties in computation or pre-existing knowledge of the solution. An example of such an approach is DIDO optimization. This technique is applied in the current research to find these minimum fuel optimal trajectories.
OPTIMAL TRAJECTORY PLANNING OF MANIPULATORS: A REVIEW
Directory of Open Access Journals (Sweden)
ATEF A. ATA
2007-04-01
Full Text Available Optimal motion planning is very important to the operation of robot manipulators. Its main target is the generation of a trajectory from start to goal that satisfies objectives, such as minimizing path traveling distance or time interval, lowest energy consumption or obstacle avoidance and satisfying the robot’s kinematics and dynamics. Review, discussion and analysis of optimization techniques to find the optimal trajectory either in Cartesian space or joint space are presented and investigated. Optimal trajectory selection approaches such as kinematics and dynamics techniques with various constraints are presented and explained. Although the kinematics approach is simple and straight forward, it will experience some problems in implementation because of lack of Inertia and torque constraints. The application of Genetic Algorithms to find the optimal trajectory of manipulators especially in the obstacle avoidance is also highlighted. Combining the Genetic Algorithms with other classical optimization methods proves to have better performance as a hybrid optimization technique.
Trajectory Optimization: OTIS 4
Riehl, John P.; Sjauw, Waldy K.; Falck, Robert D.; Paris, Stephen W.
2010-01-01
The latest release of the Optimal Trajectories by Implicit Simulation (OTIS4) allows users to simulate and optimize aerospace vehicle trajectories. With OTIS4, one can seamlessly generate optimal trajectories and parametric vehicle designs simultaneously. New features also allow OTIS4 to solve non-aerospace continuous time optimal control problems. The inputs and outputs of OTIS4 have been updated extensively from previous versions. Inputs now make use of objectoriented constructs, including one called a metastring. Metastrings use a greatly improved calculator and common nomenclature to reduce the user s workload. They allow for more flexibility in specifying vehicle physical models, boundary conditions, and path constraints. The OTIS4 calculator supports common mathematical functions, Boolean operations, and conditional statements. This allows users to define their own variables for use as outputs, constraints, or objective functions. The user-defined outputs can directly interface with other programs, such as spreadsheets, plotting packages, and visualization programs. Internally, OTIS4 has more explicit and implicit integration procedures, including high-order collocation methods, the pseudo-spectral method, and several variations of multiple shooting. Users may switch easily between the various methods. Several unique numerical techniques such as automated variable scaling and implicit integration grid refinement, support the integration methods. OTIS4 is also significantly more user friendly than previous versions. The installation process is nearly identical on various platforms, including Microsoft Windows, Apple OS X, and Linux operating systems. Cross-platform scripts also help make the execution of OTIS and post-processing of data easier. OTIS4 is supplied free by NASA and is subject to ITAR (International Traffic in Arms Regulations) restrictions. Users must have a Fortran compiler, and a Python interpreter is highly recommended.
Multiple Satellite Trajectory Optimization
2004-12-01
SOLVING OPTIMAL CONTROL PROBLEMS ........................................5...OPTIMIZATION A. SOLVING OPTIMAL CONTROL PROBLEMS The driving principle used to solve optimal control problems was first formalized by the Soviet...methods and processes of solving optimal control problems , this section will demonstrate how the formulations work as expected. Once coded, the
混合动力发动机动态轨迹优化研究%Research on Dynamic Trajectory Optimization of Hybrid Engine
Institute of Scientific and Technical Information of China (English)
张文学; 张幽彤; 孙帅; 杨军伟
2015-01-01
Dynamic working conditions run through the whole operational process of hybrid engines, which endow great significance to its optimization to obtain the optimal dynamic trajectory of hybrid engines to improve both the economy and emission performance. The motor, responding the torque demand of the vehicle rapidly, provides the possibility to control the engine working along a specific trace. Aiming at the dynamic trajectory optimization of hybrid engines, a dynamic process effect field has been built to analysis dynamic characteristics based on field coordination principle, which transform the dynamic process optimization to path optimization in a field space. Utilizing space trajectory optimization method, the path optimization problem is simplified further to a minimization problem of cumulative number of field effect in a field space and is solved by the steepest gradient method of discrete space, depending on which the optimal trajectories under different objective factors have been achieved. Simulation and experiments results show that the optimal trace algorithm can reach the established goal, which achieves the optimal control in dynamic process.%混合动力发动机动态工况变化贯穿于其运行过程的始终，因此对该动态过程进行优化并获取发动机的最优动态轨迹对于提高发动机经济性和排放性具有重要意义，而驱动电动机对于整车动力需求的补偿也使发动机运行轨迹的控制成为可能。针对混合动力发动机动态轨迹优化问题，采用场协同理论建立混合动力发动机动态工况效应场，并进行动态特性分析，将动态工况的最优化问题转化为工况场空间内的轨迹优化问题。利用空间轨迹优化计算方法将该轨迹优化问题进一步简化为场空间内的场效应累积数最小化问题，进而采用离散空间的最速梯度法进行求解，得到动态过程中针对不同目标因素的最优轨迹。仿真及多样本对
Analysis of Controlled Trajectory Optimization for Canard Trajectory Correction Fuze
Institute of Scientific and Technical Information of China (English)
郭泽荣; 李世义; 申强
2004-01-01
The optimization method of the canard trajectory correction fuze's controlled trajectory phase is researched by using the aerodynamics of aerocraft and the optimal control theory, the trajectory parameters of the controlled trajectory phase based on the least energy cost are determined. On the basis of determining the control starting point and the target point, the optimal trajectory and the variation rule of the normal overload with the least energy cost are provided, when there is no time restriction in the simulation process. The results provide a theoretical basis for the structure design of the canard mechanism.
Perching aerodynamics and trajectory optimization
Wickenheiser, Adam; Garcia, Ephrahim
2007-04-01
Advances in smart materials, actuators, and control architecture have enabled new flight capabilities for aircraft. Perching is one such capability, described as a vertical landing maneuver using in-flight shape reconfiguration in lieu of high thrust generation. A morphing, perching aircraft design is presented that is capable of post stall flight and very slow landing on a vertical platform. A comprehensive model of the aircraft's aerodynamics, with special regard to nonlinear affects such as flow separation and dynamic stall, is discussed. Trajectory optimization using nonlinear programming techniques is employed to show the effects that morphing and nonlinear aerodynamics have on the maneuver. These effects are shown to decrease the initial height and distance required to initiate the maneuver, reduce the bounds on the trajectory, and decrease the required thrust for the maneuver. Perching trajectories comparing morphing versus fixed-configuration and stalled versus un-stalled aircraft are presented. It is demonstrated that a vertical landing is possible in the absence of high thrust if post-stall flight capabilities and vehicle reconfiguration are utilized.
Hybrid Airy Plasmons with Dynamically Steerable Trajectories
Li, Rujiang; Lin, Xiao; Wang, Huaping; Xu, Zhiwei; Chen, Hongsheng
2016-01-01
With the intriguing properties of diffraction-free, self-accelerating, and self-healing, Airy plasmons are promising to be used in the trapping, transporting, and sorting of micro-objects, imaging, and chip scale signal processing. However, the high dissipative loss and the lack of dynamical steerability restrict the implementation of Airy plasmons in these applications. Here we reveal the hybrid Airy plasmons for the first time by taking a hybrid graphene-based plasmonic waveguide in the terahertz (THz) domain as an example. Due to the coupling between an optical mode and a plasmonic mode, the hybrid Airy plasmons can have large propagation lengths and effective transverse deflections, where the transverse waveguide confinements are governed by the hybrid modes with moderate quality factors. Meanwhile, the propagation trajectories of hybrid Airy plasmons are dynamically steerable by changing the chemical potential of graphene. These hybrid Airy plasmons may promote the further discovery of non-diffracting be...
Hybrid Airy plasmons with dynamically steerable trajectories.
Li, Rujiang; Imran, Muhammad; Lin, Xiao; Wang, Huaping; Xu, Zhiwei; Chen, Hongsheng
2017-01-26
With their intriguing diffraction-free, self-accelerating, and self-healing properties, Airy plasmons show promise for use in the trapping, transporting, and sorting of micro-objects, imaging, and chip scale signal processing. However, high dissipative loss and lack of dynamical steerability restrict the implementation of Airy plasmons in these applications. Here we reveal hybrid Airy plasmons for the first time by taking a hybrid graphene-based plasmonic waveguide in the terahertz (THz) domain as an example. Due to coupling between optical modes and plasmonic modes, the hybrid Airy plasmons can have large propagation lengths and effective transverse deflections, where the transverse waveguide confinements are governed by the hybrid modes with moderate quality factors. Meanwhile, the propagation trajectories of the hybrid Airy plasmons are dynamically steerable by changing the chemical potential of graphene. These hybrid Airy plasmons may promote the further discovery of non-diffracting beams along with the emerging developments of optical tweezers and tractor beams.
Simulation Propulsion System and Trajectory Optimization
Hendricks, Eric S.; Falck, Robert D.; Gray, Justin S.
2017-01-01
A number of new aircraft concepts have recently been proposed which tightly couple the propulsion system design and operation with the overall vehicle design and performance characteristics. These concepts include propulsion technology such as boundary layer ingestion, hybrid electric propulsion systems, distributed propulsion systems and variable cycle engines. Initial studies examining these concepts have typically used a traditional decoupled approach to aircraft design where the aerodynamics and propulsion designs are done a-priori and tabular data is used to provide inexpensive look ups to the trajectory ana-ysis. However the cost of generating the tabular data begins to grow exponentially when newer aircraft concepts require consideration of additional operational parameters such as multiple throttle settings, angle-of-attack effects on the propulsion system, or propulsion throttle setting effects on aerodynamics. This paper proposes a new modeling approach that eliminated the need to generate tabular data, instead allowing an expensive propulsion or aerodynamic analysis to be directly integrated into the trajectory analysis model and the entire design problem optimized in a fully coupled manner. The new method is demonstrated by implementing a canonical optimal control problem, the F-4 minimum time-to-climb trajectory optimization using three relatively new analysis tools: Open M-DAO, PyCycle and Pointer. Pycycle and Pointer both provide analytic derivatives and Open MDAO enables the two tools to be combined into a coupled model that can be run in an efficient parallel manner that helps to cost the increased cost of the more expensive propulsion analysis. Results generated with this model serve as a validation of the tightly coupled design method and guide future studies to examine aircraft concepts with more complex operational dependencies for the aerodynamic and propulsion models.
Global 4-D trajectory optimization for spacecraft
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
Global 4-D trajectory(x,y,z,t)is optimized for a spacecraft,which is launched from the Earth to fly around the Sun,just as star-drift of 1437 asteroids in the solar system.The spacecraft trajectory is controlled by low thrust.The performance index of optimal trajectory is to maximize the rendezvous times with the intermediate asteroids,and also maximize the final mass.This paper provides a combined algorithm of global 4-D trajectory optimization.The algorithm is composed of dynamic programming and two-point-boundary algorithm based on optimal control theory.The best 4-D trajectory is obtained:the spacecraft flies passing 55 asteroids,and rendezvous with(following or passing again)asteroids for 454 days,and finally rendezvous with the asteroid 2005SN25 on the day 60521(MJD),the final mass of the spacecraft is 836.53 kg.
Variable length trajectory compressible hybrid Monte Carlo
Nishimura, Akihiko
2016-01-01
Hybrid Monte Carlo (HMC) generates samples from a prescribed probability distribution in a configuration space by simulating Hamiltonian dynamics, followed by the Metropolis (-Hastings) acceptance/rejection step. Compressible HMC (CHMC) generalizes HMC to a situation in which the dynamics is reversible but not necessarily Hamiltonian. This article presents a framework to further extend the algorithm. Within the existing framework, each trajectory of the dynamics must be integrated for the same amount of (random) time to generate a valid Metropolis proposal. Our generalized acceptance/rejection mechanism allows a more deliberate choice of the integration time for each trajectory. The proposed algorithm in particular enables an effective application of variable step size integrators to HMC-type sampling algorithms based on reversible dynamics. The potential of our framework is further demonstrated by another extension of HMC which reduces the wasted computations due to unstable numerical approximations and corr...
Designing Complex Interplanetary Trajectories for the Global Trajectory Optimization Competitions
Izzo, Dario; Simões, Luís F; Märtens, Marcus
2015-01-01
The design of interplanetary trajectories often involves a preliminary search for options that are later refined into one final selected trajectory. It is this broad search that, often being intractable, inspires the international event called Global Trajectory Optimization Competition. In the first part of this chapter, we introduce some fundamental problems of space flight mechanics, building blocks of any attempt to participate successfully in these competitions and we describe the use of the open source software PyKEP to assemble them into a final global solution strategy. In the second part, we formulate an instance of a multiple asteroid rendezvous problem, related to the 7th edition of the competition, and we show step by step how to build a possible solution strategy. We introduce two new techniques useful in the design of this particular mission type: the use of an asteroid phasing value and its surrogates and the efficient computation of asteroid clusters. We show how basic building blocks, sided to...
Helicopter trajectory planning using optimal control theory
Menon, P. K. A.; Cheng, V. H. L.; Kim, E.
1988-01-01
A methodology for optimal trajectory planning, useful in the nap-of-the-earth guidance of helicopters, is presented. This approach uses an adjoint-control transformation along with a one-dimensional search scheme for generating the optimal trajectories. In addition to being useful for helicopter nap-of-the-earth guidance, the trajectory planning solution is of interest in several other contexts, such as robotic vehicle guidance and terrain-following guidance for cruise missiles and aircraft. A distinguishing feature of the present research is that the terrain constraint and the threat envelopes are incorporated in the equations of motion. Second-order necessary conditions are examined.
Trajectory Optimization Design for Morphing Wing Missile
Institute of Scientific and Technical Information of China (English)
Ruisheng Sun; Chao Ming; Chuanjie Sun
2015-01-01
This paper presents a new particle swarm optimization ( PSO) algorithm to optimize the trajectory of morphing⁃wing missile so as to achieve the enlargement of the maximum range. Equations of motion for the two⁃dimensional dynamics are derived by treating the missile as an ideal controllable mass point. An investigation of aerodynamic characteristics of morphing⁃wing missile with varying geometries is performed. After deducing the optimizing trajectory model for maximizing range, a type of discrete method is put forward for taking optimization control problem into nonlinear dynamic programming problem. The optimal trajectory is solved by using PSO algorithm and penalty function method. The simulation results suggest that morphing⁃wing missile has the larger range than the fixed⁃shape missile when launched at supersonic speed, while morphing⁃wing missile has no obvious range increment than the fixed⁃shape missile at subsonic speed.
Trajectory Control and Optimization for Responsive Spacecraft
2012-03-22
functions. The scalar function φ defines the cost associated with the terminal conditions, and is referred to as the Mayer cost. The scalar function L...defines the cost associated with the values of x and u throughout the trajectory, and is referred to as the Lagrange cost. When J contains both a Mayer ...optimal space trajectories and is a fundamental reference in a vast majority of the literature on this subject. [22] Building on Lawden’s work, Jean
Constrained Multi-Level Algorithm for Trajectory Optimization
Adimurthy, V.; Tandon, S. R.; Jessy, Antony; Kumar, C. Ravi
The emphasis on low cost access to space inspired many recent developments in the methodology of trajectory optimization. Ref.1 uses a spectral patching method for optimization, where global orthogonal polynomials are used to describe the dynamical constraints. A two-tier approach of optimization is used in Ref.2 for a missile mid-course trajectory optimization. A hybrid analytical/numerical approach is described in Ref.3, where an initial analytical vacuum solution is taken and gradually atmospheric effects are introduced. Ref.4 emphasizes the fact that the nonlinear constraints which occur in the initial and middle portions of the trajectory behave very nonlinearly with respect the variables making the optimization very difficult to solve in the direct and indirect shooting methods. The problem is further made complex when different phases of the trajectory have different objectives of optimization and also have different path constraints. Such problems can be effectively addressed by multi-level optimization. In the multi-level methods reported so far, optimization is first done in identified sub-level problems, where some coordination variables are kept fixed for global iteration. After all the sub optimizations are completed, higher-level optimization iteration with all the coordination and main variables is done. This is followed by further sub system optimizations with new coordination variables. This process is continued until convergence. In this paper we use a multi-level constrained optimization algorithm which avoids the repeated local sub system optimizations and which also removes the problem of non-linear sensitivity inherent in the single step approaches. Fall-zone constraints, structural load constraints and thermal constraints are considered. In this algorithm, there is only a single multi-level sequence of state and multiplier updates in a framework of an augmented Lagrangian. Han Tapia multiplier updates are used in view of their special role in
Trajectory metaheuristic algorithms to optimize problems combinatorics
Directory of Open Access Journals (Sweden)
Natalia Alancay
2016-12-01
Full Text Available The application of metaheuristic algorithms to optimization problems has been very important during the last decades. The main advantage of these techniques is their flexibility and robustness, which allows them to be applied to a wide range of problems. In this work we concentrate on metaheuristics based on Simulated Annealing, Tabu Search and Variable Neighborhood Search trajectory whose main characteristic is that they start from a point and through the exploration of the neighborhood vary the current solution, forming a trajectory. By means of the instances of the selected combinatorial problems, a computational experimentation is carried out that illustrates the behavior of the algorithmic methods to solve them. The main objective of this work is to perform the study and comparison of the results obtained for the selected trajectories metaheuristics in its application for the resolution of a set of academic problems of combinatorial optimization.
Hybrid Batch Bayesian Optimization
Azimi, Javad; Fern, Xiaoli
2012-01-01
Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function evaluations are possible. Under such a setting, BO could choose to either sequentially evaluate the function, one input at a time and wait for the output of the function before making the next selection, or evaluate the function at a batch of multiple inputs at once. These two different settings are commonly referred to as the sequential and batch settings of Bayesian Optimization. In general, the sequential setting leads to better optimization performance as each function evaluation is selected with more information, whereas the batch setting has an advantage in terms of the total experimental time (the number of iterations). In this work, our goal is to combine the strength of both settings. Specifically, we systematically analyze Bayesian optimization using Gaussian process as the posterior estimator and provide a hybrid algorithm t...
Tokamak Scenario Trajectory Optimization Using Fast Integrated Simulations
Urban, Jakub; Artaud, Jean-François; Vahala, Linda; Vahala, George
2015-11-01
We employ a fast integrated tokamak simulator, METIS, for optimizing tokamak discharge trajectories. METIS is based on scaling laws and simplified transport equations, validated on existing experiments and capable of simulating a full tokamak discharge in about 1 minute. Rapid free-boundary equilibrium post-processing using FREEBIE provides estimates of PF coil currents or forces. We employ several optimization strategies for optimizing key trajectories, such as Ip or heating power, of a model ITER hybrid discharge. Local and global algorithms with single or multiple objective functions show how to reach optimum performance, stationarity or minimum flux consumption. We constrain fundamental operation parameters, such as ramp-up rate, PF coils currents and forces or heating power. As an example, we demonstrate the benefit of current over-shoot for hybrid mode, consistent with previous results. This particular optimization took less than 2 hours on a single PC. Overall, we have established a powerful approach for rapid, non-linear tokamak scenario optimization, including operational constraints, pertinent to existing and future devices design and operation.
Optimal trajectory and insertion accuracy of sacral alar iliac screws
Directory of Open Access Journals (Sweden)
Katsutaka Yamada
2017-07-01
Conclusion: The optimal trajectories of SAISs in a Japanese patient population are more lateral in males and more caudal in females. This study examines the clinical safety and accuracy of SAIS insertion on these optimal trajectories.
Versatile and Extensible, Continuous-Thrust Trajectory Optimization Tool Project
National Aeronautics and Space Administration — We propose to develop an innovative, versatile and extensible, continuous-thrust trajectory optimization tool for planetary mission design and optimization of...
Improved Propulsion Modeling for Low-Thrust Trajectory Optimization
Knittel, Jeremy M.; Englander, Jacob A.; Ozimek, Martin T.; Atchison, Justin A.; Gould, Julian J.
2017-01-01
Low-thrust trajectory design is tightly coupled with spacecraft systems design. In particular, the propulsion and power characteristics of a low-thrust spacecraft are major drivers in the design of the optimal trajectory. Accurate modeling of the power and propulsion behavior is essential for meaningful low-thrust trajectory optimization. In this work, we discuss new techniques to improve the accuracy of propulsion modeling in low-thrust trajectory optimization while maintaining the smooth derivatives that are necessary for a gradient-based optimizer. The resulting model is significantly more realistic than the industry standard and performs well inside an optimizer. A variety of deep-space trajectory examples are presented.
Optimal growth trajectories with finite carrying capacity.
Caravelli, F; Sindoni, L; Caccioli, F; Ududec, C
2016-08-01
We consider the problem of finding optimal strategies that maximize the average growth rate of multiplicative stochastic processes. For a geometric Brownian motion, the problem is solved through the so-called Kelly criterion, according to which the optimal growth rate is achieved by investing a constant given fraction of resources at any step of the dynamics. We generalize these finding to the case of dynamical equations with finite carrying capacity, which can find applications in biology, mathematical ecology, and finance. We formulate the problem in terms of a stochastic process with multiplicative noise and a nonlinear drift term that is determined by the specific functional form of carrying capacity. We solve the stochastic equation for two classes of carrying capacity functions (power laws and logarithmic), and in both cases we compute the optimal trajectories of the control parameter. We further test the validity of our analytical results using numerical simulations.
Optimal growth trajectories with finite carrying capacity
Caravelli, F.; Sindoni, L.; Caccioli, F.; Ududec, C.
2016-08-01
We consider the problem of finding optimal strategies that maximize the average growth rate of multiplicative stochastic processes. For a geometric Brownian motion, the problem is solved through the so-called Kelly criterion, according to which the optimal growth rate is achieved by investing a constant given fraction of resources at any step of the dynamics. We generalize these finding to the case of dynamical equations with finite carrying capacity, which can find applications in biology, mathematical ecology, and finance. We formulate the problem in terms of a stochastic process with multiplicative noise and a nonlinear drift term that is determined by the specific functional form of carrying capacity. We solve the stochastic equation for two classes of carrying capacity functions (power laws and logarithmic), and in both cases we compute the optimal trajectories of the control parameter. We further test the validity of our analytical results using numerical simulations.
Hybrid Airy Plasmons with Dynamically Steerable Trajectories
Li, Rujiang; Imran, Muhammad; Lin, Xiao; Wang, Huaping; Xu, Zhiwei; Chen, Hongsheng
2016-01-01
With the intriguing properties of diffraction-free, self-accelerating, and self-healing, Airy plasmons are promising to be used in the trapping, transporting, and sorting of micro-objects, imaging, and chip scale signal processing. However, the high dissipative loss and the lack of dynamical steerability restrict the implementation of Airy plasmons in these applications. Here we reveal the hybrid Airy plasmons for the first time by taking a hybrid graphene-based plasmonic waveguide in the ter...
Solvable Optimal Velocity Models and Asymptotic Trajectory
Nakanishi, K; Igarashi, Y; Bando, M
1996-01-01
In the Optimal Velocity Model proposed as a new version of Car Following Model, it has been found that a congested flow is generated spontaneously from a homogeneous flow for a certain range of the traffic density. A well-established congested flow obtained in a numerical simulation shows a remarkable repetitive property such that the velocity of a vehicle evolves exactly in the same way as that of its preceding one except a time delay $T$. This leads to a global pattern formation in time development of vehicles' motion, and gives rise to a closed trajectory on $\\Delta x$-$v$ (headway-velocity) plane connecting congested and free flow points. To obtain the closed trajectory analytically, we propose a new approach to the pattern formation, which makes it possible to reduce the coupled car following equations to a single difference-differential equation (Rondo equation). To demonstrate our approach, we employ a class of linear models which are exactly solvable. We also introduce the concept of ``asymptotic traj...
Mars Hybrid Propulsion System Trajectory Analysis. Part II; Cargo Missions
Chai, Patrick R.; Merrill, Raymond G.; Qu, Min
2015-01-01
NASA's Human Spaceflight Architecture Team is developing a reusable hybrid transportation architecture in which both chemical and electric propulsion systems are used to send crew and cargo to Mars destinations such as Phobos, Deimos, the surface of Mars, and other orbits around Mars. By combining chemical and electrical propulsion into a single spaceship and applying each where it is more effective, the hybrid architecture enables a series of Mars trajectories that are more fuel-efficient than an all chemical architecture without significant increases in flight times. This paper shows the feasibility of the hybrid transportation architecture to pre-deploy cargo to Mars and Phobos in support of the Evolvable Mars Campaign crew missions. The analysis shows that the hybrid propulsion stage is able to deliver all of the current manifested payload to Phobos and Mars through the first three crew missions. The conjunction class trajectory also allows the hybrid propulsion stage to return to Earth in a timely fashion so it can be reused for additional cargo deployment. The 1,100 days total trip time allows the hybrid propulsion stage to deliver cargo to Mars every other Earth-Mars transit opportunity. For the first two Mars surface mission in the Evolvable Mars Campaign, the short trip time allows the hybrid propulsion stage to be reused for three round-trip journeys to Mars, which matches the hybrid propulsion stage's designed lifetime for three round-trip crew missions to the Martian sphere of influence.
Optimal guidance of extended trajectory shaping
Institute of Scientific and Technical Information of China (English)
Wang Hui; Lin Defu; Cheng Zhenxuan; Wang Jiang
2014-01-01
To control missile’s miss distance as well as terminal impact angle, by involving the time-to-go-nth power in the cost function, an extended optimal guidance law against a constant maneu-vering target or a stationary target is proposed using the linear quadratic optimal control theory. An extended trajectory shaping guidance (ETSG) law is then proposed under the assumption that the missile-target relative velocity is constant and the line of sight angle is small. For a lag-free ETSG system, closed-form solutions for the missile’s acceleration command are derived by the method of Schwartz inequality and linear simulations are performed to verify the closed-form results. Normalized adjoint systems for miss distance and terminal impact angle error are presented independently for stationary targets and constant maneuvering targets, respectively. Detailed discussions about the terminal misses and impact angle errors induced by terminal impact angle constraint, initial heading error, seeker zero position errors and target maneuvering, are performed.
Trajectory optimization and applications using high performance solar sails
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
The high performance solar sail can enable fast missions to the outer solar system and produce exotic non-Keplerian orbits.As there is no fuel consumption,mission trajectories for solar sail spacecraft are typically optimized with respect to flight time.Several investigations focused on interstellar probe missions have been made,including optimal methods and new objective functions. Two modes of interstellar mission trajectories,namely "direct flyby" and "angular momentum reversal trajectory",are compare...
Optimal Hankel Norm Model Reduction by Truncation of Trajectories
Roorda, B.; Weiland, S.
2000-01-01
We show how optimal Hankel-norm approximations of dynamical systems allow for a straightforward interpretation in terms of system trajectories. It is shown that for discrete time single-input systems optimal reductions are obtained by cutting 'balanced trajectories', i.e., by disconnecting the past
Trajectory analysis and optimization system (TAOS) user`s manual
Energy Technology Data Exchange (ETDEWEB)
Salguero, D.E.
1995-12-01
The Trajectory Analysis and Optimization System (TAOS) is software that simulates point--mass trajectories for multiple vehicles. It expands upon the capabilities of the Trajectory Simulation and Analysis program (TAP) developed previously at Sandia National Laboratories. TAOS is designed to be a comprehensive analysis tool capable of analyzing nearly any type of three degree-of-freedom, point-mass trajectory. Trajectories are broken into segments, and within each segment, guidance rules provided by the user control how the trajectory is computed. Parametric optimization provides a powerful method for satisfying mission-planning constraints. Althrough TAOS is not interactive, its input and output files have been designed for ease of use. When compared to TAP, the capability to analyze trajectories for more than one vehicle is the primary enhancement, although numerous other small improvements have been made. This report documents the methods used in TAOS as well as the input and output file formats.
Trajectory analysis and optimization system (TAOS) user`s manual
Energy Technology Data Exchange (ETDEWEB)
Salguero, D.E.
1995-12-01
The Trajectory Analysis and Optimization System (TAOS) is software that simulates point--mass trajectories for multiple vehicles. It expands upon the capabilities of the Trajectory Simulation and Analysis program (TAP) developed previously at Sandia National Laboratories. TAOS is designed to be a comprehensive analysis tool capable of analyzing nearly any type of three degree-of-freedom, point-mass trajectory. Trajectories are broken into segments, and within each segment, guidance rules provided by the user control how the trajectory is computed. Parametric optimization provides a powerful method for satisfying mission-planning constraints. Althrough TAOS is not interactive, its input and output files have been designed for ease of use. When compared to TAP, the capability to analyze trajectories for more than one vehicle is the primary enhancement, although numerous other small improvements have been made. This report documents the methods used in TAOS as well as the input and output file formats.
Hybrid undulator numerical optimization
Energy Technology Data Exchange (ETDEWEB)
Hairetdinov, A.H. [Kurchatov Institute, Moscow (Russian Federation); Zukov, A.A. [Solid State Physics Institute, Chernogolovka (Russian Federation)
1995-12-31
3D properties of the hybrid undulator scheme arc studied numerically using PANDIRA code. It is shown that there exist two well defined sets of undulator parameters which provide either maximum on-axis field amplitude or minimal higher harmonics amplitude of the basic undulator field. Thus the alternative between higher field amplitude or pure sinusoidal field exists. The behavior of the undulator field amplitude and harmonics structure for a large set of (undulator gap)/(undulator wavelength) values is demonstrated.
Optimal control of hybrid vehicles
Jager, Bram; Kessels, John
2013-01-01
Optimal Control of Hybrid Vehicles provides a description of power train control for hybrid vehicles. The background, environmental motivation and control challenges associated with hybrid vehicles are introduced. The text includes mathematical models for all relevant components in the hybrid power train. The power split problem in hybrid power trains is formally described and several numerical solutions detailed, including dynamic programming and a novel solution for state-constrained optimal control problems based on Pontryagin’s maximum principle. Real-time-implementable strategies that can approximate the optimal solution closely are dealt with in depth. Several approaches are discussed and compared, including a state-of-the-art strategy which is adaptive for vehicle conditions like velocity and mass. Two case studies are included in the book: · a control strategy for a micro-hybrid power train; and · experimental results obtained with a real-time strategy implemented in...
Automated Sensitivity Analysis of Interplanetary Trajectories for Optimal Mission Design
Knittel, Jeremy; Hughes, Kyle; Englander, Jacob; Sarli, Bruno
2017-01-01
This work describes a suite of Python tools known as the Python EMTG Automated Trade Study Application (PEATSA). PEATSA was written to automate the operation of trajectory optimization software, simplify the process of performing sensitivity analysis, and was ultimately found to out-perform a human trajectory designer in unexpected ways. These benefits will be discussed and demonstrated on sample mission designs.
Mars Hybrid Propulsion System Trajectory Analysis. Part I; Crew Missions
Chai, Patrick R.; Merrill, Raymond G.; Qu, Min
2015-01-01
NASAs Human spaceflight Architecture team is developing a reusable hybrid transportation architecture in which both chemical and electric propulsion systems are used to send crew and cargo to Mars destinations such as Phobos, Deimos, the surface of Mars, and other orbits around Mars. By combining chemical and electrical propulsion into a single space- ship and applying each where it is more effective, the hybrid architecture enables a series of Mars trajectories that are more fuel-efficient than an all chemical architecture without significant increases in flight times. This paper provides the analysis of the interplanetary segments of the three Evolvable Mars Campaign crew missions to Mars using the hybrid transportation architecture. The trajectory analysis provides departure and arrival dates and propellant needs for the three crew missions that are used by the campaign analysis team for campaign build-up and logistics aggregation analysis. Sensitivity analyses were performed to investigate the impact of mass growth, departure window, and propulsion system performance on the hybrid transportation architecture. The results and system analysis from this paper contribute to analyses of the other human spaceflight architecture team tasks and feed into the definition of the Evolvable Mars Campaign.
Reentry trajectory optimization for hypersonic vehicle satisfying complex constraints
Institute of Scientific and Technical Information of China (English)
Jiang Zhao; Rui Zhou
2013-01-01
The reentry trajectory optimization for hypersonic vehicle (HV) is a current problem of great interest. Some complex constraints, such as waypoints for reconnaissance and no-fly zones for threat avoidance, are inevitably involved in a global strike mission. Of the many direct methods, Gauss pseudospectral method (GPM) has been demonstrated as an effective tool to solve the tra-jectory optimization problem with typical constraints. However, a series of difficulties arises for complex constraints, such as the uncertainty of passage time for waypoints and the inaccuracy of approximate trajectory near no-fly zones. The research herein proposes a multi-phase technique based on the GPM to generate an optimal reentry trajectory for HV satisfying waypoint and no-fly zone constraints. Three kinds of specific breaks are introduced to divide the full trajectory into multiple phases. The continuity conditions are presented to ensure a smooth connection between each pair of phases. Numerical examples for reentry trajectory optimization in free-space flight and with complex constraints are used to demonstrate the proposed technique. Simulation results show the feasible application of multi-phase technique in reentry trajectory optimization with way-point and no-fly zone constraints.
Optimal trajectories for LEO-to-LEO aeroassisted orbital transfer
Miele, A.; Lee, W. Y.; Mease, K. D.
This paper considers both classical and minimax problems of optimal control arising in the study of noncoplanar, aeroassisted orbital transfer. The maneuver considered involves the transfer from one planetary orbit to another having different orbital inclination, but the same radius. An example is the LEO-to-LEO transfer of a spacecraft with a prescribed plane change, where LEO denotes low Earth orbit. The basic idea is to employ the hybrid combination of propulsive maneuvers in space and aerodynamic maneuvers in the sensible atmosphere. Hence, this type of flight is also called synergetic space flight. With reference to the atmospheric part of the maneuver, trajectory control is achieved by modulating the lift coefficient (hence, the angle of attack) and the angle of bank. The presence of upper and lower bounds on the lift coefficient is considered. Three different transfer maneuvers are studied. Type 1 involves four impulses and four space plane changes; Type 2 involves three impulses and three space plane changes; and Type 3 involves three impulses and no space plane change. In Type 1, the initial impulse directs the spacecraft away from Earth, and then is followed by an apogee impulse propelling the spacecraft toward Earth; in Types 2 and 3, the initial impulse directs the spacecraft toward Earth. A common element of these maneuvers is that they all include an atmospheric pass, with velocity depletion coupled with plane change. Within the framework of classical optimal control, the following problems are studied: (P1) minimize the energy required for orbital transfer; (P4) maximize the time of flight during the atmospheric portion of the trajectory; (P5) minimize the time integral of the square of the path inclination. Within the framework of minimax optimal control, the following problem is studied: (Q1) minimize the peak heating rate. Numerical solutions for Problems (P1), (P4), (P5), (Q1) are obtained by means of the sequential gradient-restoration algorithm
Optimal take-off trajectories in the presence of windshear
Miele, A.; Wang, T.; Melvin, W. W.
1986-01-01
The present consideration of takeoff trajectory optimization in eight different fundamental problems involving wind shears assumes that the power setting is held at the maximum value, and that the aircraft is controlled with respect to angle-of-attack. While the first three problems are least-squares ones of the Bolza type, the remaining five are minimax problems of the Chebyshev type which can be converted to Bolza type by means of suitable transformations. All problems are solved on the basis of the dual sequential gradient-restoration algorithm for optimal control problems. The trajectory solutions obtained are superior to constant angle-of-attack trajectories.
Nonlinear Optimal Trajectories Using Successive Linearization
1977-06-28
integral sign represents a penalty for the local vertical and passing through the vehicle deviations of the perturbed trajectory from the at time equals... integral sign represents the penalty for control variations about the nominal, and needs z s - sin y (14) to be weighted to ensure that the control does
Temporal Parameter Optimization in Four-Dimensional Flash Trajectory Imaging
Institute of Scientific and Technical Information of China (English)
WANG Xin-Wei; ZHOU Yan; FAN Song-Tao; LIU Yu-Liang
2011-01-01
In four-dimensional fiash trajectory imaging, temporal parameters include time delay, laser pulse width, gate time, pulse pair repetition frequency and the frame rate of CCD, which directly impact on the acquisition of target trajectories over time. We propose a method of optimizing the temporal parameters of flash trajectory imaging. All the temporal parameters can be estimated by the spatial parameters of the volumes of interest, target scale and velocity, and target sample number. The formulae for optimizing temporal parameters are derived, and the method is demonstrated in an experiment with a ball oscillating as a pendulum.%In four-dimensional flash trajectory imaging,temporal parameters include time delay,laser pulse width,gate time,pulse pair repetition frequency and the frame rate of CCD,which directly impact on the acquisition of target trajectories over time.We propose a method of optimizing the temporal parameters of flash trajectory imaging.All the temporal parameters can be estimated by the spatial parameters of the volumes of interest,target scale and velocity,and target sample number.The formulae for optimizing temporal parameters are derived,and the method is demonstrated in an experiment with a ball oscillating as a pendulum.Four-dimensional flash trajectory imaging (FTI)based on time-delay-modulated range-gated viewing can directly image the trajectories of moving objects with backgrounds filtered and deduce target 3D positions over time,[1] which has potentials in astronomy,remote sensing and biomedical applications.[2 4] Temporal parameters are crucial for FTI.An unreasonable setting of temporal parameters will lead to failure in obtaining target trajectories.However,in the previous work,[1] the optimization of temporal parameters has not been discussed in detail.Therefore,in this Letter we give a method of estimating the temporal parameters of FTI.
Earth-moon Trajectory Optimization Using Solar Electric Propulsion
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The optimization of the Earth-moon trajectory using solar electric propulsion is presented. A feasible method is proposed to optimize the transfer trajectory starting from a low Earth circular orbit (500 km altitude) to a low lunar circular orbit (200 km altitude). Due to the use of low-thrust solar electric propulsion, the entire transfer trajectory consists of hundreds or even thousands of orbital revolutions around the Earth and the moon. The Earth-orbit ascending (from low Earth orbit to high Earth orbit) and lunar descending (from high lunar orbit to low lunar orbit) trajectories in the presence of J2 perturbations and shadowing effect are computed by an analytic orbital averaging technique. A direct/indirect method is used to optimize the control steering for the trans-lunar trajectory segment, a segment fiom a high Earth orbit to a high lunar orbit, with a fixed thrust-coast-thrust engine sequence. For the trans-lunar trajectory segment, the equations of motion are expressed in the inertial coordinates about the Earth and the moon using a set of nonsingular equinoctial elements inclusive of the gravitational forces of the sun, the Earth, and the moon. By way of the analytic orbital averaging technique and the direct/indirect method, the Earth-moon transfer problem is converted to a parameter optimization problem, and the entire transfer trajectory is formulated and optimized in the form of a single nonlinear optimization problem with a small number of variables and constraints. Finally, an example of an Earth-moon transfer trajectory using solar electric propulsion is demonstrated.
Bulgakov, V. K.; Strigunov, V. V.
2009-05-01
The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.
Optimizing Hybrid Spreading in Metapopulations
Zhang, Changwang; Cox, Ingemar J; Chain, Benjamin M
2014-01-01
Epidemic spreading phenomena are ubiquitous in nature and society. Examples include the spreading of diseases, information, and computer viruses. Epidemics can spread by \\textit{local spreading}, where infected nodes can only infect a limited set of direct target nodes and \\textit{global spreading}, where an infected node can infect every other node. In reality, many epidemics spread using a hybrid mixture of both types of spreading. In this study we develop a theoretical framework for studying hybrid epidemics, and examine the optimum balance between spreading mechanisms in terms of achieving the maximum outbreak size. In a metapopulation, made up of many weakly connected subpopulations, we show that one can calculate an optimal tradeoff between local and global spreading which will maximise the extent of the epidemic. As an example we analyse the 2008 outbreak of the Internet worm Conficker, which uses hybrid spreading to propagate through the internet. Our results suggests that the worm would have been eve...
Nguyen, Nhan T.; Hornby, Gregory; Ishihara, Abe
2013-01-01
This paper describes two methods of trajectory optimization to obtain an optimal trajectory of minimum-fuel- to-climb for an aircraft. The first method is based on the adjoint method, and the second method is based on a direct trajectory optimization method using a Chebyshev polynomial approximation and cubic spine approximation. The approximate optimal trajectory will be compared with the adjoint-based optimal trajectory which is considered as the true optimal solution of the trajectory optimization problem. The adjoint-based optimization problem leads to a singular optimal control solution which results in a bang-singular-bang optimal control.
Receding Horizon Trajectory Optimization with Terminal Impact Specifications
Directory of Open Access Journals (Sweden)
Limin Zhang
2014-01-01
Full Text Available The trajectory optimization problem subject to terminal impact time and angle specifications can be reformulated as a nonlinear programming problem using the Gauss pseudospectral method. The cost function of the trajectory optimization problem is modified to reduce the terminal control energy. A receding horizon optimization strategy is implemented to reject the errors caused by the motion of a surface target. Several simulations were performed to validate the proposed method via the C programming language. The simulation results demonstrate the effectiveness of the proposed algorithm and that the real-time requirement can be easily achieved if the C programming language is used to realize it.
Global, Multi-Objective Trajectory Optimization With Parametric Spreading
Vavrina, Matthew A.; Englander, Jacob A.; Phillips, Sean M.; Hughes, Kyle M.
2017-01-01
Mission design problems are often characterized by multiple, competing trajectory optimization objectives. Recent multi-objective trajectory optimization formulations enable generation of globally-optimal, Pareto solutions via a multi-objective genetic algorithm. A byproduct of these formulations is that clustering in design space can occur in evolving the population towards the Pareto front. This clustering can be a drawback, however, if parametric evaluations of design variables are desired. This effort addresses clustering by incorporating operators that encourage a uniform spread over specified design variables while maintaining Pareto front representation. The algorithm is demonstrated on a Neptune orbiter mission, and enhanced multidimensional visualization strategies are presented.
Some impulsive rendezvous trajectories and their possible optimality.
Peltier, J. P.
1972-01-01
Two- and three-impulse trajectories are investigated for fixed-time, fixed-angle rendezvous between vacant circular coplanar orbits, for trip angles less than, or equal to 2 pi in magnitude. For two-impulse trajectories, general features of the characteristic velocity function are outlined. Parameters of the intermediate orbit are reviewed. Attention is given to limiting cases. Computation of the adjoint system helps to define the domain of possible optimality foajectories: it is a closed domain in the trip time, trip angle plane. Waiting periods on terminal orbits are considered. The domain of possible optimality is defined using Lawden's primer vrtory. This domain extends to infinity if the radius ratio of terminal orbits is less than 15.6. Three-impulse trajectories are tried in cases where two-impulse trajectories, with or without cost, have been found nonoptimal. Improvements on the characteristic velocity are thus obtained.
Directory of Open Access Journals (Sweden)
Trevor Davies
2008-08-01
Full Text Available This paper presents the development and implementation a hybrid control architecture to direct a collective of three X80 mobile robots to multiple user-defined waypoints. The Genetic Algorithm Path Planner created an optimized, reduction in the time to complete the task, path plan for each robot in the collective such that each waypoint was visited once without colliding with a priori obstacles. The deliberative Genetic Algorithm Path Planner was then coupled with a reactive Potential Field Trajectory Planner and kinematic based controller to create a hybrid control architecture allowing the mobile robot to navigate between multiple user-defined waypoints, while avoiding a priori obstacles and obstacles detected using the robots' range sensors. The success of this hybrid control architecture was proven through simulation and experimentation using three of Dr. Robot's ™ wireless X80 mobile robots.
A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases
Directory of Open Access Journals (Sweden)
Shengnan Ke
2014-07-01
Full Text Available In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.
A hybrid spatio-temporal data indexing method for trajectory databases.
Ke, Shengnan; Gong, Jun; Li, Songnian; Zhu, Qing; Liu, Xintao; Zhang, Yeting
2014-07-21
In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.
Solar sail time-optimal interplanetary transfer trajectory design
Institute of Scientific and Technical Information of China (English)
Sheng-Ping Gong; Yun-Feng Gao; Jun-Feng Li
2011-01-01
The fuel consumption associated with some interplanetary transfer trajectories using chemical propulsion is not affordable.A solar sail is a method of propulsion that does not consume fuel.Transfer time is one of the most pressing problems of solar sail transfer trajectory design.This paper investigates the time-optimal interplanetary transfer trajectories to a circular orbit of given inclination and radius.The optimal control law is derived from the principle of maximization.An indirect method is used to solve the optimal control problem by selecting values for the initial adjoint variables,which are normalized within a unit sphere.The conditions for the existence of the time-optimal transfer are dependent on the lightness number of the sail and the inclination and radius of the target orbit.A numerical method is used to obtain the boundary values for the time-optimal transfer trajectories.For the cases where no time-optimal transfer trajectories exist,first-order necessary conditions of the optimal control are proposed to obtain feasible solutions.The results show that the transfer time decreases as the minimum distance from the Sun decreases during the transfer duration.For a solar sail with a small lightness number,the transfer time may be evaluated analytically for a three-phase transfer trajectory.The analytical results are compared with previous results and the associated numerical results.The transfer time of the numerical result here is smaller than the transfer time from previous results and is larger than the analytical result.
IMPORTANCE OF KINETIC MEASURES IN TRAJECTORY PREDICTION WITH OPTIMAL CONTROL
Directory of Open Access Journals (Sweden)
Ömer GÜNDOĞDU
2001-02-01
Full Text Available A two-dimensional sagittally symmetric human-body model was established to simulate an optimal trajectory for manual material handling tasks. Nonlinear control techniques and genetic algorithms were utilized in the optimizations to explore optimal lifting patterns. The simulation results were then compared with the experimental data. Since the kinetic measures such as joint reactions and moments are vital parameters in injury determination, the importance of comparing kinetic measures rather than kinematical ones was emphasized.
Design and Analysis of Optimal Ascent Trajectories for Stratospheric Airships
Mueller, Joseph Bernard
Stratospheric airships are lighter-than-air vehicles that have the potential to provide a long-duration airborne presence at altitudes of 18-22 km. Designed to operate on solar power in the calm portion of the lower stratosphere and above all regulated air traffic and cloud cover, these vehicles represent an emerging platform that resides between conventional aircraft and satellites. A particular challenge for airship operation is the planning of ascent trajectories, as the slow moving vehicle must traverse the high wind region of the jet stream. Due to large changes in wind speed and direction across altitude and the susceptibility of airship motion to wind, the trajectory must be carefully planned, preferably optimized, in order to ensure that the desired station be reached within acceptable performance bounds of flight time and energy consumption. This thesis develops optimal ascent trajectories for stratospheric airships, examines the structure and sensitivity of these solutions, and presents a strategy for onboard guidance. Optimal ascent trajectories are developed that utilize wind energy to achieve minimum-time and minimum-energy flights. The airship is represented by a three-dimensional point mass model, and the equations of motion include aerodynamic lift and drag, vectored thrust, added mass effects, and accelerations due to mass flow rate, wind rates, and Earth rotation. A representative wind profile is developed based on historical meteorological data and measurements. Trajectory optimization is performed by first defining an optimal control problem with both terminal and path constraints, then using direct transcription to develop an approximate nonlinear parameter optimization problem of finite dimension. Optimal ascent trajectories are determined using SNOPT for a variety of upwind, downwind, and crosswind launch locations. Results of extensive optimization solutions illustrate definitive patterns in the ascent path for minimum time flights across
Segmented Hybrid Gasostatic Bearing Optimization
Directory of Open Access Journals (Sweden)
Prodan Nikolay Vasilevich
2014-07-01
Full Text Available The purpose of research-development of methods of numerical optimization rotatable support pads gasostatic hybrid bearing. In the world‘s aerospace engineering the gas-dynamic bearings are currently most common. They are characterized by the supporting layer of different designs, which ensures the workability of the rotors during starts and stops. The main problem of this bearing type, apart from the construction complexity is the wear of this supporting layer. Gas-static bearing has no such defect, since there is no physical contact between solid surfaces. This study presents the results of the hybrid bearing’s calculation, combining both technologies. The slotted nozzle of non-conventional shape that mirrors the solution of Reynolds equation’s isoline is studied. The dependences of the main parameters on the speed of the shaft’s rotation are discussed. The aerodynamic resistance of pads for different regimes of operation is investigated.
Cartesian Trajectory Tracking for Manipulators Using Optimal Control Theory
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Olav Egeland
1987-07-01
Full Text Available A Cartesian trajectory tracking system for manipulators is developed using optimal control theory. By including the Cartesian position in the state vector, transformation of the trajectory from Cartesian space to manipulator joint space is avoided, and the Jacobian matrix need not be inverted. The tracking system may also be applied to kinematically redundant manipulators. For this type of manipulator, singularities are avoided by choosing a suitable performance index in the optimal control problem. Simulation using a simple kinematically redundant manipulator shows that a small tracking error can be achieved with low motor torques.
Trajectory Optimization of Electric Aircraft Subject to Subsystem Thermal Constraints
Falck, Robert D.; Chin, Jeffrey C.; Schnulo, Sydney L.; Burt, Jonathan M.; Gray, Justin S.
2017-01-01
Electric aircraft pose a unique design challenge in that they lack a simple way to reject waste heat from the power train. While conventional aircraft reject most of their excess heat in the exhaust stream, for electric aircraft this is not an option. To examine the implications of this challenge on electric aircraft design and performance, we developed a model of the electric subsystems for the NASA X-57 electric testbed aircraft. We then coupled this model with a model of simple 2D aircraft dynamics and used a Legendre-Gauss-Lobatto collocation optimal control approach to find optimal trajectories for the aircraft with and without thermal constraints. The results show that the X-57 heat rejection systems are well designed for maximum-range and maximum-efficiency flight, without the need to deviate from an optimal trajectory. Stressing the thermal constraints by reducing the cooling capacity or requiring faster flight has a minimal impact on performance, as the trajectory optimization technique is able to find flight paths which honor the thermal constraints with relatively minor deviations from the nominal optimal trajectory.
Nonholonomic Mobile Robot Trajectory Tracking using Hybrid Controller
Directory of Open Access Journals (Sweden)
Muhammad Safwan
2016-04-01
Full Text Available A control scheme is being presented for the trajectory tracking of a nonholonomic kinematic model of mobile robots. As a kinematic model of mobile robots is nonlinear in nature, therefore, it is controlling is always being a difficult task. Thus, a control hybrid scheme comprises of fuzzy logic and PID (Proportional Integral Derivative is being proposed, in which adaptive gains of PID controller is being tuned by a fuzzy logic controller. Moreover, the effectiveness of this innovative technique is also proved using the simulations by adding model uncertainties and external disturbances in the system. Besides, the fuzzy logic control system is also being compared by the proposed control system. Resultsattained shows that the fuzzy based PID controller drivesimproved results than fuzzy logic controller.
Optimal penetration landing trajectories in the presence of wind shear
Miele, A.; Wang, T.; Melvin, W. W.; Wang, H.
1988-01-01
Aircraft penetration landing in the presence of strong-to-severe wind shear is investigated analytically. The optimal-control problem for vertical-plane trajectories is considered, using angle of attack as one control parameter with either (1) a power setting (PS) which remains constant at its preshear value, (2) a PS which increases to its maximum value, or (3) a PS which is controlled (as the second parameter). The problem formulation is explained in detail, and numerical results obtained with the primal sequential gradient-restoration algorithm of Miele and Wang (1986) are presented in extensive tables and graphs. It is found that the touchdown requirements can only be satisfied by optimal trajectories using scheme (1) (but only at low altitudes) or scheme (3); the characteristics of the latter trajectories are explored.
Global Launcher Trajectory Optimization for Lunar Base Settlement
Pagano, A.; Mooij, E.
2010-01-01
The problem of a mission to the Moon to set a permanent outpost can be tackled by dividing the journey into three phases: the Earth ascent, the Earth-Moon transfer and the lunar landing. In this paper we present an optimization analysis of Earth ascent trajectories of existing launch vehicles inject
Vehicle/trajectory optimization for aerocapture at Mars
Wetzel, Todd A.; Moerder, Daniel D.
1994-01-01
This paper considers joint optimization of trajectory and configuration in determining a minimum-mass vehicle for aeroassisted capture of a payload to a specific orbit at Mars. Aerocapture by a vehicle with a blunt aeroshell is assumed, supplemented by either chemical or nuclear thermal rocket impulses within the planet's sphere of influence. Optimal combinations of aeroshell size and exoatmospheric impulses are calculated for a range of entry velocities and constraints on integrated convective heating.
Heliocentric interplanetary low thrust trajectory optimization program, supplement 1
Mann, F. I.; Horsewood, J. L.
1974-01-01
The modifications and improvements made to the HILTOP electric propulsion trajectory optimization computer program up through the end of 1974 is described. New program features include the simulation of power degradation, housekeeping power, launch asymptote declination optimization, and powered and unpowered ballistic multiple swingby missions with an optional deep space burn. The report contains the new analysis describing these features, a complete description of program input quantities, and sample cases of computer output illustrating the new program capabilities.
Multi-objective trajectory optimization for the space exploration vehicle
Qin, Xiaoli; Xiao, Zhen
2016-07-01
The research determines temperature-constrained optimal trajectory for the space exploration vehicle by developing an optimal control formulation and solving it using a variable order quadrature collocation method with a Non-linear Programming(NLP) solver. The vehicle is assumed to be the space reconnaissance aircraft that has specified takeoff/landing locations, specified no-fly zones, and specified targets for sensor data collections. A three degree of freedom aircraft model is adapted from previous work and includes flight dynamics, and thermal constraints.Vehicle control is accomplished by controlling angle of attack, roll angle, and propellant mass flow rate. This model is incorporated into an optimal control formulation that includes constraints on both the vehicle and mission parameters, such as avoidance of no-fly zones and exploration of space targets. In addition, the vehicle models include the environmental models(gravity and atmosphere). How these models are appropriately employed is key to gaining confidence in the results and conclusions of the research. Optimal trajectories are developed using several performance costs in the optimal control formation,minimum time,minimum time with control penalties,and maximum distance.The resulting analysis demonstrates that optimal trajectories that meet specified mission parameters and constraints can be quickly determined and used for large-scale space exloration.
A Smoothed Eclipse Model for Solar Electric Propulsion Trajectory Optimization
Aziz, Jonathan; Scheeres, Daniel; Parker, Jeffrey; Englander, Jacob
2017-01-01
Solar electric propulsion (SEP) is the dominant design option for employing low-thrust propulsion on a space mission. Spacecraft solar arrays power the SEP system but are subject to blackout periods during solar eclipse conditions. Discontinuity in power available to the spacecraft must be accounted for in trajectory optimization, but gradient-based methods require a differentiable power model. This work presents a power model that smooths the eclipse transition from total eclipse to total sunlight with a logistic function. Example trajectories are computed with differential dynamic programming, a second-order gradient-based method.
Optimal trajectory generation for mechanical arms. M.S. Thesis
Iemenschot, J. A.
1972-01-01
A general method of generating optimal trajectories between an initial and a final position of an n degree of freedom manipulator arm with nonlinear equations of motion is proposed. The method is based on the assumption that the time history of each of the coordinates can be expanded in a series of simple time functions. By searching over the coefficients of the terms in the expansion, trajectories which minimize the value of a given cost function can be obtained. The method has been applied to a planar three degree of freedom arm.
On cooperative patrolling: optimal trajectories, complexity analysis, and approximation algorithms
Pasqualetti, Fabio; Bullo, Francesco
2011-01-01
The subject of this work is the patrolling of an environment with the aid of a team of autonomous agents. We consider both the design of open-loop trajectories with optimal properties, and of distributed control laws converging to optimal trajectories. As performance criteria, the refresh time and the latency are considered, i.e., respectively, time gap between any two visits of the same region, and the time necessary to inform every agent about an event occurred in the environment. We associate a graph with the environment, and we study separately the case of a chain, tree, and cyclic graph. For the case of chain graph, we first describe a minimum refresh time and latency team trajectory, and we propose a polynomial time algorithm for its computation. Then, we describe a distributed procedure that steers the robots toward an optimal trajectory. For the case of tree graph, a polynomial time algorithm is developed for the minimum refresh time problem, under the technical assumption of a constant number of robo...
Trajectory generation for manipulators using linear quadratic optimal tracking
Directory of Open Access Journals (Sweden)
Olav Egeland
1989-04-01
Full Text Available The reference trajectory is normally known in advance in manipulator control which makes it possible to apply linear quadratic optimal tracking. This gives a control system which rounds corners and generates optimal feedforward. The method may be used for references consisting of straight-line segments as an alternative to the two-step method of using splines to smooth the reference and then applying feedforward. In addition, the method can be used for more complex trajectories. The actual dynamics of the manipulator are taken into account, and this results in smooth and accurate tracking. The method has been applied in combination with the computed torque technique and excellent performance was demonstrated in a simulation study. The method has also been applied experimentally to an industrial spray-painting robot where a saw-tooth reference was tracked. The corner was rounded extremely well, and the steady-state tracking error was eliminated by the optimal feedforward.
HOPSPACK: Hybrid Optimization Parallel Search Package.
Energy Technology Data Exchange (ETDEWEB)
Gray, Genetha Anne.; Kolda, Tamara G.; Griffin, Joshua; Taddy, Matt; Martinez-Canales, Monica L.
2008-12-01
In this paper, we describe the technical details of HOPSPACK (Hybrid Optimization Parallel SearchPackage), a new software platform which facilitates combining multiple optimization routines into asingle, tightly-coupled, hybrid algorithm that supports parallel function evaluations. The frameworkis designed such that existing optimization source code can be easily incorporated with minimalcode modification. By maintaining the integrity of each individual solver, the strengths and codesophistication of the original optimization package are retained and exploited.4
Optimal lunar soft landing trajectories using taboo evolutionary programming
Mutyalarao, M.; Raj, M. Xavier James
A safe lunar landing is a key factor to undertake an effective lunar exploration. Lunar lander consists of four phases such as launch phase, the earth-moon transfer phase, circumlunar phase and landing phase. The landing phase can be either hard landing or soft landing. Hard landing means the vehicle lands under the influence of gravity without any deceleration measures. However, soft landing reduces the vertical velocity of the vehicle before landing. Therefore, for the safety of the astronauts as well as the vehicle lunar soft landing with an acceptable velocity is very much essential. So it is important to design the optimal lunar soft landing trajectory with minimum fuel consumption. Optimization of Lunar Soft landing is a complex optimal control problem. In this paper, an analysis related to lunar soft landing from a parking orbit around Moon has been carried out. A two-dimensional trajectory optimization problem is attempted. The problem is complex due to the presence of system constraints. To solve the time-history of control parameters, the problem is converted into two point boundary value problem by using the maximum principle of Pontrygen. Taboo Evolutionary Programming (TEP) technique is a stochastic method developed in recent years and successfully implemented in several fields of research. It combines the features of taboo search and single-point mutation evolutionary programming. Identifying the best unknown parameters of the problem under consideration is the central idea for many space trajectory optimization problems. The TEP technique is used in the present methodology for the best estimation of initial unknown parameters by minimizing objective function interms of fuel requirements. The optimal estimation subsequently results into an optimal trajectory design of a module for soft landing on the Moon from a lunar parking orbit. Numerical simulations demonstrate that the proposed approach is highly efficient and it reduces the minimum fuel
Augmenting Parametric Optimal Ascent Trajectory Modeling with Graph Theory
Dees, Patrick D.; Zwack, Matthew R.; Edwards, Stephen; Steffens, Michael
2016-01-01
It has been well documented that decisions made in the early stages of Conceptual and Pre-Conceptual design commit up to 80% of total Life-Cycle Cost (LCC) while engineers know the least about the product they are designing [1]. Once within Preliminary and Detailed design however, making changes to the design becomes far more difficult to enact in both cost and schedule. Primarily this has been due to a lack of detailed data usually uncovered later during the Preliminary and Detailed design phases. In our current budget-constrained environment, making decisions within Conceptual and Pre-Conceptual design which minimize LCC while meeting requirements is paramount to a program's success. Within the arena of launch vehicle design, optimizing the ascent trajectory is critical for minimizing the costs present within such concerns as propellant, aerodynamic, aeroheating, and acceleration loads while meeting requirements such as payload delivered to a desired orbit. In order to optimize the vehicle design its constraints and requirements must be known, however as the design cycle proceeds it is all but inevitable that the conditions will change. Upon that change, the previously optimized trajectory may no longer be optimal, or meet design requirements. The current paradigm for adjusting to these updates is generating point solutions for every change in the design's requirements [2]. This can be a tedious, time-consuming task as changes in virtually any piece of a launch vehicle's design can have a disproportionately large effect on the ascent trajectory, as the solution space of the trajectory optimization problem is both non-linear and multimodal [3]. In addition, an industry standard tool, Program to Optimize Simulated Trajectories (POST), requires an expert analyst to produce simulated trajectories that are feasible and optimal [4]. In a previous publication the authors presented a method for combatting these challenges [5]. In order to bring more detailed information
Optimal command generation for tracking a class of discontinuous trajectories
Weissenberger, S.
1976-01-01
Commands are found to drive a linear system to optimally track a class of prescribed trajectories, each of which contains a point of discontinuity. The paper focuses on the guidance problem of command generation, to be implemented in a feedforward fashion; the necessary additional control or feedback regulation structure is not studied in detail, but assumed to be provided in applications as a conventional error-feedback regulator. Solutions are found for the infinite-time problem which are optimal with respect to a quadratic performance criterion; suboptimal controls which satisfy a continuity condition are also found. The controls have applications to certain problems in aircraft guidance where command trajectories are piecewise continuous. Several examples are worked out in detail, with comparisons with conventional, nonfeedforward solutions to the problem, and a brief discussion of a simpler, suboptimal solution.
Optimization of Low-Thrust Spiral Trajectories by Collocation
Falck, Robert D.; Dankanich, John W.
2012-01-01
As NASA examines potential missions in the post space shuttle era, there has been a renewed interest in low-thrust electric propulsion for both crewed and uncrewed missions. While much progress has been made in the field of software for the optimization of low-thrust trajectories, many of the tools utilize higher-fidelity methods which, while excellent, result in extremely high run-times and poor convergence when dealing with planetocentric spiraling trajectories deep within a gravity well. Conversely, faster tools like SEPSPOT provide a reasonable solution but typically fail to account for other forces such as third-body gravitation, aerodynamic drag, solar radiation pressure. SEPSPOT is further constrained by its solution method, which may require a very good guess to yield a converged optimal solution. Here the authors have developed an approach using collocation intended to provide solution times comparable to those given by SEPSPOT while allowing for greater robustness and extensible force models.
Zhengnan Li; Tao Yang; Zhiwei Feng
2016-01-01
To solve the multiobjective optimization problem on hypersonic glider vehicle trajectory design subjected to complex constraints, this paper proposes a multiobjective trajectory optimization method that combines the boundary intersection method and pseudospectral method. The multiobjective trajectory optimization problem (MTOP) is established based on the analysis of the feature of hypersonic glider vehicle trajectory. The MTOP is translated into a set of general optimization subproblems by u...
Trajectory optimization for a cleaning robot for a hotline insulator
Institute of Scientific and Technical Information of China (English)
SUN Bin; XU Wei; YANG Ru-qing
2005-01-01
We investigated the vibration of a cleaning robot for hotline insulators, providing a flexible elevating link with a rigid moving link at the end. A Lagrange dynamic model is established based on the assumed mode method. An approach is proposed to reduce residual vibration of the flexible elevating link by optimizing acceleration of rigid link using the Pontryagin maximum principle (PMP). A numerical solution to the proposed optimization problem including a two-point boundary-value problem (2PBVP) is developed. Residual vibration of the flexible elevating link of the optimal acceleration profile is compared with that of the optimal trapezoid velocity profile. The result shows that the proposed trajectory optimization method can reduce the residual vibration more effectively.
Optimal trajectory planning for natural biped walking locomotion
Institute of Scientific and Technical Information of China (English)
刘荣强; 焦映厚; 陈照波
2003-01-01
An optimal trajectory planning method has been proposed for the walking locomotion of a biped me-c hanical system with thighs, shanks and small feet, which is modelled as a 3-DOF link system consisting of aninverted pendulum and a 2-DOF swing leg. The locomotion of swing and supporting legs is solved by the optimaltrajectory planning based on function approximation. The optimal trajectory planning based on function approxi-mation. The optimal walking locomotion solution with minimum square of input torque exhibits a natural walkinggait with one step period of 0.64 s similar to the human walking gait by using the link parameters of an adult'sleg. It is concluded from the computation results that the method proposed in this paper has been proved to bean effective tool for solving the optimal walking locomotion and joint control torque problems for a 3-DOF bipedmechanism; when the ankle joint of the supporting leg is a passive joint, a nearly, optimal walking solution canbe obtained at t1 = 0. 49 s and t2 = 10 s, and however, when the knee is a passive joint, it is impossible to ob-tain a solution which satisfies the constraint condition; for the link parameters used in this paper, the length ofan optimal stride is 0.3 m.
Humanoid robot simulation with a joint trajectory optimized controller
2008-01-01
This paper describes a joint trajectory optimized controller for a humanoid robot simulator following the real robot characteristics. As simulation is a powerful tool for speeding up the control software development, the proposed accurate simulator allows to fulfil this goal. The simulator, based on the Open Dynamics Engine and GLScene graphics library, provides instant visual feedback. The proposed simulator, with realistic dynamics, allows to design and test behaviours and control strat...
4D Trajectory Estimation for Air Traffic Control Automation System Based on Hybrid System Theory
Directory of Open Access Journals (Sweden)
Xin-Min Tang
2012-03-01
Full Text Available To resolve the problem of future airspace management under great traffic flow and high density condition, 4D trajectory estimation has become one of the core technologies of the next new generation air traffic control automation system. According to the flight profile and the dynamics models of different aircraft types under different flight conditions, a hybrid system model that switches the aircraft from one flight stage to another with aircraft state changing continuously in one state is constructed. Additionally, air temperature and wind speed are used to modify aircraft true airspeed as well as ground speed, and the hybrid system evolution simulation is used to estimate aircraft 4D trajectory. The case study proves that 4D trajectory estimated through hybrid system model can image the flight dynamic states of aircraft and satisfy the needs of the planned flight altitude profile.KEY WORDSair traffic management, 4D trajectory estimation, hybrid system model, aircraft dynamic model
Clustering molecular dynamics trajectories for optimizing docking experiments.
De Paris, Renata; Quevedo, Christian V; Ruiz, Duncan D; Norberto de Souza, Osmar; Barros, Rodrigo C
2015-01-01
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.
New Search Space Reduction Algorithm for Vertical Reference Trajectory Optimization
Directory of Open Access Journals (Sweden)
Alejandro MURRIETA-MENDOZA
2016-06-01
Full Text Available Burning the fuel required to sustain a given flight releases pollution such as carbon dioxide and nitrogen oxides, and the amount of fuel consumed is also a significant expense for airlines. It is desirable to reduce fuel consumption to reduce both pollution and flight costs. To increase fuel savings in a given flight, one option is to compute the most economical vertical reference trajectory (or flight plan. A deterministic algorithm was developed using a numerical aircraft performance model to determine the most economical vertical flight profile considering take-off weight, flight distance, step climb and weather conditions. This algorithm is based on linear interpolations of the performance model using the Lagrange interpolation method. The algorithm downloads the latest available forecast from Environment Canada according to the departure date and flight coordinates, and calculates the optimal trajectory taking into account the effects of wind and temperature. Techniques to avoid unnecessary calculations are implemented to reduce the computation time. The costs of the reference trajectories proposed by the algorithm are compared with the costs of the reference trajectories proposed by a commercial flight management system using the fuel consumption estimated by the FlightSim® simulator made by Presagis®.
Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments
Directory of Open Access Journals (Sweden)
Renata De Paris
2015-01-01
Full Text Available Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.
Rapid near-optimal aerospace plane trajectory generation and guidance
Corban, J. E.; Calise, A. J.; Flandro, G. A.
1991-01-01
Problems associated with onboard trajectory optimization, propulsion system cycle selection, and the synthesis of guidance laws are addressed for ascent to low earth orbit of an airbreathing, single-stage-to-orbit vehicle. A multicycle propulsion system is assumed that incorporates turbojet, ramjet, scramjet, and rocket engines. An energy state approximation is applied to a singularly perturbed, four-state dynamic model for flight of a point mass over a spherical nonrotating earth. An algorithm is then derived for generating both the fuel-optimal climb profile and the guidance commands required to follow that profile. In particular, analytic switching conditions are derived that, under appropriate assumptions, efficiently govern optimal transition from one propulsion cycle to another. The algorithm proves to be computationally efficient and suitable for real-time implementation. The paper concludes with the presentation of representative numerical results that illustrate the nature of the fuel-optimal climb paths and the tracking performance of the guidance algorithm.
Multi-Objective Hybrid Optimal Control for Interplanetary Mission Planning
Englander, Jacob
2015-01-01
Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. Because low-thrust trajectory design is tightly coupled with systems design, power and propulsion characteristics must be chosen as well. In addition, a time-history of control variables must be chosen which defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The methods is demonstrated on hypothetical mission to the main asteroid belt and to Deimos.
The Development of an Optimal Control Strategy for a Series Hydraulic Hybrid Vehicle
Directory of Open Access Journals (Sweden)
Chih-Wei Hung
2016-03-01
Full Text Available In this work, a Truck Class II series hydraulic hybrid model is established. Dynamic Programming (DP methodology is applied to derive the optimal power-splitting factor for the hybrid system for preselected driving schedules. Implementable rules are derived by extracting the optimal trajectory features from a DP scheme. The system behaviors illustrate that the improved control strategy gives a highly effective operation region for the engine and high power density characteristics for the hydraulic components.
Ascent trajectory optimization for stratospheric airship with thermal effects
Guo, Xiao; Zhu, Ming
2013-09-01
Ascent trajectory optimization with thermal effects is addressed for a stratospheric airship. Basic thermal characteristics of the stratospheric airship are introduced. Besides, the airship’s equations of motion are constructed by including the factors about aerodynamic force, added mass and wind profiles which are developed based on horizontal-wind model. For both minimum-time and minimum-energy flights during ascent, the trajectory optimization problem is described with the path and terminal constraints in different scenarios and then, is converted into a parameter optimization problem by a direct collocation method. Sparse Nonlinear OPTimizer(SNOPT) is employed as a nonlinear programming solver and two scenarios are adopted. The solutions obtained illustrate that the trajectories are greatly affected by the thermal behaviors which prolong the daytime minimum-time flights of about 20.8% compared with that of nighttime in scenario 1 and of about 10.5% in scenario 2. And there is the same trend for minimum-energy flights. For the energy consumption of minimum-time flights, 6% decrease is abstained in scenario 1 and 5% decrease in scenario 2. However, a few energy consumption reduction is achieved for minimum-energy flights. Solar radiation is the principal component and the natural wind also affects the thermal behaviors of stratospheric airship during ascent. The relationship between take-off time and performance of airship during ascent is discussed. it is found that the take-off time at dusk is best choice for stratospheric airship. And in addition, for saving energy, airship prefers to fly downwind.
System identification and trajectory optimization for guided store separation
Carter, Ryan E.
Combat aircraft utilize expendable stores such as missiles, bombs, flares, and external tanks to execute their missions. Safe and acceptable separation of these stores from the parent aircraft is essential for meeting the mission objectives. In many cases, the employed missile or bomb includes an onboard guidance and control system to enable precise engagement of the selected target. Due to potential interference, the guidance and control system is usually not activated until the store is sufficiently far away from the aircraft. This delay may result in large perturbations from the desired flight attitude caused by separation transients, significantly reducing the effectiveness of the store and jeopardizing mission objectives. The purpose of this research is to investigate the use of a transitional control system to guide the store during separation. The transitional control system, or "store separation autopilot", explicitly accounts for the nonuniform flow field through characterization of the spatially variant aerodynamics of the store during separation. This approach can be used to mitigate aircraft-store interference and leverage aerodynamic interaction to improve separation characteristics. This investigation proceeds in three phases. First, system identification is used to determine a parametric model for the spatially variant aerodynamics. Second, the store separation problem is recast into a trajectory optimization problem, and optimal control theory is used to establish a framework for designing a suitable reference trajectory with explicit dependence on the spatially variant aerodynamics. Third, neighboring optimal control is used to construct a linear-optimal feedback controller for correcting deviations from the nominal reference trajectory due varying initial conditions, modeling errors, and flowfield perturbations. An extended case study based on actual wind tunnel and flight test measurements is used throughout to illustrate the effectiveness of the
Optimization of interplanetary trajectories to Mars via electrical propulsion
Williams, Powtawche Neengay
Although chemical rocket propulsion is widely used in space transportation, large amounts of propellant mass limit designs for spacecraft missions to Mars. Electrical propulsion, which requires a smaller propellant load, is an alternative propulsion system that can be used for interplanetary flight. After the recent successes of the NASA Deep Space 1 spacecraft and the ESA SMART 1 spacecraft, which incorporate an electrical propulsion system, there is a strong need for trajectory tools to support these systems. This thesis describes the optimization of interplanetary trajectories from Earth to Mars for spacecraft utilizing low-thrust electrical propulsion systems. It is assumed that the controls are the thrust direction and the thrust setting. Specifically, the minimum time and minimum propellant problems are studied and solutions are computed with the sequential gradient-restoration algorithm (SGRA). The results indicate that, when the thrust direction and thrust setting are simultaneously optimized, the minimum time and minimum propellant solutions are not identical. For minimum time, it is found that the thrust setting must be at the maximum value; also, the thrust direction has a normal component with a switch at midcourse from upward to downward. This changes the curvature of the trajectory, has a beneficial effect on time, but a detrimental effect on propellant mass; indeed, the propellant mass ratio of the minimum time solution is about twice that of the Hohmann transfer solution. Thus, the minimum time solution yields a rather inefficient trajectory. For minimum propellant consumption, it is found that the best thrust setting is bang-zero-bang (maximum thrust, followed by coasting, followed by maximum thrust) and that the best thrust direction is tangent to the trajectory. This is a rather efficient trajectory; to three significant digits, the associated mass ratio is the same as that of the Hohmann transfer solution, even for thrust-to-weight ratios of
Particle swarm optimization of ascent trajectories of multistage launch vehicles
Pontani, Mauro
2014-02-01
Multistage launch vehicles are commonly employed to place spacecraft and satellites in their operational orbits. If the rocket characteristics are specified, the optimization of its ascending trajectory consists of determining the optimal control law that leads to maximizing the final mass at orbit injection. The numerical solution of a similar problem is not trivial and has been pursued with different methods, for decades. This paper is concerned with an original approach based on the joint use of swarming theory and the necessary conditions for optimality. The particle swarm optimization technique represents a heuristic population-based optimization method inspired by the natural motion of bird flocks. Each individual (or particle) that composes the swarm corresponds to a solution of the problem and is associated with a position and a velocity vector. The formula for velocity updating is the core of the method and is composed of three terms with stochastic weights. As a result, the population migrates toward different regions of the search space taking advantage of the mechanism of information sharing that affects the overall swarm dynamics. At the end of the process the best particle is selected and corresponds to the optimal solution to the problem of interest. In this work the three-dimensional trajectory of the multistage rocket is assumed to be composed of four arcs: (i) first stage propulsion, (ii) second stage propulsion, (iii) coast arc (after release of the second stage), and (iv) third stage propulsion. The Euler-Lagrange equations and the Pontryagin minimum principle, in conjunction with the Weierstrass-Erdmann corner conditions, are employed to express the thrust angles as functions of the adjoint variables conjugate to the dynamics equations. The use of these analytical conditions coming from the calculus of variations leads to obtaining the overall rocket dynamics as a function of seven parameters only, namely the unknown values of the initial state
On high-resolution manoeuvres control via trajectory optimization
Indian Academy of Sciences (India)
A H MAZINAN; M SHAHI
2017-02-01
This research is on a realization of control approach in line with the trajectory optimization for the purpose of dealing with overactuated spacecraft in the process of the high-resolution manoeuvres. The idea behind the research is to realize closed control loops to cope with the rotational angles and the corresponding angular rates,synchronously, to handle the spacecraft manoeuvres. It is to be noted that the traditional techniques may not have sufficient merit to deal with such a complicated process, suitably. The proposed trajectory optimization is designed to provide the three-axis referenced commands, in finite burn, for transferring the aforementioned overactuated spacecraft from the initial orbit to its final outcomes in the orbital transfer process. The outcomes are realized through the variations of the orbital parameters, including the inclination, the eccentricity, the angular momentum, the semi-major axis and so on, in the high-resolution manoeuvres. It aims to get the system under control to guarantee the performance of the three-dimensional rotational angles tracking to be desirable, instantly. The contribution of the research is to make the high-thrust optimization trajectory,which is organized in association with the new configuration of the three-axis attitude control approach, to be applicable to manage the present overactuated spacecraft in the procedure of high-resolution orbital transfer process. The investigated outcomes of the research are efficient and competitive along with the potential materials through a series of experiments, as long as the desirable tracking performance in the three-dimensional space manoeuvres is apparently guaranteed.
Theoretical Foundation of Copernicus: A Unified System for Trajectory Design and Optimization
Ocampo, Cesar; Senent, Juan S.; Williams, Jacob
2010-01-01
The fundamental methods are described for the general spacecraft trajectory design and optimization software system called Copernicus. The methods rely on a unified framework that is used to model, design, and optimize spacecraft trajectories that may operate in complex gravitational force fields, use multiple propulsion systems, and involve multiple spacecraft. The trajectory model, with its associated equations of motion and maneuver models, are discussed.
Recent Improvements to the Copernicus Trajectory Design and Optimization System
Williams, Jacob; Senent, Juan S.; Ocampo, Cesar; Lee, David E.
2012-01-01
Copernicus is a software tool for spacecraft trajectory design and optimization. The latest version (v3.0.1) was released in October 2011. It is available at no cost to NASA centers, government contractors, and organizations with a contractual affiliation with NASA. This paper is a brief overview of the recent development history of Copernicus. An overview of the evolution of the software and a discussion of significant new features and improvements is given, and how the tool is used to design spacecraft missions
Spin system trajectory analysis under optimal control pulses
Kuprov, Ilya
2013-08-01
Several methods are proposed for the analysis, visualization and interpretation of high-dimensional spin system trajectories produced by quantum mechanical simulations. It is noted that expectation values of specific observables in large spin systems often feature fast, complicated and hard-to-interpret time dynamics and suggested that populations of carefully selected subspaces of states are much easier to analyze and interpret. As an illustration of the utility of the proposed methods, it is demonstrated that the apparent "noisy" appearance of many optimal control pulses in NMR and EPR spectroscopy is an illusion - the underlying spin dynamics is shown to be smooth, orderly and very tightly controlled.
Directory of Open Access Journals (Sweden)
Zhengnan Li
2016-01-01
Full Text Available To solve the multiobjective optimization problem on hypersonic glider vehicle trajectory design subjected to complex constraints, this paper proposes a multiobjective trajectory optimization method that combines the boundary intersection method and pseudospectral method. The multiobjective trajectory optimization problem (MTOP is established based on the analysis of the feature of hypersonic glider vehicle trajectory. The MTOP is translated into a set of general optimization subproblems by using the boundary intersection method and pseudospectral method. The subproblems are solved by nonlinear programming algorithm. In this method, the solution that has been solved is employed as the initial guess for the next subproblem so that the time consumption of the entire multiobjective trajectory optimization problem shortens. The maximal range and minimal peak heat problem is solved by the proposed method. The numerical results demonstrate that the proposed method can obtain the Pareto front of the optimal trajectory, which can provide the reference for the trajectory design of hypersonic glider vehicle.
Hybrid Models for Trajectory Error Modelling in Urban Environments
Angelatsa, E.; Parés, M. E.; Colomina, I.
2016-06-01
This paper tackles the first step of any strategy aiming to improve the trajectory of terrestrial mobile mapping systems in urban environments. We present an approach to model the error of terrestrial mobile mapping trajectories, combining deterministic and stochastic models. Due to urban specific environment, the deterministic component will be modelled with non-continuous functions composed by linear shifts, drifts or polynomial functions. In addition, we will introduce a stochastic error component for modelling residual noise of the trajectory error function. First step for error modelling requires to know the actual trajectory error values for several representative environments. In order to determine as accurately as possible the trajectories error, (almost) error less trajectories should be estimated using extracted nonsemantic features from a sequence of images collected with the terrestrial mobile mapping system and from a full set of ground control points. Once the references are estimated, they will be used to determine the actual errors in terrestrial mobile mapping trajectory. The rigorous analysis of these data sets will allow us to characterize the errors of a terrestrial mobile mapping system for a wide range of environments. This information will be of great use in future campaigns to improve the results of the 3D points cloud generation. The proposed approach has been evaluated using real data. The data originate from a mobile mapping campaign over an urban and controlled area of Dortmund (Germany), with harmful GNSS conditions. The mobile mapping system, that includes two laser scanner and two cameras, was mounted on a van and it was driven over a controlled area around three hours. The results show the suitability to decompose trajectory error with non-continuous deterministic and stochastic components.
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
The problem of real-time trajectory optimization for small solid launch vehicle of operational responsive space (ORS) was studied by using pseudospectral method. According to the characteristic of the trajectory design, the dynamics model was set up in the inertia right-angled reference frame, and the equation and parameter at the orbit injection point were simplified and converted. The infinite dimension dynamic optimal control problem was converted to a finite dimension static state optimization problem and the algorithm reduced the complexity so as to become a general algorithm in trajectories optimization. With the trajectories optimization of a three-stage solid vehicle with a liquor upper stage as example, the model of the trajectory optimization was set up and simulations were carried out. The results demonstrated the advantage and validity of the pseudospectral method. The rejection time of fairing was also analyzed by the simulation results, and the optimal flight procedure and trajectory were obtained.
Miele, A.; Wang, T.; Melvin, W. W.; Tzeng, C. Y.
1988-01-01
The optimal-control problem of abort-landing trajectories in the presence of low-altitude wind shear is investigated analytically. The vertical-plane Newtonian motion of a point-mass aircraft in a steady wind field is modeled, and a sequential gradient-restoration algorithm is applied. Numerical results showing the effects of wind-shear intensity, initial altitude, and power-setting rate are presented in extensive graphs and discussed in detail. Optimal trajectories for strong or severe wind shears are found to begin with a descent, followed by level flight and then an ascent after leaving the shear region.
Aerodynamic Shape Optimization Using Hybridized Differential Evolution
Madavan, Nateri K.
2003-01-01
An aerodynamic shape optimization method that uses an evolutionary algorithm known at Differential Evolution (DE) in conjunction with various hybridization strategies is described. DE is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Various hybridization strategies for DE are explored, including the use of neural networks as well as traditional local search methods. A Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the hybrid DE optimizer. The method is implemented on distributed parallel computers so that new designs can be obtained within reasonable turnaround times. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. (The final paper will include at least one other aerodynamic design application). The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated.
Original Framework for Optimizing Hybrid Energy Supply
Directory of Open Access Journals (Sweden)
Amevi Acakpovi
2016-01-01
Full Text Available This paper proposes an original framework for optimizing hybrid energy systems. The recent growth of hybrid energy systems in remote areas across the world added to the increasing cost of renewable energy has triggered the inevitable development of hybrid energy systems. Hybrid energy systems always pose a problem of optimization of cost which has been approached with different perspectives in the recent past. This paper proposes a framework to guide the techniques of optimizing hybrid energy systems in general. The proposed framework comprises four stages including identification of input variables for energy generation, establishment of models of energy generation by individual sources, development of artificial intelligence, and finally summation of selected sources. A case study of a solar, wind, and hydro hybrid system was undertaken with a linear programming approach. Substantial results were obtained with regard to how load requests were constantly satisfied while minimizing the cost of electricity. The developed framework gained its originality from the fact that it has included models of individual sources of energy that even make the optimization problem more complex. This paper also has impacts on the development of policies which will encourage the integration and development of renewable energies.
Pinson, Robin Marie
Mission proposals that land spacecraft on asteroids are becoming increasingly popular. However, in order to have a successful mission the spacecraft must reliably and softly land at the intended landing site with pinpoint precision. The problem under investigation is how to design a propellant (fuel) optimal powered descent trajectory that can be quickly computed onboard the spacecraft, without interaction from ground control. The goal is to autonomously design the optimal powered descent trajectory onboard the spacecraft immediately prior to the descent burn for use during the burn. Compared to a planetary powered landing problem, the challenges that arise from designing an asteroid powered descent trajectory include complicated nonlinear gravity fields, small rotating bodies, and low thrust vehicles. The nonlinear gravity fields cannot be represented by a constant gravity model nor a Newtonian model. The trajectory design algorithm needs to be robust and efficient to guarantee a designed trajectory and complete the calculations in a reasonable time frame. This research investigates the following questions: Can convex optimization be used to design the minimum propellant powered descent trajectory for a soft landing on an asteroid? Is this method robust and reliable to allow autonomy onboard the spacecraft without interaction from ground control? This research designed a convex optimization based method that rapidly generates the propellant optimal asteroid powered descent trajectory. The solution to the convex optimization problem is the thrust magnitude and direction, which designs and determines the trajectory. The propellant optimal problem was formulated as a second order cone program, a subset of convex optimization, through relaxation techniques by including a slack variable, change of variables, and incorporation of the successive solution method. Convex optimization solvers, especially second order cone programs, are robust, reliable, and are guaranteed
Low-Thrust Trajectory Optimization with Simplified SQP Algorithm
Parrish, Nathan L.; Scheeres, Daniel J.
2017-01-01
The problem of low-thrust trajectory optimization in highly perturbed dynamics is a stressing case for many optimization tools. Highly nonlinear dynamics and continuous thrust are each, separately, non-trivial problems in the field of optimal control, and when combined, the problem is even more difficult. This paper de-scribes a fast, robust method to design a trajectory in the CRTBP (circular restricted three body problem), beginning with no or very little knowledge of the system. The approach is inspired by the SQP (sequential quadratic programming) algorithm, in which a general nonlinear programming problem is solved via a sequence of quadratic problems. A few key simplifications make the algorithm presented fast and robust to initial guess: a quadratic cost function, neglecting the line search step when the solution is known to be far away, judicious use of end-point constraints, and mesh refinement on multiple shooting with fixed-step integration.In comparison to the traditional approach of plugging the problem into a “black-box” NLP solver, the methods shown converge even when given no knowledge of the solution at all. It was found that the only piece of information that the user needs to provide is a rough guess for the time of flight, as the transfer time guess will dictate which set of local solutions the algorithm could converge on. This robustness to initial guess is a compelling feature, as three-body orbit transfers are challenging to design with intuition alone. Of course, if a high-quality initial guess is available, the methods shown are still valid.We have shown that endpoints can be efficiently constrained to lie on 3-body repeating orbits, and that time of flight can be optimized as well. When optimizing the endpoints, we must make a trade between converging quickly on sub-optimal endpoints or converging more slowly on end-points that are arbitrarily close to optimal. It is easy for the mission design engineer to adjust this trade based on
Development of a Multi-Event Trajectory Optimization Tool for Noise-Optimized Approach Route Design
Braakenburg, M.L.; Hartjes, S.; Visser, H.G.; Hebly, S.J.
2011-01-01
This paper presents preliminary results from an ongoing research effort towards the development of a multi-event trajectory optimization methodology that allows to synthesize RNAV approach routes that minimize a cumulative measure of noise, taking into account the total noise effect aggregated for a
Optimized Fuzzy Control For Natural Trajectory Based Fes- Swinging Motion
Directory of Open Access Journals (Sweden)
B.S.K.K Ibrahim
2011-12-01
Full Text Available The use of electrical signals to restore the function of paralyzed muscles is called functional electrical stimulation (FES. FES is a promising method to restore mobility to individuals paralyzed due to spinal cord injury (SCI. A crucial issue of FES is the control of motor function by the artificial activation of paralyzed muscles due to the various characteristics of the underlying physiological/biomechanical system. Muscle response characteristics are nonlinear and time-varying. After developing a nonlinear model describing the dynamic behavior of the knee joint and muscles, a closed-loop approach of control strategy to track the reference trajectory is assessed in computer simulations. Then, the controller was validated through experimental work. In this approach only the quadriceps muscle is stimulated to perform the swinging motion by controlling the amount of stimulation pulsewidth. An approach of fuzzy trajectory tracking control of swinging motion optimized with genetic algorithm is presented. The results show the effectiveness of the approach in controlling FES-induced swinging motion in the simulation as well as in the practical environment.
Automating Initial Guess Generation for High Fidelity Trajectory Optimization Tools
Villa, Benjamin; Lantoine, Gregory; Sims, Jon; Whiffen, Gregory
2013-01-01
Many academic studies in spaceflight dynamics rely on simplified dynamical models, such as restricted three-body models or averaged forms of the equations of motion of an orbiter. In practice, the end result of these preliminary orbit studies needs to be transformed into more realistic models, in particular to generate good initial guesses for high-fidelity trajectory optimization tools like Mystic. This paper reviews and extends some of the approaches used in the literature to perform such a task, and explores the inherent trade-offs of such a transformation with a view toward automating it for the case of ballistic arcs. Sample test cases in the libration point regimes and small body orbiter transfers are presented.
Trajectory and Population Metaheuristics applied to Combinatorial Optimization Problems
Directory of Open Access Journals (Sweden)
Natalia Alancay
2016-04-01
Full Text Available In the world there are a multitude of everyday problems that require a solution that meets a set of requirements in the most appropriate way maximizing or minimizing a certain value. However, finding an optimal solution for certain optimization problems can be an incredibly difficult or an impossible task. This is because when a problem becomes large enough, we have to look through a huge number of possible solutions, the most efficient solution, that is, the one that has the lower cost. The ways to treat feasible solutions for their practical application are varied. One of the strategy that has gained a great acceptance and that has been getting an important formal body are the metaheuristics since it is established strategies to cross and explore the space of solutions of the problem usually generated in a random and iterative way. The main advantage of this technique is their flexibility and robustness, which allows them to be applied to a wide range of problems. In this work we focus on a metaheuristic based on Simulated Annealing trajectory and a population - based Cellular Genetic Algorithm with the objective of carrying out a study and comparison of the results obtained in its application for the resolution of a set of academic problems of combinatorial optimization.
Six-DOF trajectory optimization for reusable launch vehicles via Gauss pseudospectral method
Institute of Scientific and Technical Information of China (English)
Zhen Wang; Zhong Wu
2016-01-01
To be close to the practical flight process and in-crease the precision of optimal trajectory, a six-degree-of- freedom (6-DOF) trajectory is optimized for the reusable launch vehicle (RLV) using the Gauss pseudospectral method (GPM). Different from the traditional trajectory optimization problem which generaly considers the RLV as a point mass, the coupling between translational dynamics and rotational dynamics is taken into account. An optimization problem is formulated to minimize a performance index subject to 6-DOF equations of motion, including translational and rotational dynamics. A two-step optimal strategy is then introduced to reduce the large calculations caused by multiple variables and convergence confinement in 6-DOF trajectory optimization. The simulation results demonstrate that the 6-DOF trajectory optimal strategy for RLV is feasible.
A Hybrid Trajectory Clustering for Predicting User Navigation
Munaga, Hazarath; Venkateswarlu, N B
2011-01-01
Wireless sensor networks (WSNs) suffers from the hot spot problem where the sensor nodes closest to the base station are need to relay more packet than the nodes farther away from the base station. Thus, lifetime of sensory network depends on these closest nodes. Clustering methods are used to extend the lifetime of a wireless sensor network. However, current clustering algorithms usually utilize two techniques; selecting cluster heads with more residual energy, and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster and lengthen the network lifetime. Most of the algorithms use random selection for selecting the cluster heads. Here, we propose a novel trajectory clustering technique for selecting the cluster heads in WSNs. Our algorithm selects the cluster heads based on traffic and rotates periodically. It provides the first trajectory based clustering technique for selecting the cluster heads and to extenuate the hot spot problem by prolonging the network lif...
Hybrid optimization model of product concepts
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Deficiencies of applying the simple genetic algorithm to generate concepts were specified. Based on analyzing conceptual design and the morphological matrix of an excavator, the hybrid optimization model of generating its concepts was proposed, viz. an improved adaptive genetic algorithm was applied to explore the excavator concepts in the searching space of conceptual design, and a neural network was used to evaluate the fitness of the population. The optimization of generating concepts was finished through the "evolution - evaluation" iteration. The results show that by using the hybrid optimization model, not only the fitness evaluation and constraint conditions are well processed, but also the search precision and convergence speed of the optimization process are greatly improved. An example is presented to demonstrate the advantages of the proposed method and associated algorithms.
Direct trajectory optimization based on a mapped Chebyshev pseudospectral method
Institute of Scientific and Technical Information of China (English)
Guo Xiao; Zhu Ming
2013-01-01
In view of generating optimal trajectories of Bolza problems,standard Chebyshev pseudospectral (PS) method makes the points' accumulation near the extremities and rarefaction of nodes close to the center of interval,which causes an ill-condition of differentiation matrix and an oscillation of the optimal solution.For improvement upon the difficulties,a mapped Chebyshev pseudospectral method is proposed.A conformal map is applied to Chebyshev points to move the points closer to equidistant nodes.Condition number and spectral radius of differentiation matrices from both methods are presented to show the improvement.Furthermore,the modification keeps the Chebyshev pseudospectral method's advantage,the spectral convergence rate.Based on three numerical examples,a comparison of the execution time,convergence and accuracy is presented among the standard Chebyshev pseudospectral method,other collocation methods and the proposed one.In one example,the error of results from mapped Chebyshev pseudospectral method is reduced to 5％ of that from standard Chebyshev pseudospectral method.
A "Hybrid" Approach for Synthesizing Optimal Controllers of Hybrid Systems
DEFF Research Database (Denmark)
Zhao, Hengjun; Zhan, Naijun; Kapur, Deepak
2012-01-01
We propose an approach to reduce the optimal controller synthesis problem of hybrid systems to quantifier elimination; furthermore, we also show how to combine quantifier elimination with numerical computation in order to make it more scalable but at the same time, keep arising errors due...... to discretization manageable and within bounds. A major advantage of our approach is not only that it avoids errors due to numerical computation, but it also gives a better optimal controller. In order to illustrate our approach, we use the real industrial example of an oil pump provided by the German company HYDAC...
Miele, A.; Wang, T.; Williams, P. N.
2005-12-01
The success of the solar-electric ion engine powering the DS1 spacecraft has paved the way toward the use of low-thrust electrical engines in future planetary/interplanetary missions. Vis-à-vis a chemical engine, an electrical engine has a higher specific impulse, implying a possible decrease in propellant mass; however, the low-thrust aspect discourages the use of an electrical engine in the near-planet phases of a trip, since this might result in an increase in flight time. Therefore, a fundamental design problem is to find the best combination of chemical propulsion and electrical propulsion for a given mission, for example, a mission from Earth to Mars. With this in mind, this paper is the third of a series dealing with the optimization of Earth Mars missions via the use of hybrid engines, namely the combination of high-thrust chemical engines for planetary flight and low-thrust electrical engines for interplanetary flight. We look at the deep-space interplanetary portion of the trajectory under rather idealized conditions. The two major performance indexes, the propellant mass and the flight time, are in conflict with one another for the following reason: any attempt at reducing the former causes an increase in the latter and vice versa. Therefore, it is natural to consider a compromise performance index involving the scaled values of the propellant mass and flight time weighted respectively by the compromise factor C and its complement 1-C. We use the compromise factor as the parameter of the one-parameter family of compromise trajectories. Analyses carried out with the sequential gradient-restoration algorithm for optimal control problems lead to results which can be highlighted as follows. Thrust profile. Generally speaking, the thrust profile of the compromise trajectory includes three subarcs: the first subarc is characterized by maximum thrust in conjunction with positive (upward) thrust direction; the second subarc is characterized by zero thrust
A Hybrid Evolutionary Algorithm for Discrete Optimization
Directory of Open Access Journals (Sweden)
J. Bhuvana
2015-03-01
Full Text Available Most of the real world multi-objective problems demand us to choose one Pareto optimal solution out of a finite set of choices. Flexible job shop scheduling problem is one such problem whose solutions are required to be selected from a discrete solution space. In this study we have designed a hybrid genetic algorithm to solve this scheduling problem. Hybrid genetic algorithms combine both the aspects of the search, exploration and exploitation of the search space. Proposed algorithm, Hybrid GA with Discrete Local Search, performs global search through the GA and exploits the locality through discrete local search. Proposed hybrid algorithm not only has the ability to generate Pareto optimal solutions and also identifies them with less computation. Five different benchmark test instances are used to evaluate the performance of the proposed algorithm. Results observed shown that the proposed algorithm has produced the known Pareto optimal solutions through exploration and exploitation of the search space with less number of functional evaluations.
Optimization methods applied to hybrid vehicle design
Donoghue, J. F.; Burghart, J. H.
1983-01-01
The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.
Pritchard, Victoria L; Edmands, Suzanne
2013-03-01
Introgressive hybridization between genetically divergent populations is an important evolutionary process. The degree to which repeated hybridization events between the same parental taxa lead to similar genomic outcomes is unknown. This study addressed this question by following genomic trajectories of replicate hybrid swarms of the copepod Tigriopus californicus over many generations of free mating. Swarm composition was determined both by differential reproductive success of founder individuals and subsequent selection on hybrid genotypes. For one cross, between two populations showing differential fitness in the laboratory and no hybrid breakdown, the genetic trajectory was highly repeatable: replicates rapidly became dominated by alleles from the fitter parent. In a second cross, between two populations showing similar fitness and significant F2 hybrid breakdown, alleles from alternative populations dominated different replicates. Swarms exhibited a general temporal trend of decreasing cytonuclear mismatch. Some patterns of differential introgression across the genome were strikingly congruent amongst swarm replicates, both within and between cross types, and reflected patterns of segregation distortion previously observed within controlled crosses between the same parental populations. Differences in heterozygosity between the sexes, and evidence for a previously suspected sex-distortion locus, suggest that complex interactions between sex and genotype influence hybrid swarm outcome.
Minimum Time Trajectory Optimization of CNC Machining with Tracking Error Constraints
Directory of Open Access Journals (Sweden)
Qiang Zhang
2014-01-01
Full Text Available An off-line optimization approach of high precision minimum time feedrate for CNC machining is proposed. Besides the ordinary considered velocity, acceleration, and jerk constraints, dynamic performance constraint of each servo drive is also considered in this optimization problem to improve the tracking precision along the optimized feedrate trajectory. Tracking error is applied to indicate the servo dynamic performance of each axis. By using variable substitution, the tracking error constrained minimum time trajectory planning problem is formulated as a nonlinear path constrained optimal control problem. Bang-bang constraints structure of the optimal trajectory is proved in this paper; then a novel constraint handling method is proposed to realize a convex optimization based solution of the nonlinear constrained optimal control problem. A simple ellipse feedrate planning test is presented to demonstrate the effectiveness of the approach. Then the practicability and robustness of the trajectory generated by the proposed approach are demonstrated by a butterfly contour machining example.
Raiszadeh, Behzad; Queen, Eric M.; Hotchko, Nathaniel J.
2009-01-01
A capability to simulate trajectories of multiple interacting rigid bodies has been developed, tested and validated. This capability uses the Program to Optimize Simulated Trajectories II (POST 2). The standard version of POST 2 allows trajectory simulation of multiple bodies without force interaction. In the current implementation, the force interaction between the parachute and the suspended bodies has been modeled using flexible lines, allowing accurate trajectory simulation of the individual bodies in flight. The POST 2 multibody capability is intended to be general purpose and applicable to any parachute entry trajectory simulation. This research paper explains the motivation for multibody parachute simulation, discusses implementation methods, and presents validation of this capability.
Optimization of Conformational Dynamics in an Epistatic Evolutionary Trajectory.
González, Mariano M; Abriata, Luciano A; Tomatis, Pablo E; Vila, Alejandro J
2016-07-01
The understanding of protein evolution depends on the ability to relate the impact of mutations on molecular traits to organismal fitness. Biological activity and robustness have been regarded as important features in shaping protein evolutionary landscapes. Conformational dynamics, which is essential for protein function, has received little attention in the context of evolutionary analyses. Here we employ NMR spectroscopy, the chief experimental tool to describe protein dynamics at atomic level in solution at room temperature, to study the intrinsic dynamic features of a metallo- Β: -lactamase enzyme and three variants identified during a directed evolution experiment that led to an expanded substrate profile. We show that conformational dynamics in the catalytically relevant microsecond to millisecond timescale is optimized along the favored evolutionary trajectory. In addition, we observe that the effects of mutations on dynamics are epistatic. Mutation Gly262Ser introduces slow dynamics on several residues that surround the active site when introduced in the wild-type enzyme. Mutation Asn70Ser removes the slow dynamics observed for few residues of the wild-type enzyme, but increases the number of residues that undergo slow dynamics when introduced in the Gly262Ser mutant. These effects on dynamics correlate with the epistatic interaction between these two mutations on the bacterial phenotype. These findings indicate that conformational dynamics is an evolvable trait, and that proteins endowed with more dynamic active sites also display a larger potential for promoting evolution.
Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization
Directory of Open Access Journals (Sweden)
MadhuSudana Rao Nalluri
2017-01-01
Full Text Available With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM and multilayer perceptron (MLP technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs. Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.
Low-thrust trajectory optimization for multiple target bodies tour mission
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
Spacecraft science missions to planets or asteroids have historically visited only one or several celestial bodies per mission.The research goal of this paper is to create a trajectory design algorithm that generates trajectory allowing a spacecraft to visit a significant number of asteroids during a single mission.For the problem of global trajectory optimization,even with recent advances in low-thrust trajectory optimization,a full enumeration of this problem is not possible.This work presents an algorith...
Control Valve Trajectories for SOFC Hybrid System Startup
Energy Technology Data Exchange (ETDEWEB)
Gorrell, Megan; Banta, Larry; Rosen, William; Restrepo, Bernardo; Tucker, David
2012-07-01
Control and management of cathode airflow in a solid oxide fuel cell gas turbine hybrid power system was analyzed using the Hybrid Performance (HyPer) hardware simulation at the National Energy Technology (NETL), U.S. Department of Energy. This work delves into previously unexplored operating practices for HyPer, via simultaneous manipulation of bypass valves and the electric load on the generator. The work is preparatory to the development of a Multi-Input, Multi-Output (MIMO) controller for HyPer. A factorial design of experiments was conducted to acquire data for 81 different combinations of the manipulated variables, which consisted of three air flow control valves and the electric load on the turbine generator. From this data the response surface for the cathode airflow with respect to bypass valve positions was analyzed. Of particular interest is the control of airflow through the cathode during system startup and during large load swings. This paper presents an algorithm for controlling air mass flow through the cathode based on a modification of the steepest ascent method.
Solid Rocket Motor Design Using Hybrid Optimization
Directory of Open Access Journals (Sweden)
Kevin Albarado
2012-01-01
Full Text Available A particle swarm/pattern search hybrid optimizer was used to drive a solid rocket motor modeling code to an optimal solution. The solid motor code models tapered motor geometries using analytical burn back methods by slicing the grain into thin sections along the axial direction. Grains with circular perforated stars, wagon wheels, and dog bones can be considered and multiple tapered sections can be constructed. The hybrid approach to optimization is capable of exploring large areas of the solution space through particle swarming, but is also able to climb “hills” of optimality through gradient based pattern searching. A preliminary method for designing tapered internal geometry as well as tapered outer mold-line geometry is presented. A total of four optimization cases were performed. The first two case studies examines designing motors to match a given regressive-progressive-regressive burn profile. The third case study studies designing a neutrally burning right circular perforated grain (utilizing inner and external geometry tapering. The final case study studies designing a linearly regressive burning profile for right circular perforated (tapered grains.
Directory of Open Access Journals (Sweden)
Panfeng Huang
2014-09-01
Full Text Available The tethered space robot (TSR is a new concept of space robot which consists of a robot platform, space tether and operation robot. This paper presents a multi-objective optimal trajectory planning and a coordinated tracking control scheme for TSR based on velocity impulse in the approaching phase. Both total velocity impulse and flight time are included in this optimization. The non-dominated sorting genetic algorithm is employed to obtain the optimal trajectory Pareto solution using the TSR dynamic model and optimal trajectory planning model. The coordinated tracking control scheme utilizes optimal velocity impulse. Furthermore, the PID controller is designed in order to compensate for the distance measurement errors. The PID control force is optimized and distributed to thrusters and the space tether using a simulated annealing algorithm. The attitude interferential torque of the space tether is compensated a using time-delay algorithm through reaction wheels. The simulation results show that the multi-objective optimal trajectory planning method can reveal the relationships among flight time, fuel consumption, planar view angle and velocity impulse number. This method can provide a series of optimal trajectory according to a number of special tasks. The coordinated control scheme can significantly save thruster fuel for tracking the optimal trajectory, restrain the attitude interferential torque produced by space tether and maintain the relative attitude stability of the operation robot.
An Expert System-Driven Method for Parametric Trajectory Optimization During Conceptual Design
Dees, Patrick D.; Zwack, Mathew R.; Steffens, Michael; Edwards, Stephen; Diaz, Manuel J.; Holt, James B.
2015-01-01
During the early phases of engineering design, the costs committed are high, costs incurred are low, and the design freedom is high. It is well documented that decisions made in these early design phases drive the entire design's life cycle cost. In a traditional paradigm, key design decisions are made when little is known about the design. As the design matures, design changes become more difficult in both cost and schedule to enact. The current capability-based paradigm, which has emerged because of the constrained economic environment, calls for the infusion of knowledge usually acquired during later design phases into earlier design phases, i.e. bringing knowledge acquired during preliminary and detailed design into pre-conceptual and conceptual design. An area of critical importance to launch vehicle design is the optimization of its ascent trajectory, as the optimal trajectory will be able to take full advantage of the launch vehicle's capability to deliver a maximum amount of payload into orbit. Hence, the optimal ascent trajectory plays an important role in the vehicle's affordability posture yet little of the information required to successfully optimize a trajectory is known early in the design phase. Thus, the current paradigm of optimizing ascent trajectories involves generating point solutions for every change in a vehicle's design parameters. This is often a very tedious, manual, and time-consuming task for the analysts. Moreover, the trajectory design space is highly non-linear and multi-modal due to the interaction of various constraints. When these obstacles are coupled with the Program to Optimize Simulated Trajectories (POST), an industry standard program to optimize ascent trajectories that is difficult to use, expert trajectory analysts are required to effectively optimize a vehicle's ascent trajectory. Over the course of this paper, the authors discuss a methodology developed at NASA Marshall's Advanced Concepts Office to address these issues
Enabling Parametric Optimal Ascent Trajectory Modeling During Early Phases of Design
Holt, James B.; Dees, Patrick D.; Diaz, Manuel J.
2015-01-01
During the early phases of engineering design, the costs committed are high, costs incurred are low, and the design freedom is high. It is well documented that decisions made in these early design phases drive the entire design's life cycle. In a traditional paradigm, key design decisions are made when little is known about the design. As the design matures, design changes become more difficult -- in both cost and schedule -- to enact. Indeed, the current capability-based paradigm that has emerged because of the constrained economic environment calls for the infusion of knowledge acquired during later design phases into earlier design phases, i.e. bring knowledge acquired during preliminary and detailed design into pre-conceptual and conceptual design. An area of critical importance to launch vehicle design is the optimization of its ascent trajectory, as the optimal trajectory will be able to take full advantage of the launch vehicle's capability to deliver a maximum amount of payload into orbit. Hence, the optimal ascent trajectory plays an important role in the vehicle's affordability posture as the need for more economically viable access to space solutions are needed in today's constrained economic environment. The problem of ascent trajectory optimization is not a new one. There are several programs that are widely used in industry that allows trajectory analysts to, based on detailed vehicle and insertion orbit parameters, determine the optimal ascent trajectory. Yet, little information is known about the launch vehicle early in the design phase - information that is required of many different disciplines in order to successfully optimize the ascent trajectory. Thus, the current paradigm of optimizing ascent trajectories involves generating point solutions for every change in a vehicle's design parameters. This is often a very tedious, manual, and time-consuming task for the analysts. Moreover, the trajectory design space is highly non-linear and multi
Trajectory optimization for real-time guidance. I - Time-varying LQR on a parallel processor
Psiaki, Mark L.; Park, Kihong
1990-01-01
A key algorithmic element of a real-time trajectory optimization hardware/software implementation, the quadratic program (QP) solver element, is presented. The purpose of the effort is to make nonlinear trajectory optimization fast enough to provide real-time commands during guidance of a vehicle such as an aeromaneuvering orbiter. Many methods of nonlinear programming require the solution of a QP at each iteration. In the trajectory optimization case the QP has a special dynamic programming structure, a LQR-like structure. QP algorithm speed is increased by taking advantage of this special structure and by parallel implementation.
Multi-Objective Hybrid Optimal Control for Multiple-Flyby Low-Thrust Mission Design
Englander, Jacob A.; Vavrina, Matthew A.; Ghosh, Alexander R.
2015-01-01
Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. In addition, a time-history of control variables must be chosen that defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The method is demonstrated on a hypothetical mission to the main asteroid belt.
Trajectory generation algorithm for smooth movement of a hybrid-type robot Rocker-Pillar
Energy Technology Data Exchange (ETDEWEB)
Jung, Seung Min; Choi, Dong Kyu; Kim, Jong Won [School of Mechanical and Aerospace Engineering, Seoul National University, Seoul (Korea, Republic of); Kim, Hwa Soo [Dept. of Mechanical System Engineering, Kyonggi University, Suwon (Korea, Republic of)
2016-11-15
While traveling on rough terrain, smooth movement of a mobile robot plays an important role in carrying out the given tasks successfully. This paper describes the trajectory generation algorithm for smooth movement of hybrid-type mobile robot Rocker-Pillar by adjusting the angular velocity of its caterpillar as well as each wheel velocity in such a manner to minimize a proper index for smoothness. To this end, a new Smoothness index (SI) is first suggested to evaluate the smoothness of movement of Rocker-Pillar. Then, the trajectory generation algorithm is proposed to reduce the undesired oscillations of its Center of mass (CoM). The experiment are performed to examine the movement of Rocker-Pillar climbing up the step whose height is twice larger than its wheel radius. It is verified that the resulting SI is improved by more than 40 % so that the movement of Rocker-Pillar becomes much smoother by the proposed trajectory algorithm.
On a Variational Approach to Optimization of Hybrid Mechanical Systems
Directory of Open Access Journals (Sweden)
Vadim Azhmyakov
2010-01-01
Full Text Available This paper deals with multiobjective optimization techniques for a class of hybrid optimal control problems in mechanical systems. We deal with general nonlinear hybrid control systems described by boundary-value problems associated with hybrid-type Euler-Lagrange or Hamilton equations. The variational structure of the corresponding solutions makes it possible to reduce the original “mechanical” problem to an auxiliary multiobjective programming reformulation. This approach motivates possible applications of theoretical and computational results from multiobjective optimization related to the original dynamical optimization problem. We consider first order optimality conditions for optimal control problems governed by hybrid mechanical systems and also discuss some conceptual algorithms.
A Robot Trajectory Optimization Approach for Thermal Barrier Coatings Used for Free-Form Components
Cai, Zhenhua; Qi, Beichun; Tao, Chongyuan; Luo, Jie; Chen, Yuepeng; Xie, Changjun
2017-08-01
This paper is concerned with a robot trajectory optimization approach for thermal barrier coatings. As the requirements of high reproducibility of complex workpieces increase, an optimal thermal spraying trajectory should not only guarantee an accurate control of spray parameters defined by users (e.g., scanning speed, spray distance, scanning step, etc.) to achieve coating thickness homogeneity but also help to homogenize the heat transfer distribution on the coating surface. A mesh-based trajectory generation approach is introduced in this work to generate path curves on a free-form component. Then, two types of meander trajectories are generated by performing a different connection method. Additionally, this paper presents a research approach for introducing the heat transfer analysis into the trajectory planning process. Combining heat transfer analysis with trajectory planning overcomes the defects of traditional trajectory planning methods (e.g., local over-heating), which helps form the uniform temperature field by optimizing the time sequence of path curves. The influence of two different robot trajectories on the process of heat transfer is estimated by coupled FEM models which demonstrates the effectiveness of the presented optimization approach.
A Robot Trajectory Optimization Approach for Thermal Barrier Coatings Used for Free-Form Components
Cai, Zhenhua; Qi, Beichun; Tao, Chongyuan; Luo, Jie; Chen, Yuepeng; Xie, Changjun
2017-10-01
This paper is concerned with a robot trajectory optimization approach for thermal barrier coatings. As the requirements of high reproducibility of complex workpieces increase, an optimal thermal spraying trajectory should not only guarantee an accurate control of spray parameters defined by users (e.g., scanning speed, spray distance, scanning step, etc.) to achieve coating thickness homogeneity but also help to homogenize the heat transfer distribution on the coating surface. A mesh-based trajectory generation approach is introduced in this work to generate path curves on a free-form component. Then, two types of meander trajectories are generated by performing a different connection method. Additionally, this paper presents a research approach for introducing the heat transfer analysis into the trajectory planning process. Combining heat transfer analysis with trajectory planning overcomes the defects of traditional trajectory planning methods (e.g., local over-heating), which helps form the uniform temperature field by optimizing the time sequence of path curves. The influence of two different robot trajectories on the process of heat transfer is estimated by coupled FEM models which demonstrates the effectiveness of the presented optimization approach.
Hybrid optimization schemes for quantum control
Energy Technology Data Exchange (ETDEWEB)
Goerz, Michael H.; Koch, Christiane P. [Universitaet Kassel, Theoretische Physik, Kassel (Germany); Whaley, K. Birgitta [University of California, Department of Chemistry, Berkeley, CA (United States)
2015-12-15
Optimal control theory is a powerful tool for solving control problems in quantum mechanics, ranging from the control of chemical reactions to the implementation of gates in a quantum computer. Gradient-based optimization methods are able to find high fidelity controls, but require considerable numerical effort and often yield highly complex solutions. We propose here to employ a two-stage optimization scheme to significantly speed up convergence and achieve simpler controls. The control is initially parametrized using only a few free parameters, such that optimization in this pruned search space can be performed with a simplex method. The result, considered now simply as an arbitrary function on a time grid, is the starting point for further optimization with a gradient-based method that can quickly converge to high fidelities. We illustrate the success of this hybrid technique by optimizing a geometric phase gate for two superconducting transmon qubits coupled with a shared transmission line resonator, showing that a combination of Nelder-Mead simplex and Krotov's method yields considerably better results than either one of the two methods alone. (orig.)
Hybrid Optimization for Wind Turbine Thick Airfoils
Energy Technology Data Exchange (ETDEWEB)
Grasso, F. [ECN Wind Energy, Petten (Netherlands)
2012-06-15
One important element in aerodynamic design of wind turbines is the use of specially tailored airfoils to increase the ratio of energy capture and reduce cost of energy. This work is focused on the design of thick airfoils for wind turbines by using numerical optimization. A hybrid scheme is proposed in which genetic and gradient based algorithms are combined together to improve the accuracy and the reliability of the design. Firstly, the requirements and the constraints for this class of airfoils are described; then, the hybrid approach is presented. The final part of this work is dedicated to illustrate a numerical example regarding the design of a new thick airfoil. The results are discussed and compared to existing airfoils.
Time optimal trajectories for mobile robots with two independently driven wheels
Energy Technology Data Exchange (ETDEWEB)
Reister, D.B.; Pin, F.G.
1992-03-01
This paper addresses the problem of time-optional motions for a mobile platform in a planar environment. The platform has two non-steerable independently driven wheels. The overall mission of the robot is expressed in terms of a sequence of via points at which the platform must be at rest in a given configuration (position and orientation). The objective is to plan time-optimal trajectories between these configurations assuming an unobstructed environment. Using Pontryagin`s maximum principle (PMP), we formally demonstrate that all time optimal motions of the platform for this problem occur for bang-bang controls on the wheels (at each instant, the acceleration on each wheel is either at its upper or lower limit). The PMP, however, only provides necessary conditions for time optimality. To find the time optimal robot trajectories, we first parameterize the bang-bang trajectories using the switch times on the wheels (the times at which the wheel accelerations change sign). With this parameterization, we can fully search the robot trajectory space and find the switch times that will produce particular paths to a desired final configuration of the platform. We show numerically that robot trajectories with three switch times (two on one wheel, one on the other) can reach any position, while trajectories with four switch times can reach any configuration. By numerical comparison with other trajectories involving similar or greater numbers of switch times, we then identify the sets of time-optimal trajectories. These are uniquely defined using ranges of the parameters, and consist of subsets of trajectories with three switch times for the problem when the final orientation of the robot is not specified, and four switch times when a full final configuration is specified. We conclude with a description of the use of the method for trajectory planning for one of our robots.
Time optimal trajectories for mobile robots with two independently driven wheels
Energy Technology Data Exchange (ETDEWEB)
Reister, D.B.; Pin, F.G.
1992-03-01
This paper addresses the problem of time-optional motions for a mobile platform in a planar environment. The platform has two non-steerable independently driven wheels. The overall mission of the robot is expressed in terms of a sequence of via points at which the platform must be at rest in a given configuration (position and orientation). The objective is to plan time-optimal trajectories between these configurations assuming an unobstructed environment. Using Pontryagin's maximum principle (PMP), we formally demonstrate that all time optimal motions of the platform for this problem occur for bang-bang controls on the wheels (at each instant, the acceleration on each wheel is either at its upper or lower limit). The PMP, however, only provides necessary conditions for time optimality. To find the time optimal robot trajectories, we first parameterize the bang-bang trajectories using the switch times on the wheels (the times at which the wheel accelerations change sign). With this parameterization, we can fully search the robot trajectory space and find the switch times that will produce particular paths to a desired final configuration of the platform. We show numerically that robot trajectories with three switch times (two on one wheel, one on the other) can reach any position, while trajectories with four switch times can reach any configuration. By numerical comparison with other trajectories involving similar or greater numbers of switch times, we then identify the sets of time-optimal trajectories. These are uniquely defined using ranges of the parameters, and consist of subsets of trajectories with three switch times for the problem when the final orientation of the robot is not specified, and four switch times when a full final configuration is specified. We conclude with a description of the use of the method for trajectory planning for one of our robots.
Optimal online robot trajectory generation in Cartesian space
Bazaz, Shafat A.; Tondu, Bertrand
1997-12-01
We propose the use of cubic quadratic cubic squared (CQCS) spline for the trajectory generation in Cartesian space. Use of CQCS spline gives simple analytical solution to minimum time trajectory generation with velocity and acceleration constraints. The expressions for wandering time and wandering acceleration are also calculated. A straight line path with constant maximum allowed speed in minimum time can be generated with this method. This property leads to interpolate two position points by constant speed straight line motion with smooth transition. The advantage of this method is that the trajectory thus obtained is traversed in minimum time while passing through the given intermediate points. The simplicity of this method makes its on-line computation possible.
Ship Trajectory Control Optimization in Anti-collision Maneuvering
Directory of Open Access Journals (Sweden)
Jinfen Zhang
2013-03-01
Full Text Available A lot of attentions are being paid to ship’s intelligent anti-collision by researchers. Several solutions have been introduced to find an optimum trajectory for ship, such as Game Theory, Genetic or Evolutionary Algorithms and so on. However, ship’s maneuverability should be taken into consideration before their real applications. This paper focuses on ship’s trajectory control problem in anti-collision maneuvering. At first, a simple linear ship maneuverability model is introduced to simulate its movement under different speed and rudder angle. After that, ship’s trajectory control is studied by considering the duration of rudder, operation distance to turning points, and maximum angular velocity. The details for algorithm design are also introduced. By giving some restrictions according to the requirements from COLREGs, the intervals for rudder angle in different circumstances can be determined by the curves. The results can give very meaningful guidance for seafarers when making decisions.
Optimization of Low Thrust Trajectories With Terminal Aerocapture
2003-06-01
SOLVING OPTIMAL CONTROL PROBLEMS A. PRELIMINARIES An optimal control problem is the...respectively. Eqn.(86) will be useful later for verifying the switching structure of the controls. B. SOLUTION METHODS Methods for solving optimal control problems can...for a more accurate indirect method. F. VERIFICATION OF OPTIMALITY When solving optimal control problems one is often challenged as to how one
Directory of Open Access Journals (Sweden)
Thi Rein Myo
2008-11-01
Full Text Available Optimal point-to-point trajectory planning for planar redundant manipulator is considered in this study. The main objective is to minimize the sum of the position error of the end-effector at each intermediate point along the trajectory so that the end-effector can track the prescribed trajectory accurately. An algorithm combining Genetic Algorithm and Pattern Search as a Generalized Pattern Search GPS is introduced to design the optimal trajectory. To verify the proposed algorithm, simulations for a 3-D-O-F planar manipulator with different end-effector trajectories have been carried out. A comparison between the Genetic Algorithm and the Generalized Pattern Search shows that The GPS gives excellent tracking performance.
Systematic Low-Thrust Trajectory Optimization for a Multi-Rendezvous Mission using Adjoint Scaling
Jiang, Fanghua
2016-01-01
A deep-space exploration mission with low-thrust propulsion to rendezvous with multiple asteroids is investigated. Indirect methods, based on the optimal control theory, are implemented to optimize the fuel consumption. The application of indirect methods for optimizing low-thrust trajectories between two asteroids is briefly given. An effective method is proposed to provide initial guesses for transfers between close near-circular near-coplanar orbits. The conditions for optimality of a multi-asteroid rendezvous mission are determined. The intuitive method of splitting the trajectories into several legs that are solved sequentially is applied first. Then the results are patched together by a scaling method to provide a tentative guess for optimizing the whole trajectory. Numerical examples of optimizing three probe exploration sequences that contain a dozen asteroids each demonstrate the validity and efficiency of these methods.
A multi-objective approach to the design of low thrust space trajectories using optimal control
Dellnitz, Michael; Ober-Blöbaum, Sina; Post, Marcus; Schütze, Oliver; Thiere, Bianca
2009-11-01
In this article, we introduce a novel three-step approach for solving optimal control problems in space mission design. We demonstrate its potential by the example task of sending a group of spacecraft to a specific Earth L 2 halo orbit. In each of the three steps we make use of recently developed optimization methods and the result of one step serves as input data for the subsequent one. Firstly, we perform a global and multi-objective optimization on a restricted class of control functions. The solutions of this problem are (Pareto-)optimal with respect to Δ V and flight time. Based on the solution set, a compromise trajectory can be chosen suited to the mission goals. In the second step, this selected trajectory serves as initial guess for a direct local optimization. We construct a trajectory using a more flexible control law and, hence, the obtained solutions are improved with respect to control effort. Finally, we consider the improved result as a reference trajectory for a formation flight task and compute trajectories for several spacecraft such that these arrive at the halo orbit in a prescribed relative configuration. The strong points of our three-step approach are that the challenging design of good initial guesses is handled numerically by the global optimization tool and afterwards, the last two steps only have to be performed for one reference trajectory.
Institute of Scientific and Technical Information of China (English)
Xiong LUO; Xiaoping FAN; Heng ZHANG; Tefang CHEN
2004-01-01
Optimal trajectory planning for robot manipulators plays an important role in implementing the high productivity for robots.The performance indexes used in optimal trajectory planning are classified into two main categories:optimum traveling time and optimum mechanical energy of the actuators.The current trajectory planning algorithms are designed based on one of the above two performance indexes.So far,there have been few planning algorithms designed to satisfy two performance indexes simultaneously.On the other hand,some deficiencies arise in the existing integrated optimization algorithms of trajectory planning.In order to overcome those deficiencies,the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators.In the algorithm,two object functions are designed based on the specific weight coefficient method and "ideal point" strategy.Moreover,based on the features of optimization problem,the intensified evolutionary programming is proposed to solve the corresponding optimization model.Especially,for the Stanford Robot,the high-quality solutions are found at a lower cost.
Peng, Haijun; Wang, Wei
2016-10-01
An adaptive surrogate model-based multi-objective optimization strategy that combines the benefits of invariant manifolds and low-thrust control toward developing a low-computational-cost transfer trajectory between libration orbits around the L1 and L2 libration points in the Sun-Earth system has been proposed in this paper. A new structure for a multi-objective transfer trajectory optimization model that divides the transfer trajectory into several segments and gives the dominations for invariant manifolds and low-thrust control in different segments has been established. To reduce the computational cost of multi-objective transfer trajectory optimization, a mixed sampling strategy-based adaptive surrogate model has been proposed. Numerical simulations show that the results obtained from the adaptive surrogate-based multi-objective optimization are in agreement with the results obtained using direct multi-objective optimization methods, and the computational workload of the adaptive surrogate-based multi-objective optimization is only approximately 10% of that of direct multi-objective optimization. Furthermore, the generating efficiency of the Pareto points of the adaptive surrogate-based multi-objective optimization is approximately 8 times that of the direct multi-objective optimization. Therefore, the proposed adaptive surrogate-based multi-objective optimization provides obvious advantages over direct multi-objective optimization methods.
Optimizing the specificity of nucleic acid hybridization.
Zhang, David Yu; Chen, Sherry Xi; Yin, Peng
2012-01-22
The specific hybridization of complementary sequences is an essential property of nucleic acids, enabling diverse biological and biotechnological reactions and functions. However, the specificity of nucleic acid hybridization is compromised for long strands, except near the melting temperature. Here, we analytically derived the thermodynamic properties of a hybridization probe that would enable near-optimal single-base discrimination and perform robustly across diverse temperature, salt and concentration conditions. We rationally designed 'toehold exchange' probes that approximate these properties, and comprehensively tested them against five different DNA targets and 55 spurious analogues with energetically representative single-base changes (replacements, deletions and insertions). These probes produced discrimination factors between 3 and 100+ (median, 26). Without retuning, our probes function robustly from 10 °C to 37 °C, from 1 mM Mg(2+) to 47 mM Mg(2+), and with nucleic acid concentrations from 1 nM to 5 µM. Experiments with RNA also showed effective single-base change discrimination.
Aziz, Jonathan D.; Parker, Jeffrey S.; Scheeres, Daniel J.; Englander, Jacob A.
2017-01-01
Low-thrust trajectories about planetary bodies characteristically span a high count of orbital revolutions. Directing the thrust vector over many revolutions presents a challenging optimization problem for any conventional strategy. This paper demonstrates the tractability of low-thrust trajectory optimization about planetary bodies by applying a Sundman transformation to change the independent variable of the spacecraft equations of motion to the eccentric anomaly and performing the optimization with differential dynamic programming. Fuel-optimal geocentric transfers are shown in excess of 1000 revolutions while subject to Earths J2 perturbation and lunar gravity.
A Hybrid Aggressive Space Mapping Algorithm for EM Optimization
DEFF Research Database (Denmark)
Bakr, M.; Bandler, J. W.; Georgieva, N.;
1999-01-01
We present a novel, Hybrid Aggressive Space Mapping (HASM) optimization algorithm. HASM is a hybrid approach exploiting both the Trust Region Aggressive Space Mapping (TRASM) algorithm and direct optimization. It does not assume that the final space-mapped design is the true optimal design and is...
Global Optimization of Low-Thrust Interplanetary Trajectories Subject to Operational Constraints
Englander, Jacob A.; Vavrina, Matthew A.; Hinckley, David
2016-01-01
Low-thrust interplanetary space missions are highly complex and there can be many locally optimal solutions. While several techniques exist to search for globally optimal solutions to low-thrust trajectory design problems, they are typically limited to unconstrained trajectories. The operational design community in turn has largely avoided using such techniques and has primarily focused on accurate constrained local optimization combined with grid searches and intuitive design processes at the expense of efficient exploration of the global design space. This work is an attempt to bridge the gap between the global optimization and operational design communities by presenting a mathematical framework for global optimization of low-thrust trajectories subject to complex constraints including the targeting of planetary landing sites, a solar range constraint to simplify the thermal design of the spacecraft, and a real-world multi-thruster electric propulsion system that must switch thrusters on and off as available power changes over the course of a mission.
Institute of Scientific and Technical Information of China (English)
Wang Nan; Shen Lincheng; Liu Hongfu; Chen Jing; Hu Tianjiang
2013-01-01
Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft.Aiming to overcome these difficulties,this paper presents an alternative approach for trajectory optimization,where the problem is formulated into a parametric optimization of the maneuver variables under a tactics template framework.To reduce the size of the problem,global sensitivity analysis (GSA) is performed to identify the less-influential maneuver variables.The probability collectives (PC) algorithm,which is well-suited to discrete and discontinuous optimization,is applied to solve the trajectory optimization problem.The robustness of the trajectory is assessed through multiple sampling around the chosen values of the maneuver variables.Meta-models based on radius basis function (RBF) are created for evaluations of the means and deviations of the problem objectives and constraints.To guarantee the approximation accuracy,the meta-models are adaptively updated during optimization.The proposed approach is demonstrated on a typical airground attack mission scenario.Results reveal that the proposed approach is capable of generating robust and optimal trajectories with both accuracy and efficiency.
Time-optimal trajectories for mobile robots with two independently driven wheels
Energy Technology Data Exchange (ETDEWEB)
Reister, D.B.; Pin, F.G. (Oak Ridge National Lab., TN (United States))
1994-02-01
This article addresses the problem of time-optimal motions for a mobile platform in a planar environment. The platform has two nonsteerable, independently driven wheels. The overall mission of the robot is expressed in terms of a sequence of via points at which the platform must be at rest in a given configuration (position and orientation). The objective is to plan time-optimal trajectories between these configurations, assuming an unobstructed environment. Using Pontryagin's maximum principle (PMP), we formally demonstrate that all time-optimal motions of the platform for this problem occur for bang-bang controls on the wheels (at each instant, the acceleration on each wheel is at either its upper or its lower limit). The PMP, however, provides only the conditions necessary for time optimality. To find the time-optimal robot trajectories, we first parameterize the bang-bang trajectories using the switch times on the wheels (the times at which the wheel accelerations change sign). With this parameterization, we can fully search the robot trajectory space and find the switch times that will produce particular paths to a desired final configuration of the platform. We show numerically that robot trajectories with three switch times (two on one wheel and one on the other) can reach any position, while trajectories with four switch times can reach any configuration. By numerical comparison with other trajectories involving similar or greater numbers of switch times, we then identify the sets of time-optimal trajectories. 28 refs., 22 figs., 1 tab.
Directory of Open Access Journals (Sweden)
Junqiang Lou
2015-01-01
Full Text Available Trajectory planning is an effective feed-forward control technology for vibration suppression of flexible manipulators. However, the inherent drawback makes this strategy inefficient when dealing with modeling errors and disturbances. An optimal trajectory planning approach is proposed and applied to a flexible piezoelectric manipulator system in this paper, which is a combination of feed-forward trajectory planning method and feedback control of piezoelectric actuators. Specifically, the joint controller is responsible for the trajectory tracking and gross vibration suppression of the link during motion, while the active controller of actuators is expected to deal with the link vibrations after joint motion. In the procedure of trajectory planning, the joint angle of the link is expressed as a quintic polynomial function. And the sum of the link vibration energy is chosen as the objective function. Then, genetic algorithm is used to determine the optimal trajectory. The effectiveness of the proposed method is validated by simulation and experiments. Both the settling time and peak value of the link vibrations along the optimal trajectory reduce significantly, with the active control of the piezoelectric actuators.
Optimization and Sensitivity Analysis for a Launch Trajectory
2014-12-01
Finding solutions to a boundary value problem can be time consuming and difficult due to the twin curses of sensitivity and dimensionality. In an...Hamiltonian boundary value problem. Finding solutions to a boundary value problem can be time consuming and difficult due to the twin curses of...case performance to be identified. This knowledge will lead to more flexibility in the launch window and a more reliable launch trajectory. D
Jianwen Guo; Zhenzhong Sun; Hong Tang; Xuejun Jia; Song Wang; Xiaohui Yan; Guoliang Ye; Guohong Wu
2016-01-01
All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test fun...
Trajectory optimization for vehicles using control vector parameterization and nonlinear programming
Energy Technology Data Exchange (ETDEWEB)
Spangelo, I.
1994-12-31
This thesis contains a study of optimal trajectories for vehicles. Highly constrained nonlinear optimal control problems have been solved numerically using control vector parameterization and nonlinear programming. Control vector parameterization with shooting has been described in detail to provide the reader with the theoretical background for the methods which have been implemented, and which are not available in standard text books. Theoretical contributions on accuracy analysis and gradient computations have also been presented. Optimal trajectories have been computed for underwater vehicles controlled in all six degrees of freedom by DC-motor driven thrusters. A class of nonlinear optimal control problems including energy-minimization, possibly combined with time minimization and obstacle avoidance, has been developed. A program system has been specially designed and written in the C language to solve this class of optimal control problems. Control vector parameterization with single shooting was used. This special implementation has made it possible to perform a detailed analysis, and to investigate numerical details of this class of optimization methods which would have been difficult using a general purpose CVP program system. The results show that this method for solving general optimal control problems is well suited for use in guidance and control of marine vehicles. Results from rocket trajectory optimization has been studied in this work to bring knowledge from this area into the new area of trajectory optimization of marine vehicles. 116 refs., 24 figs., 23 tabs.
Trajectory Optimization for Helicopter Unmanned Aerial Vehicles (UAVs)
2010-06-01
INTRODUCTION For quite some time, mathematicians have struggled with a reliable method for solving optimal control problems with complicated nonlinear...problems have several fundamental differences from the computation of PDEs. Solving optimal control problems asks for the collective and...and differentiations. These are all critical pieces for solving optimal control problems . The derivative of ( )Nix t at the LGL node kt is
Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory
Directory of Open Access Journals (Sweden)
Rahul Deb Das
2016-11-01
Full Text Available Transport mode information is essential for understanding people’s movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real time mode specific patronage estimation. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classification. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format along with a provision of inferring different outcome possibilities, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel hybrid knowledge driven framework is developed by integrating a fuzzy logic and a neural network to complement each other’s limitations. Thus the aim of this paper is to automate the tuning process in order to generate an intelligent hybrid model that can perform effectively in near-real time mode detection using GPS trajectory. Tests demonstrate that a hybrid knowledge driven model works better than a purely knowledge driven model and at per the machine learning models in the context of transport mode detection.
Institute of Scientific and Technical Information of China (English)
TENG Fei; ZHANG Wanxi; LIANG Jicai; GAO Song
2015-01-01
Most of the existing studies use constant force to reduce springback while researching stretch force. However, variable stretch force can reduce springback more efficiently. The current research on springback prediction in stretch bending forming mainly focuses on artificial neural networks combined with the finite element simulation. There is a lack of springback prediction by support vector regression (SVR). In this paper, SVR is applied to predict springback inthe three-dimensional stretch bending forming process,and variable stretch force trajectory is optimized. Six parameters of variable stretch force trajectory are chosen as the input parameters of the SVR model. Sixty experiments generated by design of experiments (DOE) are carried out to train and test the SVR model. The experimental results confirm that the accuracy of the SVR model is higher than that of artificial neural networks. Based on this model, an optimization algorithm of variable stretch force trajectory using particle swarm optimization (PSO) is proposed. The springback amount is used as the objective function. Changes of local thickness are applied as the criterion of forming constraints. The objection and constraints are formulated by response surface models. The precision of response surface models is examined. Six different stretch force trajectories are employed to certify springback reduction in the optimum stretch force trajectory, which can efficiently reduce springback. This research proposes a new method of springback prediction using SVR and optimizes variable stretch force trajectory to reduce springback.
Dynamic Optimization Algorithm for Flying Trajectory of a Free-flying Space Robot
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A new method of dynamic optimization for the flying trajectory of a free-flying space robot based on its flying motion characteristics is presented. The continuous flying trajectory is broken into a number of segment and the control efforts and the duration of the segment are chosen as the optimization parameters. The objective function is made by using the weighted sum of the fuel used and the time spent, and the constraint equations are selected. Finally, the internal point punishment function method is adopted in the optimization program, and the results of computer simulation are given.
Overview and Software Architecture of the Copernicus Trajectory Design and Optimization System
Williams, Jacob; Senent, Juan S.; Ocampo, Cesar; Mathur, Ravi; Davis, Elizabeth C.
2010-01-01
The Copernicus Trajectory Design and Optimization System represents an innovative and comprehensive approach to on-orbit mission design, trajectory analysis and optimization. Copernicus integrates state of the art algorithms in optimization, interactive visualization, spacecraft state propagation, and data input-output interfaces, allowing the analyst to design spacecraft missions to all possible Solar System destinations. All of these features are incorporated within a single architecture that can be used interactively via a comprehensive GUI interface, or passively via external interfaces that execute batch processes. This paper describes the Copernicus software architecture together with the challenges associated with its implementation. Additionally, future development and planned new capabilities are discussed. Key words: Copernicus, Spacecraft Trajectory Optimization Software.
Directory of Open Access Journals (Sweden)
S. Subchan Subchan
2011-08-01
Full Text Available Numerical solution of constrained nonlinear optimal control problem is an important field in a wide range of applications in science and engineering. The real time solution for an optimal control problem is a challenge issue especially the state constrained handling. Missile trajectory shaping with terminal bunt manoeuvre with state constaints is addressed. The problem can be stated as an optimal control problem in which an objective function is minimised satisfying a series of constraints on the trajectory which includes state and control constraints. Numerical solution based on a direct multiple shooting is proposed. As an example the method has been implemented to a design of optimal trajectory for a missile where the missile must struck the target by vertical dive. The qualitative analysis and physical interpretation of the numerical solutions are given.
Chai, Runqi; Savvaris, Al; Tsourdos, Antonios
2016-06-01
In this paper, a fuzzy physical programming (FPP) method has been introduced for solving multi-objective Space Manoeuvre Vehicles (SMV) skip trajectory optimization problem based on hp-adaptive pseudospectral methods. The dynamic model of SMV is elaborated and then, by employing hp-adaptive pseudospectral methods, the problem has been transformed to nonlinear programming (NLP) problem. According to the mission requirements, the solutions were calculated for each single-objective scenario. To get a compromised solution for each target, the fuzzy physical programming (FPP) model is proposed. The preference function is established with considering the fuzzy factor of the system such that a proper compromised trajectory can be acquired. In addition, the NSGA-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the FPP solution. Simulation results indicate that the proposed method is effective and feasible in terms of dealing with the multi-objective skip trajectory optimization for the SMV.
Wu, Q.; Xiong, F.; Wang, F.; Xiong, Y.
2016-10-01
In order to reduce the computational time, a fully parallel implementation of the particle swarm optimization (PSO) algorithm on a graphics processing unit (GPU) is presented. Instead of being executed on the central processing unit (CPU) sequentially, PSO is executed in parallel via the GPU on the compute unified device architecture (CUDA) platform. The processes of fitness evaluation, updating of velocity and position of all particles are all parallelized and introduced in detail. Comparative studies on the optimization of four benchmark functions and a trajectory optimization problem are conducted by running PSO on the GPU (GPU-PSO) and CPU (CPU-PSO). The impact of design dimension, number of particles and size of the thread-block in the GPU and their interactions on the computational time is investigated. The results show that the computational time of the developed GPU-PSO is much shorter than that of CPU-PSO, with comparable accuracy, which demonstrates the remarkable speed-up capability of GPU-PSO.
Optimism and Pessimism as Predictors of Alcohol Use Trajectories in Adolescence
Wray, Tyler B.; Dvorak, Rob D.; Hsia, Jennifer F.; Arens, Ashley M.; Schweinle, William E.
2013-01-01
A range of research has recognized the benefits of optimism in a variety of health-related outcomes. Pessimism has received less attention but may be a distinct concept that is uniquely related to certain health behaviors, including drug use. The present study examined relationships between optimism and pessimism and alcohol use trajectories of…
Trajectory optimization of multiple quad-rotor UAVs in collaborative assembling task
Directory of Open Access Journals (Sweden)
Chen Yongbo
2016-02-01
Full Text Available A hierarchic optimization strategy based on the offline path planning process and online trajectory planning process is presented to solve the trajectory optimization problem of multiple quad-rotor unmanned aerial vehicles in the collaborative assembling task. Firstly, the path planning process is solved by a novel parallel intelligent optimization algorithm, the central force optimization-genetic algorithm (CFO-GA, which combines the central force optimization (CFO algorithm with the genetic algorithm (GA. Because of the immaturity of the CFO, the convergence analysis of the CFO is completed by the stability theory of the linear time-variant discrete-time systems. The results show that the parallel CFO-GA algorithm converges faster than the parallel CFO and the central force optimization-sequential quadratic programming (CFO-SQP algorithm. Then, the trajectory planning problem is established based on the path planning results. In order to limit the range of the attitude angle and guarantee the flight stability, the optimized object is changed from the ordinary six-degree-of-freedom rigid-body dynamic model to the dynamic model with an inner-loop attitude controller. The results show that the trajectory planning process can be solved by the mature SQP algorithm easily. Finally, the discussion and analysis of the real-time performance of the hierarchic optimization strategy are presented around the group number of the waypoints and the equal interval time.
Trajectory optimization of multiple quad-rotor UAVs in collaborative assembling task
Institute of Scientific and Technical Information of China (English)
Chen Yongbo; Yu Jianqiao; Mei Yuesong; Zhang Siyu; Ai Xiaolin; Jia Zhenyue
2016-01-01
A hierarchic optimization strategy based on the offline path planning process and online trajectory planning process is presented to solve the trajectory optimization problem of multiple quad-rotor unmanned aerial vehicles in the collaborative assembling task. Firstly, the path planning process is solved by a novel parallel intelligent optimization algorithm, the central force optimization-genetic algorithm (CFO-GA), which combines the central force optimization (CFO) algorithm with the genetic algorithm (GA). Because of the immaturity of the CFO, the convergence analysis of the CFO is completed by the stability theory of the linear time-variant discrete-time sys-tems. The results show that the parallel CFO-GA algorithm converges faster than the parallel CFO and the central force optimization-sequential quadratic programming (CFO-SQP) algorithm. Then, the trajectory planning problem is established based on the path planning results. In order to limit the range of the attitude angle and guarantee the flight stability, the optimized object is changed from the ordinary six-degree-of-freedom rigid-body dynamic model to the dynamic model with an inner-loop attitude controller. The results show that the trajectory planning process can be solved by the mature SQP algorithm easily. Finally, the discussion and analysis of the real-time per-formance of the hierarchic optimization strategy are presented around the group number of the waypoints and the equal interval time.
Mann, F. I.; Horsewood, J. L.
1974-01-01
Modifications and improvements are described that were made to the HILTOP electric propulsion trajectory optimization computer program during calendar years 1973 and 1974. New program features include the simulation of power degradation, housekeeping power, launch asymptote declination optimization, and powered and unpowered ballistic multiple swingby missions with an optional deep space burn.
Optimism and Pessimism as Predictors of Alcohol Use Trajectories in Adolescence
Wray, Tyler B.; Dvorak, Rob D.; Hsia, Jennifer F.; Arens, Ashley M.; Schweinle, William E.
2013-01-01
A range of research has recognized the benefits of optimism in a variety of health-related outcomes. Pessimism has received less attention but may be a distinct concept that is uniquely related to certain health behaviors, including drug use. The present study examined relationships between optimism and pessimism and alcohol use trajectories of…
Control and Optimization of UAV Trajectory for Aerial Coverage in Photogrammetry Applications
Directory of Open Access Journals (Sweden)
POPESCU, D.
2016-08-01
Full Text Available Photogrammetry is a well-studied and much-used analysis tool. Typical use cases include area surveillance, flood monitoring and related tasks. Usually, an Unmanned Aerial System (UAS is used as support for image acquisition from an a priori delimited region in a semi-automated manner (via a mix of ground control and autonomous trajectory tracking. This in turn has led to various algorithms which handle path trajectory generation under realistic constraints but still many avenues remain open. In this paper, we consider typical costs and constraints (UAS dynamics, total-path length, line inter-distance, turn points, etc. in order to obtain, via optimization procedures, an optimal trajectory. To this end we make use of polyhedral set operations, flat trajectory generation and other similar tools. Additional work includes the study of non-convex regions and estimation of the number of photographs taken via Ehrhart polynomial computations.
Optimized Vertex Method and Hybrid Reliability
Smith, Steven A.; Krishnamurthy, T.; Mason, B. H.
2002-01-01
A method of calculating the fuzzy response of a system is presented. This method, called the Optimized Vertex Method (OVM), is based upon the vertex method but requires considerably fewer function evaluations. The method is demonstrated by calculating the response membership function of strain-energy release rate for a bonded joint with a crack. The possibility of failure of the bonded joint was determined over a range of loads. After completing the possibilistic analysis, the possibilistic (fuzzy) membership functions were transformed to probability density functions and the probability of failure of the bonded joint was calculated. This approach is called a possibility-based hybrid reliability assessment. The possibility and probability of failure are presented and compared to a Monte Carlo Simulation (MCS) of the bonded joint.
Optimal trajectory planning based on Hamiltonian function of a spherical mobile robot
Institute of Scientific and Technical Information of China (English)
Chen Ming; Zhan Qiang; Liu Zengbo; Cai Yao
2008-01-01
Designed for planetary exploration, a spherical mobile robot BHQ-1 was briefly introduced.The motion model of BHQ-1 was established and quasi-velocities were introduced to simplify some dynamic quantities.Based on the model, the time- and energy-based optimal trajectory of BHQ-1 was planned with Hamiltonian function.The effects of three key coefficients on the shape and direction of the planned trajectory were discussed by simulations.Experimental result of the robot ability in avoiding an obstacle was presented to validate the trajectory planning method.
Motion Planning Using an Impact-Based Hybrid Control for Trajectory Generation in Adaptive Walking
Directory of Open Access Journals (Sweden)
Umar Asif
2011-09-01
Full Text Available This paper aims to solve a major drawback of walking robots i.e. their inability to react to environmental disturbances while navigating in natural rough terrains. This problem is reduced here by suggesting the use of a hybrid force‐position control based trajectory generation with the impact dynamics into consideration that compensates for the stability variations, thus helping the robot react stably in the face of environmental disturbances. As a consequence, the proposed impact‐based hybrid control helps the robot achieve better and stable motion planning than conventional position‐based control algorithms. Dynamic simulations and real world outdoor experiments performed on a six legged hexapod robot show a relevant improvement in the robot locomotion.
High-accuracy optimal finite-thrust trajectories for Moon escape
Shen, Hong-Xin; Casalino, Lorenzo
2017-02-01
The optimization problem of fuel-optimal trajectories from a low circular Moon orbit to a target hyperbolic excess velocity vector using finite-thrust propulsion is solved. The ability to obtain the most accurate satisfaction of necessary optimality conditions in a high-accuracy dynamic model is the main motivation of the current study. The solutions allow attaining anytime-return Earth-interface conditions from a low lunar orbit. Gravitational effects of the Sun, Earth, and Moon are included throughout the entire trajectory. Severe constraints on the fuel budget combined with high-accuracy demands on the endpoint conditions necessitate a high-fidelity solution to the trajectory optimization problem and JPL DE405 ephemeris model is used to determine the perturbing bodies' positions. The optimization problem is solved using an indirect method. The optimality of the solution is verified by an application of Pontryagin's maximum principle. More accurate and fuel-efficient trajectories are found for the same mission objectives and constraints published in other research, emphasizing the advantages of this technique. It is also shown that the thrust structure consists of three finite burns. In contrast to previous research, no singular arc is required in the optimal solutions, and all the controls appear bang-bang.
A modular approach to intensity-modulated arc therapy optimization with noncoplanar trajectories
Papp, Dávid; Bortfeld, Thomas; Unkelbach, Jan
2015-07-01
Utilizing noncoplanar beam angles in volumetric modulated arc therapy (VMAT) has the potential to combine the benefits of arc therapy, such as short treatment times, with the benefits of noncoplanar intensity modulated radiotherapy (IMRT) plans, such as improved organ sparing. Recently, vendors introduced treatment machines that allow for simultaneous couch and gantry motion during beam delivery to make noncoplanar VMAT treatments possible. Our aim is to provide a reliable optimization method for noncoplanar isocentric arc therapy plan optimization. The proposed solution is modular in the sense that it can incorporate different existing beam angle selection and coplanar arc therapy optimization methods. Treatment planning is performed in three steps. First, a number of promising noncoplanar beam directions are selected using an iterative beam selection heuristic; these beams serve as anchor points of the arc therapy trajectory. In the second step, continuous gantry/couch angle trajectories are optimized using a simple combinatorial optimization model to define a beam trajectory that efficiently visits each of the anchor points. Treatment time is controlled by limiting the time the beam needs to trace the prescribed trajectory. In the third and final step, an optimal arc therapy plan is found along the prescribed beam trajectory. In principle any existing arc therapy optimization method could be incorporated into this step; for this work we use a sliding window VMAT algorithm. The approach is demonstrated using two particularly challenging cases. The first one is a lung SBRT patient whose planning goals could not be satisfied with fewer than nine noncoplanar IMRT fields when the patient was treated in the clinic. The second one is a brain tumor patient, where the target volume overlaps with the optic nerves and the chiasm and it is directly adjacent to the brainstem. Both cases illustrate that the large number of angles utilized by isocentric noncoplanar VMAT plans
Research of trajectory optimization on feeding manipulator based on internal penalty function
Wei, Chunli
2016-10-01
This paper has discussed the problems of trajectory optimization of feeding manipulator based on penalty function. Has selected the types of feeding robot, which work on NC machining center of the flexible workshop, and created the mathematical model with penalty function, for the purpose not only to optimize its walking path to reduce the production cost, but also improve its safety and efficiency of production. It has been verified by theoretical analysis and practice, the path optimization method is feasible.
Optimal low-thrust trajectories for nuclear and solar electric propulsion
Genta, G.; Maffione, P. F.
2016-01-01
The optimization of the trajectory and of the thrust profile of a low-thrust interplanetary transfer is usually solved under the assumption that the specific mass of the power generator is constant. While this is reasonable in the case of nuclear electric propulsion, if solar electric propulsion is used the specific mass depends on the distance of the spacecraft from the Sun. In the present paper the optimization of the trajectory of the spacecraft and of the thrust profile is solved under the latter assumption, to obtain optimized interplanetary trajectories for solar electric spacecraft, also taking into account all phases of the journey, from low orbit about the starting planet to low orbit about the destination one. General plots linking together the travel time, the specific mass of the generator and the propellant consumption are obtained.
Luo, Jianjun; Zong, Lijun; Wang, Mingming; Yuan, Jianping
2017-07-01
This paper presents an optimal trajectory planning scheme for robotic capturing of a tumbling object. Motion planning of a space robot is much more complex than that of a fixed-based robot, due to the dynamic coupling between the manipulator and its base. In this work, the Path Independent Workspace (PIW), in which no dynamic singularity occurs, and Path Dependent Workspace (PDW) of the space robot are first calculated by the proposed algorithm. The motion equations of the tumbling object are formulated based on the Euler dynamics equations and the quaternion, which are used to predict the long-term motion of a grasping point on the tumbling object. Subsequently, the obtained PIW workspace and predicted motion trajectories are used to plan the trajectory of the end-effector to intercept the grasping point with zero relative velocity (to avoid impact) in an optimal way. In order to avoid dynamic singularity occurring at the capture moment, the optimal capture occasion is first determined by three proposed criterions guaranteeing the capture can be safely, reliably and rapidly performed, then the optimal trajectory of the end-effector is generated minimizing a cost function which acts as a constraint on acceleration magnitude. Simulations are presented to demonstrate the trajectory planning scheme for a space robot with a 3-degree of freedom (DOF) manipulator grasping a tumbling satellite, the results show that the manipulator end-effector can smoothly intercept the grasping point on the tumbling satellite with zero relative velocity.
Miller, Michael; Strom, Ben; Breuer, Kenneth; Mandre, Shreyas
2014-11-01
We determine the feasibility of applying optimization algorithms to an oscillating hydrofoil's motion trajectory to determine maximum efficiency of energy capture. Optimization is performed using the Nelder-Meade downhill simplex method. The objective function is the energy captured measured experimentally in run-time with an oscillating hydrofoil capable of measuring mechanical energy capture in a laboratory flume. For sinusoidal trajectories, optimization is performed over pitch and heave amplitudes as well as frequency; this system is shown to be capable of optimization in run-time. The optimum efficiency of 30% is found for a pitch amplitude of 70°, a heave amplitude of 0.8* chord and a dimensionless frequency of 0.13. To treat non-sinusoidal trajectories, we expand them in a truncated Fourier series and consider the coefficients of this series as variables for optimization. The sinusoidal case is simply an extreme case of such a truncated Fourier series, with only one term in the series retained. We present a systematic method for optimization over general non-sinusoidal trajectories by including more and more terms in the Fourier series.
Hybrid Optimized and Localized Vibrational Coordinates.
Klinting, Emil Lund; König, Carolin; Christiansen, Ove
2015-11-01
We present a new type of vibrational coordinates denoted hybrid optimized and localized coordinates (HOLCs) aiming at a good set of rectilinear vibrational coordinates supporting fast convergence in vibrational stucture calculations. The HOLCs are obtained as a compromise between the recently promoted optimized coordinates (OCs) and localized coordinates (LCs). The three sets of coordinates are generally different from each other and differ from standard normal coordinates (NCs) as well. In determining the HOLCs, we optimize the vibrational self-consistent field (VSCF) energy with respect to orthogonal transformation of the coordinates, which is similar to determining OCs but for HOLCs we additionally introduce a penalty for delocalization, by using a measure of localization similar to that employed in determining LCs. The same theory and implementation covers OCs, LCs, and HOLCs. It is shown that varying one penalty parameter allows for connecting OCs and LCs. The HOLCs are compared to NCs, OCs, and LCs in their nature and performance as basis for vibrational coupled cluster (VCC) response calculations of vibrational anharmonic energies for a small set of simple systems comprising water, formaldehyde, and ethylene. It is found that surprisingly good results can be obtained with HOLCs by using potential energy surfaces as simple as quadratic Taylor expansions. Quite similar coordinates are found for the already established OCs but obtaining these OCs requires much more elaborate and expensive potential energy surfaces and localization is generally not guaranteed. The ability to compute HOLCs for somewhat larger systems is demonstrated for coumarin and the alanine quadramer. The good agreement between HOLCs and OCs, together with the much easier applicability of HOLCs for larger systems, suggests that HOLCs may be a pragmatically very interesting option for anharmonic calculations on medium to large molecular systems.
Optimal trajectory generation for generalization of discrete movements with boundary condition
DEFF Research Database (Denmark)
Herzog, Sebastian; Wörgötter, Florentin; Kulvicius, Tomas
2016-01-01
with zero error. Moreover, it has most of the properties of the state-of-the-art trajectory generation methods such as robustness to perturbations and generalisation to new boundary position and velocity conditions. We believe that, due to these features, our method has great potential for various robotic......Trajectory generation methods play an important role in robotics since they are essential for the execution of actions. In this paper we present a novel trajectory generation method for generalization of accurate movements with boundary conditions. Our approach originates from optimal control...... theory and is based on a second order dynamic system. We evaluate our method and compare it to state-of-the-art movement generation methods in both simulations and a real robot experiment. We show that the new method is very compact in its representation and can reproduce demonstrated trajectories...
Trajectory Based Optimal Segment Computation in Road Network Databases
DEFF Research Database (Denmark)
Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.
that adopt different approaches to computing the query. Algorithm AUG uses graph augmentation, and ITE uses iterative road-network partitioning. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings....... that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route...
Trajectory Based Optimal Segment Computation in Road Network Databases
DEFF Research Database (Denmark)
Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.
2013-01-01
that adopt different approaches to computing the query. Algorithm AUG uses graph augmentation, and ITE uses iterative road-network partitioning. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings....... that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route...
Global Optimal Trajectory in Chaos and NP-Hardness
Latorre, Vittorio; Gao, David Yang
This paper presents an unconventional theory and method for solving general nonlinear dynamical systems. Instead of the direct iterative methods, the discretized nonlinear system is first formulated as a global optimization problem via the least squares method. A newly developed canonical duality theory shows that this nonconvex minimization problem can be solved deterministically in polynomial time if a global optimality condition is satisfied. The so-called pseudo-chaos produced by linear iterative methods are mainly due to the intrinsic numerical error accumulations. Otherwise, the global optimization problem could be NP-hard and the nonlinear system can be really chaotic. A conjecture is proposed, which reveals the connection between chaos in nonlinear dynamics and NP-hardness in computer science. The methodology and the conjecture are verified by applications to the well-known logistic equation, a forced memristive circuit and the Lorenz system. Computational results show that the canonical duality theory can be used to identify chaotic systems and to obtain realistic global optimal solutions in nonlinear dynamical systems. The method and results presented in this paper should bring some new insights into nonlinear dynamical systems and NP-hardness in computational complexity theory.
The optimization of global fault tolerant trajectory for redundant manipulator based on self-motion
Directory of Open Access Journals (Sweden)
Zhang Jian
2015-01-01
Full Text Available The redundancy feature of manipulators provides the possibility for the fault tolerant trajectory planning. Aiming at the completion of the specific task, an algorithm of global fault tolerant trajectory optimization for redundant manipulator based on the self-motion is proposed in this paper. Firstly, inverse kinematics equation of single redundancy manipulator based on self-motion variable and null-space velocity array of Jacobian are analyzed. Secondly, the mathematical description of fault tolerance criteria of the configuration of manipulator is established and the fault tolerance configuration group of manipulator is obtained by using iteration traversal under the fault tolerance criteria. Then, considering the joint limits and minimum the energy consumption as the optimization target, the global fault tolerant joint trajectory is achieved. Finally, simulation for 7 degree of freedom (DOF manipulator is performed, by which the effectiveness of the algorithm is validated.
Optimal trajectory planning and train scheduling for urban rail transit systems
Wang, Yihui; van den Boom, Ton; De Schutter, Bart
2016-01-01
This book contributes to making urban rail transport fast, punctual and energy-efficient –significant factors in the importance of public transportation systems to economic, environmental and social requirements at both municipal and national levels. It proposes new methods for shortening passenger travel times and for reducing energy consumption, addressing two major topics: (1) train trajectory planning: the authors derive a nonlinear model for the operation of trains and present several approaches for calculating optimal and energy-efficient trajectories within a given schedule; and (2) train scheduling: the authors develop a train scheduling model for urban rail systems and optimization approaches with which to balance total passenger travel time with energy efficiency and other costs to the operator. Mixed-integer linear programming and pseudospectral methods are among the new methods proposed for single- and multi-train systems for the solution of the nonlinear trajectory planning problem which involv...
SU-E-T-436: Fluence-Based Trajectory Optimization for Non-Coplanar VMAT
Energy Technology Data Exchange (ETDEWEB)
Smyth, G; Bamber, JC; Bedford, JL [Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London (United Kingdom); Evans, PM [Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford (United Kingdom); Saran, FH; Mandeville, HC [The Royal Marsden NHS Foundation Trust, Sutton (United Kingdom)
2015-06-15
Purpose: To investigate a fluence-based trajectory optimization technique for non-coplanar VMAT for brain cancer. Methods: Single-arc non-coplanar VMAT trajectories were determined using a heuristic technique for five patients. Organ at risk (OAR) volume intersected during raytracing was minimized for two cases: absolute volume and the sum of relative volumes weighted by OAR importance. These trajectories and coplanar VMAT formed starting points for the fluence-based optimization method. Iterative least squares optimization was performed on control points 24° apart in gantry rotation. Optimization minimized the root-mean-square (RMS) deviation of PTV dose from the prescription (relative importance 100), maximum dose to the brainstem (10), optic chiasm (5), globes (5) and optic nerves (5), plus mean dose to the lenses (5), hippocampi (3), temporal lobes (2), cochleae (1) and brain excluding other regions of interest (1). Control point couch rotations were varied in steps of up to 10° and accepted if the cost function improved. Final treatment plans were optimized with the same objectives in an in-house planning system and evaluated using a composite metric - the sum of optimization metrics weighted by importance. Results: The composite metric decreased with fluence-based optimization in 14 of the 15 plans. In the remaining case its overall value, and the PTV and OAR components, were unchanged but the balance of OAR sparing differed. PTV RMS deviation was improved in 13 cases and unchanged in two. The OAR component was reduced in 13 plans. In one case the OAR component increased but the composite metric decreased - a 4 Gy increase in OAR metrics was balanced by a reduction in PTV RMS deviation from 2.8% to 2.6%. Conclusion: Fluence-based trajectory optimization improved plan quality as defined by the composite metric. While dose differences were case specific, fluence-based optimization improved both PTV and OAR dosimetry in 80% of cases.
Hopping trajectory optimization for surface exploration on small bodies
Liu, Yanjie; Zhu, Shengying; Cui, Pingyuan; Yu, Zhengshi; Zong, Hua
2017-07-01
Surface exploration is an important way to improve the understanding of small bodies. Considering the irregular and weak gravity field near a small body, the movement of the surface explorer is generally achieved by hopping. In this paper, a guidance algorithm method based on convex optimization approach for pinpoint hopping movement on a small body is developed in order to improve the stability and accuracy of surface exploration. We formulate a fuel-optimal control problem for the single pinpoint hopping and convert it into a second-order cone programming (SOCP) problem which can be solved effectively by primal-dual-interior points method. A multi-hopping scenario is also proposed for the long-distance transfer. To certificate the performance of the proposed guidance algorithm, a full set of simulations are conducted and the effectiveness are analyzed.
Directory of Open Access Journals (Sweden)
Alejandro MURRIETA-MENDOZA
2017-08-01
Full Text Available With the objective of reducing the flight cost and the amount of polluting emissions released in the atmosphere, a new optimization algorithm considering the climb, cruise and descent phases is presented for the reference vertical flight trajectory. The selection of the reference vertical navigation speeds and altitudes was solved as a discrete combinatory problem by means of a graph-tree passing through nodes using the beam search optimization technique. To achieve a compromise between the execution time and the algorithm’s ability to find the global optimal solution, a heuristic methodology introducing a parameter called “optimism coefficient was used in order to estimate the trajectory’s flight cost at every node. The optimal trajectory cost obtained with the developed algorithm was compared with the cost of the optimal trajectory provided by a commercial flight management system(FMS. The global optimal solution was validated against an exhaustive search algorithm(ESA, other than the proposed algorithm. The developed algorithm takes into account weather effects, step climbs during cruise and air traffic management constraints such as constant altitude segments, constant cruise Mach, and a pre-defined reference lateral navigation route. The aircraft fuel burn was computed using a numerical performance model which was created and validated using flight test experimental data.
Papp, Dávid
2013-01-01
We propose a novel optimization model for volumetric modulated arc therapy (VMAT) planning that directly optimizes deliverable leaf trajectories in the treatment plan optimization problem, and eliminates the need for a separate arc-sequencing step. In this model, a 360-degree arc is divided into a given number of arc segments in which the leaves move unidirectionally. This facilitates an algorithm that determines the optimal piecewise linear leaf trajectories for each arc segment, which are deliverable in a given treatment time. Multi-leaf collimator (MLC) constraints, including maximum leaf speed and interdigitation, are accounted for explicitly. The algorithm is customized to allow for VMAT delivery using constant gantry speed and dose rate, however, the algorithm generalizes to variable gantry speed if beneficial. We demonstrate the method for three different tumor sites: a head-and-neck case, a prostate case, and a paraspinal case. For that purpose, we first obtain a reference plan for intensity modulated...
de Pascale, P.; Vasile, M.; Casotto, S.
The design of interplanetary trajectories requires the solution of an optimization problem, which has been traditionally solved by resorting to various local optimization techniques. All such approaches, apart from the specific method employed (direct or indirect), require an initial guess, which deeply influences the convergence to the optimal solution. The recent developments in low-thrust propulsion have widened the perspectives of exploration of the Solar System, while they have at the same time increased the difficulty related to the trajectory design process. Continuous thrust transfers, typically characterized by multiple spiraling arcs, have a broad number of design parameters and thanks to the flexibility offered by such engines, they typically turn out to be characterized by a multi-modal domain, with a consequent larger number of optimal solutions. Thus the definition of the first guesses is even more challenging, particularly for a broad search over the design parameters, and it requires an extensive investigation of the domain in order to locate the largest number of optimal candidate solutions and possibly the global optimal one. In this paper a tool for the preliminary definition of interplanetary transfers with coast-thrust arcs and multiple swing-bys is presented. Such goal is achieved combining a novel methodology for the description of low-thrust arcs, with a global optimization algorithm based on a hybridization of an evolutionary step and a deterministic step. Low thrust arcs are described in a 3D model in order to account the beneficial effects of low-thrust propulsion for a change of inclination, resorting to a new methodology based on an inverse method. The two-point boundary values problem (TPBVP) associated with a thrust arc is solved by imposing a proper parameterized evolution of the orbital parameters, by which, the acceleration required to follow the given trajectory with respect to the constraints set is obtained simply through
OPTIMAL TARGET TRAJECTORY ESTIMATION AND FILTERING USING NETWORKED SENSORS
Institute of Scientific and Technical Information of China (English)
Jiangping HU; Xiaoming HU
2008-01-01
Target tracking using distributed sensor network is in general a challenging problem because it always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors such as distance and direction sensors for estimating a moving target is studied.The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisy nonlinear measurements and partially unknown input, which is generated by an exogenous system.In the worst case where the input is completely unknown, the exogenous dynamics is reduced to the random walk model. It can be shown that the nonlinear filter will have optimal convergence if the number of the sensors are large enough and the convergence rate will be highly improved if the sensors are deployed appropriately. This actually raises an interesting issue on active sensing: how to optimally move the sensors if they are considered as mobile multi-agent systems? Finally, a simulation example is given to illustrate and validate the construction of our filter.
Parallel Hybrid Vehicle Optimal Storage System
Bloomfield, Aaron P.
2009-01-01
A paper reports the results of a Hybrid Diesel Vehicle Project focused on a parallel hybrid configuration suitable for diesel-powered, medium-sized, commercial vehicles commonly used for parcel delivery and shuttle buses, as the missions of these types of vehicles require frequent stops. During these stops, electric hybridization can effectively recover the vehicle's kinetic energy during the deceleration, store it onboard, and then use that energy to assist in the subsequent acceleration.
Nonlinear dynamic analysis and optimal trajectory planning of a high-speed macro-micro manipulator
Yang, Yi-ling; Wei, Yan-ding; Lou, Jun-qiang; Fu, Lei; Zhao, Xiao-wei
2017-09-01
This paper reports the nonlinear dynamic modeling and the optimal trajectory planning for a flexure-based macro-micro manipulator, which is dedicated to the large-scale and high-speed tasks. In particular, a macro- micro manipulator composed of a servo motor, a rigid arm and a compliant microgripper is focused. Moreover, both flexure hinges and flexible beams are considered. By combining the pseudorigid-body-model method, the assumed mode method and the Lagrange equation, the overall dynamic model is derived. Then, the rigid-flexible-coupling characteristics are analyzed by numerical simulations. After that, the microscopic scale vibration excited by the large-scale motion is reduced through the trajectory planning approach. Especially, a fitness function regards the comprehensive excitation torque of the compliant microgripper is proposed. The reference curve and the interpolation curve using the quintic polynomial trajectories are adopted. Afterwards, an improved genetic algorithm is used to identify the optimal trajectory by minimizing the fitness function. Finally, the numerical simulations and experiments validate the feasibility and the effectiveness of the established dynamic model and the trajectory planning approach. The amplitude of the residual vibration reduces approximately 54.9%, and the settling time decreases 57.1%. Therefore, the operation efficiency and manipulation stability are significantly improved.
Directory of Open Access Journals (Sweden)
Jianwen Guo
2016-01-01
Full Text Available All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO and cuckoo search (CS algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.
Optimal low-thrust spiral trajectories using Lyapunov-based guidance
Yang, Da-lin; Xu, Bo; Zhang, Lei
2016-09-01
For an increasing number of electric propulsion systems used for real missions, it is very important to design optimal low-thrust spiral trajectories for these missions. However, it is particularly challenging to search for optimal low-thrust transfers. This paper describes an efficient optimal guidance scheme for the design of time-optimal and time-fixed fuel-optimal low-thrust spiral trajectories. The time-optimal solution is obtained with Lyapunov-based guidance, in which the artificial neural network (ANN) is adopted to implement control gains steering and the evolutionary algorithm is used as the learning algorithm for ANN. Moreover, the relative efficiency introduced in Q-law is analyzed and a periapis-and-apoapsis-centered burn structure is proposed for solving time-fixed fuel-optimal low-thrust orbit transfer problem. In this guidance scheme, the ANN is adopted to determine the burn structure within each orbital revolution and the optimal low-thrust orbit transfer problem is converted to the parameter optimization problem. This guidance scheme runs without an initial guess and provides closed form solutions. In addition, Earth J2 perturbation and Earth-shadow eclipse effects are considered in this paper. Finally, a comparison with solutions given by the literature demonstrates the effectiveness of the proposed method.
Masternak, Tadeusz J.
This research determines temperature-constrained optimal trajectories for a scramjet-based hypersonic reconnaissance vehicle by developing an optimal control formulation and solving it using a variable order Gauss-Radau quadrature collocation method with a Non-Linear Programming (NLP) solver. The vehicle is assumed to be an air-breathing reconnaissance aircraft that has specified takeoff/landing locations, airborne refueling constraints, specified no-fly zones, and specified targets for sensor data collections. A three degree of freedom scramjet aircraft model is adapted from previous work and includes flight dynamics, aerodynamics, and thermal constraints. Vehicle control is accomplished by controlling angle of attack, roll angle, and propellant mass flow rate. This model is incorporated into an optimal control formulation that includes constraints on both the vehicle and mission parameters, such as avoidance of no-fly zones and coverage of high-value targets. To solve the optimal control formulation, a MATLAB-based package called General Pseudospectral Optimal Control Software (GPOPS-II) is used, which transcribes continuous time optimal control problems into an NLP problem. In addition, since a mission profile can have varying vehicle dynamics and en-route imposed constraints, the optimal control problem formulation can be broken up into several "phases" with differing dynamics and/or varying initial/final constraints. Optimal trajectories are developed using several different performance costs in the optimal control formulation: minimum time, minimum time with control penalties, and maximum range. The resulting analysis demonstrates that optimal trajectories that meet specified mission parameters and constraints can be quickly determined and used for larger-scale operational and campaign planning and execution.
Traffic Aware Planner for Cockpit-Based Trajectory Optimization
Woods, Sharon E.; Vivona, Robert A.; Henderson, Jeffrey; Wing, David J.; Burke, Kelly A.
2016-01-01
The Traffic Aware Planner (TAP) software application is a cockpit-based advisory tool designed to be hosted on an Electronic Flight Bag and to enable and test the NASA concept of Traffic Aware Strategic Aircrew Requests (TASAR). The TASAR concept provides pilots with optimized route changes (including altitude) that reduce fuel burn and/or flight time, avoid interactions with known traffic, weather and restricted airspace, and may be used by the pilots to request a route and/or altitude change from Air Traffic Control. Developed using an iterative process, TAP's latest improvements include human-machine interface design upgrades and added functionality based on the results of human-in-the-loop simulation experiments and flight trials. Architectural improvements have been implemented to prepare the system for operational-use trials with partner commercial airlines. Future iterations will enhance coordination with airline dispatch and add functionality to improve the acceptability of TAP-generated route-change requests to pilots, dispatchers, and air traffic controllers.
Leaf trajectory calculation for dynamic multileaf collimation to realize optimized fluence profiles
Energy Technology Data Exchange (ETDEWEB)
Dirkx, M.L.P.; Heijmen, B.J.M.; Santvoort, J.P.C. van [University Hospital Rotterdam/Daniel den Hoed Cancer Center, Groene Hilledijk 301, 3075 EA Rotterdam (Netherlands)
1998-05-01
An algorithm for the calculation of the required leaf trajectories to generate optimized intensity modulated beam profiles by means of dynamic multileaf collimation is presented. This algorithm iteratively accounts for leaf transmission and collimator scatter and fully avoids tongue-and-groove underdosage effects. Tests on a large number of intensity modulated fields show that only a limited number of iterations, generally less than 10, are necessary to minimize the differences between optimized and realized fluence profiles. To assess the accuracy of the algorithm in combination with the dose calculation algorithm of the Cadplan 3D treatment planning system, predicted absolute dose distributions for optimized fluence profiles were compared with dose distributions measured on the MM50 Racetrack Microtron and resulting from the calculated leaf trajectories. Both theoretical and clinical cases yield an agreement within 2%, or within 2 mm in regions with a high dose gradient, showing that the accuracy is adequate for clinical application. (author)
Leaf trajectory calculation for dynamic multileaf collimation to realize optimized fluence profiles
Dirkx, M. L. P.; Heijmen, B. J. M.; van Santvoort, J. P. C.
1998-05-01
An algorithm for the calculation of the required leaf trajectories to generate optimized intensity modulated beam profiles by means of dynamic multileaf collimation is presented. This algorithm iteratively accounts for leaf transmission and collimator scatter and fully avoids tongue-and-groove underdosage effects. Tests on a large number of intensity modulated fields show that only a limited number of iterations, generally less than 10, are necessary to minimize the differences between optimized and realized fluence profiles. To assess the accuracy of the algorithm in combination with the dose calculation algorithm of the Cadplan 3D treatment planning system, predicted absolute dose distributions for optimized fluence profiles were compared with dose distributions measured on the MM50 Racetrack Microtron and resulting from the calculated leaf trajectories. Both theoretical and clinical cases yield an agreement within 2%, or within 2 mm in regions with a high dose gradient, showing that the accuracy is adequate for clinical application.
The BLAIRR Irradiation Facility Hybrid Spallation Target Optimization
Energy Technology Data Exchange (ETDEWEB)
Simos N.; Hanson A.; Brown, D.; Elbakhshawn, M.
2016-04-11
BLAIRR STUDY STATUS OVERVIEW Beamline Complex Evaluation/Assessment and Adaptation to the Goals Facility Radiological Constraints ? Large scale analyses of conventional facility and integrated shield (concrete, soil)Target Optimization and Design: Beam-target interaction optimization Hadronic interaction and energy deposition limitations Single phase and Hybrid target concepts Irradiation Damage Thermo-mechanical considerations Spallation neutron fluence optimization for (a) fast neutron irradiation damage (b) moderator/reflector studies, (c) NTOF potential and optimization (d) mono-energetic neutron beam
2015-01-01
The optimized mean-trajectory (OMT) approximation is a semiclassical method for computing vibrational response functions from action-quantized classical trajectories connected by discrete transitions representing radiation–matter interactions. Here we apply this method to an anharmonic chromophore coupled to a harmonic bath. A forward–backward trajectory implementation of the OMT method is described that addresses the numerical challenges of applying the OMT to large systems with disparate frequency scales. The OMT is shown to well reproduce line shapes and waiting time dynamics in the pure dephasing limit of weak coupling to an off-resonant bath. The OMT is also shown to describe a case where energy transfer is the predominant source of line broadening. PMID:25275943
A Fast Approach for Time Optimal and Smooth Trajectory Planning of Robot Manipulators
Institute of Scientific and Technical Information of China (English)
Gang Liu∗; Chao Yun
2016-01-01
In this paper, a fast approach to generate time optimal and smooth trajectory has been developed and tested. Minimum time is critical for the productivity in industrial applications. Meanwhile, smooth trajectories based on cubic splines are desirable for their ability to limit vibrations and ensure the continuity of position, velocity and acceleration during the robot movement. The main feature of the approach is a satisfactory solution that can be obtained by a local modification process among each interval between two consecutive via⁃points. An analytical formulation simplifies the approach to smooth trajectory and few iterations are enough to determine the correct values. The approach can be applied in many robot manipulators which require high performance on time and smooth. The simulation and application of the approach on a palletizer robot are performed, and the experimental results provide evidence that the approach can realize the robot manipulators more efficiency and high smooth performance.
Stillwater Hybrid Geo-Solar Power Plant Optimization Analyses
Energy Technology Data Exchange (ETDEWEB)
Wendt, Daniel S.; Mines, Gregory L.; Turchi, Craig S.; Zhu, Guangdong; Cohan, Sander; Angelini, Lorenzo; Bizzarri, Fabrizio; Consoli, Daniele; De Marzo, Alessio
2015-09-02
The Stillwater Power Plant is the first hybrid plant in the world able to bring together a medium-enthalpy geothermal unit with solar thermal and solar photovoltaic systems. Solar field and power plant models have been developed to predict the performance of the Stillwater geothermal / solar-thermal hybrid power plant. The models have been validated using operational data from the Stillwater plant. A preliminary effort to optimize performance of the Stillwater hybrid plant using optical characterization of the solar field has been completed. The Stillwater solar field optical characterization involved measurement of mirror reflectance, mirror slope error, and receiver position error. The measurements indicate that the solar field may generate 9% less energy than the design value if an appropriate tracking offset is not employed. A perfect tracking offset algorithm may be able to boost the solar field performance by about 15%. The validated Stillwater hybrid plant models were used to evaluate hybrid plant operating strategies including turbine IGV position optimization, ACC fan speed and turbine IGV position optimization, turbine inlet entropy control using optimization of multiple process variables, and mixed working fluid substitution. The hybrid plant models predict that each of these operating strategies could increase net power generation relative to the baseline Stillwater hybrid plant operations.
Hybrid systems, optimal control and hybrid vehicles theory, methods and applications
Böhme, Thomas J
2017-01-01
This book assembles new methods showing the automotive engineer for the first time how hybrid vehicle configurations can be modeled as systems with discrete and continuous controls. These hybrid systems describe naturally and compactly the networks of embedded systems which use elements such as integrators, hysteresis, state-machines and logical rules to describe the evolution of continuous and discrete dynamics and arise inevitably when modeling hybrid electric vehicles. They can throw light on systems which may otherwise be too complex or recondite. Hybrid Systems, Optimal Control and Hybrid Vehicles shows the reader how to formulate and solve control problems which satisfy multiple objectives which may be arbitrary and complex with contradictory influences on fuel consumption, emissions and drivability. The text introduces industrial engineers, postgraduates and researchers to the theory of hybrid optimal control problems. A series of novel algorithmic developments provides tools for solving engineering pr...
Genetic algorithm trajectory plan optimization for EAMA: EAST Articulated Maintenance Arm
Energy Technology Data Exchange (ETDEWEB)
Wu, Jing, E-mail: wujing@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, 350 Shushanhu Rd., Hefei, Anhui (China); Lappeenranta University of Technology, Skinnarilankatu 34, Lappeenranta (Finland); Wu, Huapeng [Lappeenranta University of Technology, Skinnarilankatu 34, Lappeenranta (Finland); Song, Yuntao; Cheng, Yong; Zhao, Wenglong [Institute of Plasma Physics, Chinese Academy of Sciences, 350 Shushanhu Rd., Hefei, Anhui (China); Wang, Yongbo [Lappeenranta University of Technology, Skinnarilankatu 34, Lappeenranta (Finland)
2016-11-01
Highlights: • A redundant 10-DOF serial-articulated robot for EAST assembly and maintains is presented. • A trajectory optimization algorithm of the robot is developed. • A minimum jerk objective is presented to suppress machining vibration of the robot. - Abstract: EAMA (EAST Articulated Maintenance Arm) is an articulated serial manipulator with 7 degrees of freedom (DOF) articulated arm followed by 3-DOF gripper, total length is 8.867 m, works in experimental advanced superconductor tokamak (EAST) vacuum vessel (VV) to perform blanket inspection and remote maintenance tasks. This paper presents a trajectory optimization method which aims to pursue the 7-DOF articulated arm a stable movement, which keeps the mounted inspection camera anti-vibration. Based on dynamics analysis, trajectory optimization algorithm adopts multi-order polynomial interpolation in joint space and high order geometry Jacobian transform. The object of optimization algorithm is to suppress end-effector movement vibration by minimizing jerk RMS (root mean square) value. The proposed solution has such characteristics which can satisfy kinematic constraints of EAMA’s motion and ensure the arm running under the absolute values of velocity, acceleration and jerk boundaries. GA (genetic algorithm) is employed to find global and robust solution for this problem.
A Hybrid Aggressive Space Mapping Algorithm for EM Optimization
DEFF Research Database (Denmark)
Bakr, Mohamed H.; Bandler, John W.; Georgieva, N.;
1999-01-01
We propose a novel hybrid aggressive space-mapping (HASM) optimization algorithm. HASM exploits both the trust-region aggressive space-mapping (TRASM) strategy and direct optimization. Severe differences between the coarse and fine models and nonuniqueness of the parameter extraction procedure ma...
Enhanced Fuel-Optimal Trajectory-Generation Algorithm for Planetary Pinpoint Landing
Acikmese, Behcet; Blackmore, James C.; Scharf, Daniel P.
2011-01-01
An enhanced algorithm is developed that builds on a previous innovation of fuel-optimal powered-descent guidance (PDG) for planetary pinpoint landing. The PDG problem is to compute constrained, fuel-optimal trajectories to land a craft at a prescribed target on a planetary surface, starting from a parachute cut-off point and using a throttleable descent engine. The previous innovation showed the minimal-fuel PDG problem can be posed as a convex optimization problem, in particular, as a Second-Order Cone Program, which can be solved to global optimality with deterministic convergence properties, and hence is a candidate for onboard implementation. To increase the speed and robustness of this convex PDG algorithm for possible onboard implementation, the following enhancements are incorporated: 1) Fast detection of infeasibility (i.e., control authority is not sufficient for soft-landing) for subsequent fault response. 2) The use of a piecewise-linear control parameterization, providing smooth solution trajectories and increasing computational efficiency. 3) An enhanced line-search algorithm for optimal time-of-flight, providing quicker convergence and bounding the number of path-planning iterations needed. 4) An additional constraint that analytically guarantees inter-sample satisfaction of glide-slope and non-sub-surface flight constraints, allowing larger discretizations and, hence, faster optimization. 5) Explicit incorporation of Mars rotation rate into the trajectory computation for improved targeting accuracy. These enhancements allow faster convergence to the fuel-optimal solution and, more importantly, remove the need for a "human-in-the-loop," as constraints will be satisfied over the entire path-planning interval independent of step-size (as opposed to just at the discrete time points) and infeasible initial conditions are immediately detected. Finally, while the PDG stage is typically only a few minutes, ignoring the rotation rate of Mars can introduce 10s
Optimization of Renewable Energy Hybrid System for Grid Connected Application
Directory of Open Access Journals (Sweden)
Mustaqimah Mustaqimah
2012-10-01
Full Text Available ABSTRACT. Hybrid energy systems are pollution free, takes low cost and less gestation period, user and social friendly. Such systems are important sources of energy for shops, schools, and clinics in village communities especially in remote areas. Hybrid systems can provide electricity at a comparatively economic price in many remote areas. This paper presents a method to jointly determine the sizing and operation control of hybrid energy systems. The model, PV wind hydro and biomass hybrid system connects to grid. The system configuration of the hybrid is derived based on a theoretical domestic load at a typical location and local solar radiation, wind and water flow rate data and biomass availability. The hybrid energy system is proposed for 10 of teacher’s houses of Industrial Training Institute, Mersing. It is predicted 10 kW load consumption per house. The hybrid energy system consists of wind, solar, biomass, hydro, and grid power. Approximately energy consumption is 860 kWh/day with a 105 kW peak demand load. The proposed hybrid renewable consists of solar photovoltaic (PV panels, wind turbine, hydro turbine and biomass. Battery and inverter are included as part of back-up and storage system. It provides the economic sensitivity of hybridization and the economic and environmental benefits of using a blend of technologies. It also presents the trade off that is involved in optimizing a hybrid energy system to harness and utilize the available renewable energy resources efficiently.
A New Architecture for Extending the Capabilities of the Copernicus Trajectory Optimization Program
Williams, Jacob
2015-01-01
This paper describes a new plugin architecture developed for the Copernicus spacecraft trajectory optimization program. Details of the software architecture design and development are described, as well as examples of how the capability can be used to extend the tool in order to expand the type of trajectory optimization problems that can be solved. The inclusion of plugins is a significant update to Copernicus, allowing user-created algorithms to be incorporated into the tool for the first time. The initial version of the new capability was released to the Copernicus user community with version 4.1 in March 2015, and additional refinements and improvements were included in the recent 4.2 release. It is proving quite useful, enabling Copernicus to solve problems that it was not able to solve before.
Rodionova, Olga; Sridhar, Banavar; Ng, Hok K.
2016-01-01
Air traffic in the North Atlantic oceanic airspace (NAT) experiences very strong winds caused by jet streams. Flying wind-optimal trajectories increases individual flight efficiency, which is advantageous when operating in the NAT. However, as the NAT is highly congested during peak hours, a large number of potential conflicts between flights are detected for the sets of wind-optimal trajectories. Conflict resolution performed at the strategic level of flight planning can significantly reduce the airspace congestion. However, being completed far in advance, strategic planning can only use predicted environmental conditions that may significantly differ from the real conditions experienced further by aircraft. The forecast uncertainties result in uncertainties in conflict prediction, and thus, conflict resolution becomes less efficient. This work considers wind uncertainties in order to improve the robustness of conflict resolution in the NAT. First, the influence of wind uncertainties on conflict prediction is investigated. Then, conflict resolution methods accounting for wind uncertainties are proposed.
Hybrid Algorithm for Optimal Load Sharing in Grid Computing
Directory of Open Access Journals (Sweden)
A. Krishnan
2012-01-01
Full Text Available Problem statement: Grid Computing is the fast growing industry, which shares the resources in the organization in an effective manner. Resource sharing requires more optimized algorithmic structure, otherwise the waiting time and response time are increased and the resource utilization is reduced. Approach: In order to avoid such reduction in the performances of the grid system, an optimal resource sharing algorithm is required. In recent days, many load sharing technique are proposed, which provides feasibility but there are many critical issues are still present in these algorithms. Results: In this study a hybrid algorithm for optimization of load sharing is proposed. The hybrid algorithm contains two components which are Hash Table (HT and Distributed Hash Table (DHT. Conclusion: The results of the proposed study show that the hybrid algorithm will optimize the task than existing systems.
Stochastic Optimal Control for Series Hybrid Electric Vehicles
Energy Technology Data Exchange (ETDEWEB)
Malikopoulos, Andreas [ORNL
2013-01-01
Increasing demand for improving fuel economy and reducing emissions has stimulated significant research and investment in hybrid propulsion systems. In this paper, we address the problem of optimizing online the supervisory control in a series hybrid configuration by modeling its operation as a controlled Markov chain using the average cost criterion. We treat the stochastic optimal control problem as a dual constrained optimization problem. We show that the control policy that yields higher probability distribution to the states with low cost and lower probability distribution to the states with high cost is an optimal control policy, defined as an equilibrium control policy. We demonstrate the effectiveness of the efficiency of the proposed controller in a series hybrid configuration and compare it with a thermostat-type controller.
Dynamic Optimization of Trajectory for Ramp-up Current Profile in Tokamak Plasmas
Ren, Zhigang; Ou, Yongsheng
2016-01-01
In this paper, we consider an open-loop, finite-time, optimal control problem of attaining a specific desired current profile during the ramp-up phase by finding the best open-loop actuator input trajectories. Average density, total power, and plasma current are used as control actuators to manipulate the profile shape in tokamak plasmas. Based on the control parameterization method, we propose a numerical solution procedure directly to solve the original PDE-constrained optimization problem using gradient-based optimization techniques such as sequential quadratic programming (SQP). This paper is aimed at proposing an effective framework for the solution of PDE-constrained optimization problem in tokamak plasmas. A more user-friendly and efficient graphical user interface (GUI) is designed in MATLAB and the numerical simulation results are verified to demonstrate its applicability. In addition, the proposed framework of combining existing PDE and numerical optimization solvers to solve PDE-constrained optimiz...
Optimal RTLS abort trajectories for an HL-20 personnel launch vehicle
Dutton, Kevin
1993-12-01
The primary objective of this study was to determine whether Return To Launch Site (RTLS) abort at T seconds along the launch trajectory of the Personnel Launch System (PLS) is possible using optimal control theory. The secondary objective is to assess effects of bank angle constraint, lift coefficient constraint, free and fixed final boundary conditions, etc. of the vehicle. The PLS is a complementary system to the Space Shuttle.
Trajectory optimization for dynamic couch rotation during volumetric modulated arc radiotherapy
Smyth, Gregory; Bamber, Jeffrey C.; Evans, Philip M.; Bedford, James L.
2013-11-01
Non-coplanar radiation beams are often used in three-dimensional conformal and intensity modulated radiotherapy to reduce dose to organs at risk (OAR) by geometric avoidance. In volumetric modulated arc radiotherapy (VMAT) non-coplanar geometries are generally achieved by applying patient couch rotations to single or multiple full or partial arcs. This paper presents a trajectory optimization method for a non-coplanar technique, dynamic couch rotation during VMAT (DCR-VMAT), which combines ray tracing with a graph search algorithm. Four clinical test cases (partial breast, brain, prostate only, and prostate and pelvic nodes) were used to evaluate the potential OAR sparing for trajectory-optimized DCR-VMAT plans, compared with standard coplanar VMAT. In each case, ray tracing was performed and a cost map reflecting the number of OAR voxels intersected for each potential source position was generated. The least-cost path through the cost map, corresponding to an optimal DCR-VMAT trajectory, was determined using Dijkstra’s algorithm. Results show that trajectory optimization can reduce dose to specified OARs for plans otherwise comparable to conventional coplanar VMAT techniques. For the partial breast case, the mean heart dose was reduced by 53%. In the brain case, the maximum lens doses were reduced by 61% (left) and 77% (right) and the globes by 37% (left) and 40% (right). Bowel mean dose was reduced by 15% in the prostate only case. For the prostate and pelvic nodes case, the bowel V50 Gy and V60 Gy were reduced by 9% and 45% respectively. Future work will involve further development of the algorithm and assessment of its performance over a larger number of cases in site-specific cohorts.
Zhao, Shi-Zheng; Suganthan, Ponnuthurai Nagaratnam; Das, Swagatam
In order to solve large scale continuous optimization problems, Self-adaptive DE (SaDE) is enhanced by incorporating the JADE mutation strategy and hybridized with modified multi-trajectory search (MMTS) algorithm (SaDE-MMTS). The JADE mutation strategy, the "DE/current-to-pbest" which is a variation of the classic "DE/current-to-best", is used for generating mutant vectors. After the mutation phase, the binomial (uniform) crossover, the exponential crossover as well as no crossover option are used to generate each pair of target and trial vectors. By utilizing the self-adaptation in SaDE, both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, suitable offspring generation strategy along with associated parameter settings will be determined adaptively to match different phases of the search process. MMTS is applied frequently to refine several diversely distributed solutions at different search stages satisfying both the global and the local search requirement. The initialization of step sizes is also defined by a self-adaption during every MMTS step. The success rates of both SaDE and the MMTS are determined and compared, consequently, future function evaluations for both search algorithms are assigned proportionally to their recent past performance. The proposed SaDE-MMTS is employed to solve the 20 numerical optimization problems for the CEC'2010 Special Session and Competition on Large Scale Global Optimization and competitive results are presented.
Galloping Trajectory Generation of a Legged Transport Robot Based on Energy Consumption Optimization
Directory of Open Access Journals (Sweden)
Yaguang Zhu
2016-01-01
Full Text Available Legged walking robots have very strong operation ability in the complex surface and they are very suitable for transportation of tools, materials, and equipment in unstructured environment. Aiming at the problems of energy consumption of legged transport robot during the fast moving, a method of galloping trajectory planning based on energy consumption optimization is proposed. By establishing transition angle polynomials of flight phase, lift-off phase, and stance phase and constraint condition between each state phase, the locomotion equations of the ellipse trajectory are derived. The transition angle of each state phase is introduced into the system energy consumption equations, and the energy optimization index based on transition angles is established. Inverse kinematics solution and trajectory planning in one gait cycle are applied to genetic algorithm process to solve the nonlinear programming problem. The results show that the optimized distribution of transition angles of state phases is more reasonable, and joint torques and system energy consumption are reduced effectively. Thus, the method mentioned above has a great significance to realize fast operation outdoors of transport robot.
Directory of Open Access Journals (Sweden)
Ren Ziwu
2016-04-01
Full Text Available A humanoid manipulator produces significantly reactive forces against a humanoid body when it operates in a rapid and continuous reaction environment (e.g., playing baseball, ping-pong etc.. This not only disturbs the balance and stability of the humanoid robot, but also influences its operation precision. To solve this problem, a novel approach, which is able to generate a minimum-acceleration and continuous acceleration trajectory for the humanoid manipulator, is presented in this paper. By this method, the whole trajectory of humanoid manipulation is divided into two processes, i.e., the operation process and the return process. Moreover, the target operation point is considered as a particular point that should be passed through. As such, the trajectory of each process is described through a quartic polynomial in the joint space, after which the trajectory planning problem for the humanoid manipulator can be formulated as a global constrained optimization problem. In order to alleviate the reactive force, a fitness function that aims to minimize the maximum acceleration of each joint of the manipulator is defined, while differential evolution is employed to determine the joint accelerations of the target operation point. Thus, a trajectory with a minimum-acceleration and continuous acceleration profile is obtained, which can reduce the effect on the body and be favourable for the balance and stability of the humanoid robot to a certain extent. Finally, a humanoid robot with a 7-DOF manipulator for ping-pong playing is employed as an example. Simulation experiment results show the effectiveness of this method for the trajectory planning problem being studied.
Statistical analysis of piloted simulation of real time trajectory optimization algorithms
Price, D. B.
1982-01-01
A simulation of time-optimal intercept algorithms for on-board computation of control commands is described. The effects of three different display modes and two different computation modes on the pilots' ability to intercept a moving target in minimum time were tested. Both computation modes employed singular perturbation theory to help simplify the two-point boundary value problem associated with trajectory optimization. Target intercept time was affected by both the display and computation modes chosen, but the display mode chosen was the only significant influence on the miss distance.
Optimal Control for a Parallel Hybrid Hydraulic Excavator Using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Dong-yun Wang
2013-01-01
Full Text Available Optimal control using particle swarm optimization (PSO is put forward in a parallel hybrid hydraulic excavator (PHHE. A power-train mathematical model of PHHE is illustrated along with the analysis of components’ parameters. Then, the optimal control problem is addressed, and PSO algorithm is introduced to deal with this nonlinear optimal problem which contains lots of inequality/equality constraints. Then, the comparisons between the optimal control and rule-based one are made, and the results show that hybrids with the optimal control would increase fuel economy. Although PSO algorithm is off-line optimization, still it would bring performance benchmark for PHHE and also help have a deep insight into hybrid excavators.
Yang, Chenguang; Li, Zhijun; Li, Jing
2013-02-01
In this paper, we investigate optimized adaptive control and trajectory generation for a class of wheeled inverted pendulum (WIP) models of vehicle systems. Aiming at shaping the controlled vehicle dynamics to be of minimized motion tracking errors as well as angular accelerations, we employ the linear quadratic regulation optimization technique to obtain an optimal reference model. Adaptive control has then been developed using variable structure method to ensure the reference model to be exactly matched in a finite-time horizon, even in the presence of various internal and external uncertainties. The minimized yaw and tilt angular accelerations help to enhance the vehicle rider's comfort. In addition, due to the underactuated mechanism of WIP, the vehicle forward velocity dynamics cannot be controlled separately from the pendulum tilt angle dynamics. Inspired by the control strategy of human drivers, who usually manipulate the tilt angle to control the forward velocity, we design a neural-network-based adaptive generator of implicit control trajectory (AGICT) of the tilt angle which indirectly "controls" the forward velocity such that it tracks the desired velocity asymptotically. The stability and optimal tracking performance have been rigorously established by theoretic analysis. In addition, simulation studies have been carried out to demonstrate the efficiency of the developed AGICT and optimized adaptive controller.
Optimal trajectories for an aerospace plane. Part 2: Data, tables, and graphs
Miele, Angelo; Lee, W. Y.; Wu, G. D.
1990-01-01
Data, tables, and graphs relative to the optimal trajectories for an aerospace plane are presented. A single-stage-to-orbit (SSTO) configuration is considered, and the transition from low supersonic speeds to orbital speeds is studied for a single aerodynamic model (GHAME) and three engine models. Four optimization problems are solved using the sequential gradient-restoration algorithm for optimal control problems: (1) minimization of the weight of fuel consumed; (2) minimization of the peak dynamic pressure; (3) minimization of the peak heating rate; and (4) minimization of the peak tangential acceleration. The above optimization studies are carried out for different combinations of constraints, specifically: initial path inclination that is either free or given; dynamic pressure that is either free or bounded; and tangential acceleration that is either free or bounded.
Institute of Scientific and Technical Information of China (English)
LIAO Yihuan; LI Daokui; TANG Guojin
2011-01-01
This paper is concerned with optimal motion planning for vibration reducing of flee-floating flexible redundant manipulators.Firstly,dynamic model of the system is established based on Lagrange method,and the motion planning model for vibration reducing is proposed.Secondly,a hybrid optimization approach employing Gauss pseudospectral method(GPM) and direct shooting method(DSM),is proposed to solve the motion planning problem.In this approach,the motion planning problem is transformed into a non-linear parameter optimization problem using GPM,and genetic algorithm(GA) is employed to locate the approximate solution.Subsequently,an optimization model is formulated based on DSM,and sequential quadratic programming (SQP) algorithm is used to obtain the accurate solution,with the approximate solution as an initial reference solution.Finally,several numerical simulations are investigated,and the global vibration or residual vibration of flexible link is obviously reduced by the joint trajectory which is obtained by the hybrid optimization approach.The numerical simulation results indicate that the approach is effective and stable to the motion planning problem of vibration reducing.
Military Hybrid Vehicle Optimization and Control
2012-08-14
and energy security, as well as reduce overall energy u ’C the concept of a microgrid has been introduced [7 ]. A microgricl is defined as an...vehicle-to-grid (V2G) technology has been show to have the ability to upport the microgrid ru a source, but also a storage device for excess energy [9...understood. The scope of this proposal includes introducing the concept of regarding a military hybrid vehicle as a microgrid and utilizing battery state
A Hybrid Algorithm for Optimizing Multi- Modal Functions
Institute of Scientific and Technical Information of China (English)
Li Qinghua; Yang Shida; Ruan Youlin
2006-01-01
A new genetic algorithm is presented based on the musical performance. The novelty of this algorithm is that a new genetic algorithm, mimicking the musical process of searching for a perfect state of harmony, which increases the robustness of it greatly and gives a new meaning of it in the meantime, has been developed. Combining the advantages of the new genetic algorithm, simplex algorithm and tabu search, a hybrid algorithm is proposed. In order to verify the effectiveness of the hybrid algorithm, it is applied to solving some typical numerical function optimization problems which are poorly solved by traditional genetic algorithms. The experimental results show that the hybrid algorithm is fast and reliable.
MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm
Directory of Open Access Journals (Sweden)
Ahmed M.E. Khalil
2015-06-01
Full Text Available The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA and the Krill Herd Algorithm (KHA. The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems.
Optimal Acceleration-Velocity-Bounded Trajectory Planning in Dynamic Crowd Simulation
Directory of Open Access Journals (Sweden)
Fu Yue-wen
2014-01-01
Full Text Available Creating complex and realistic crowd behaviors, such as pedestrian navigation behavior with dynamic obstacles, is a difficult and time consuming task. In this paper, we study one special type of crowd which is composed of urgent individuals, normal individuals, and normal groups. We use three steps to construct the crowd simulation in dynamic environment. The first one is that the urgent individuals move forward along a given path around dynamic obstacles and other crowd members. An optimal acceleration-velocity-bounded trajectory planning method is utilized to model their behaviors, which ensures that the durations of the generated trajectories are minimal and the urgent individuals are collision-free with dynamic obstacles (e.g., dynamic vehicles. In the second step, a pushing model is adopted to simulate the interactions between urgent members and normal ones, which ensures that the computational cost of the optimal trajectory planning is acceptable. The third step is obligated to imitate the interactions among normal members using collision avoidance behavior and flocking behavior. Various simulation results demonstrate that these three steps give realistic crowd phenomenon just like the real world.
GPAW optimized for Blue Gene/P using hybrid programming
DEFF Research Database (Denmark)
Kristensen, Mads Ruben Burgdorff; Happe, Hans Henrik; Vinter, Brian
2009-01-01
In this work we present optimizations of a Grid-based projector-augmented wave method software, GPAW for the Blue Gene/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming. The work focuses on optimi......In this work we present optimizations of a Grid-based projector-augmented wave method software, GPAW for the Blue Gene/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming. The work focuses...
Trajectory optimization and guidance law development for national aerospace plane applications
Calise, A. J.; Corban, J. E.; Flandro, G. A.
1988-01-01
The problem of onboard trajectory optimization for an airbreathing, single-stage-to-orbit vehicle is examined. A simple model representative of the aerospace plane concept, including a dual-mode propulsion system composed of scramjet and rocket engines, is presented. Consideration is restricted to hypersonic flight within the atmosphere. An energy state approximation is used in a four-state model for flight of a point mass in a vertical plane. Trajectory constraints, including those of dynamic pressure and aerodynamic heating, are initially ignored. Singular perturbation methods are applied in solving the optimal control problem of minimum fuel climb. The resulting reduced solution for the energy state dynamics provides an optimal altitude profile dependent on energy level and control for rocket thrust. A boundary-layer analysis produces an approximate lift control solution in feedback form and accounts for altitude and flight path angle dynamics. The reduced solution optimal climb path is presented for the unconstrained case and the case for which a maximum dynamic pressure constraint is enforced.
Modeling of Human Arm Energy Expenditure for Predicting Energy Optimal Trajectories
Directory of Open Access Journals (Sweden)
L. Zhou
2011-07-01
Full Text Available Human arm motion can inspire the trajectory planning of anthropomorphic robotic arms to achieve energy-efficient movements. An approach for predicting metabolic cost in the planar human arm motion by means of the biomechanical simulation is proposed in this work. Two biomechanical models, including an analytical model and a musculoskeletal model, are developed to implement the proposed approach. The analytical model is developed by modifying a human muscle expenditure model, in which the muscles are grouped as torque providers for computation efficiency. In the musculoskeletal model, the predication of metabolic cost is conducted on the basis of individual muscles. With the proposed approach, metabolic costs for parameterized target-reaching arm motions are calculated and utilized to identify optimal arm trajectories.
Speck, Thomas; Engel, Andreas; Seifert, Udo
2012-12-01
We study the large deviation function for the entropy production rate in two driven one-dimensional systems: the asymmetric random walk on a discrete lattice and Brownian motion in a continuous periodic potential. We compare two approaches: using the Donsker-Varadhan theory and using the Freidlin-Wentzell theory. We show that the wings of the large deviation function are dominated by a single optimal trajectory: either in the forward direction (positive rate) or in the backward direction (negative rate). The joining of the two branches at zero entropy production implies a non-differentiability and thus the appearance of a ‘kink’. However, around zero entropy production, many trajectories contribute and thus the ‘kink’ is smeared out.
Wu, Hao; Rosta, Edina; Noé, Frank
2014-01-01
We propose a discrete transition-based reweighting analysis method (dTRAM) for analyzing configuration-space-discretized simulation trajectories produced at different thermodynamic states (temperatures, Hamiltonians, etc.) dTRAM provides maximum-likelihood estimates of stationary quantities (probabilities, free energies, expectation values) at any thermodynamic state. In contrast to the weighted histogram analysis method (WHAM), dTRAM does not require data to be sampled from global equilibrium, and can thus produce superior estimates for enhanced sampling data such as parallel/simulated tempering, replica exchange, umbrella sampling, or metadynamics. In addition, dTRAM provides optimal estimates of Markov state models (MSMs) from the discretized state-space trajectories at all thermodynamic states. Under suitable conditions, these MSMs can be used to calculate kinetic quantities (e.g. rates, timescales). In the limit of a single thermodynamic state, dTRAM estimates a maximum likelihood reversible MSM, while i...
Optimization strategy for element sizing in hybrid power systems
del Real, Alejandro J.; Arce, Alicia; Bordons, Carlos
This paper presents a procedure to evaluate the optimal element sizing of hybrid power systems. In order to generalize the problem, this work exploits the "energy hub" formulation previously presented in the literature, defining an energy hub as an interface among energy producers, consumers and the transportation infrastructure. The resulting optimization minimizes an objective function which is based on costs and efficiencies of the system elements, while taking into account the hub model, energy and power constraints and estimated operational conditions, such as energy prices, input power flow availability and output energy demand. The resulting optimal architecture also constitutes a framework for further real-time control designs. Moreover, an example of a hybrid storage system is considered. In particular, the architecture of a hybrid plant incorporating a wind generator, batteries and intermediate hydrogen storage is optimized, based on real wind data and averaged residential demands, also taking into account possible estimation errors. The hydrogen system integrates an electrolyzer, a fuel cell stack and hydrogen tanks. The resulting optimal cost of such hybrid power plant is compared with the equivalent hydrogen-only and battery-only systems, showing improvements in investment costs of almost 30% in the worst case.
Optimization strategy for element sizing in hybrid power systems
Energy Technology Data Exchange (ETDEWEB)
del Real, Alejandro J.; Arce, Alicia; Bordons, Carlos [Departamento de Ingenieria de Sistemas y Automatica, Universidad de Sevilla, 41092 Sevilla (Spain)
2009-08-01
This paper presents a procedure to evaluate the optimal element sizing of hybrid power systems. In order to generalize the problem, this work exploits the ''energy hub'' formulation previously presented in the literature, defining an energy hub as an interface among energy producers, consumers and the transportation infrastructure. The resulting optimization minimizes an objective function which is based on costs and efficiencies of the system elements, while taking into account the hub model, energy and power constraints and estimated operational conditions, such as energy prices, input power flow availability and output energy demand. The resulting optimal architecture also constitutes a framework for further real-time control designs. Moreover, an example of a hybrid storage system is considered. In particular, the architecture of a hybrid plant incorporating a wind generator, batteries and intermediate hydrogen storage is optimized, based on real wind data and averaged residential demands, also taking into account possible estimation errors. The hydrogen system integrates an electrolyzer, a fuel cell stack and hydrogen tanks. The resulting optimal cost of such hybrid power plant is compared with the equivalent hydrogen-only and battery-only systems, showing improvements in investment costs of almost 30% in the worst case. (author)
An Effective Hybrid Optimization Algorithm for Capacitated Vehicle Routing Problem
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Capacitated vehicle routing problem (CVRP) is an important combinatorial optimization problem. However, it is quite difficult to achieve an optimal solution with the traditional optimization methods owing to the high computational complexity. A hybrid algorithm was developed to solve the problem, in which an artificial immune clonal algorithm (AICA) makes use of the global search ability to search the optimal results and simulated annealing (SA) algorithm employs certain probability to avoid becoming trapped in a local optimum. The results obtained from the computational study show that the proposed algorithm is a feasible and effective method for capacitated vehicle routing problem.
OPTIMIZED GATEWAY DISCOVERY IN HYBRID MANETS
Directory of Open Access Journals (Sweden)
A.Triviño
2009-10-01
Full Text Available Mobile users are expected to demand access to the Internet anywhere and anytime. In a MANET context,a device which is about to connect to external hosts needs the route to the element which communicatesthe MANET with the Internet. This element is the Internet Gateway. To inform about its presence as wellas about some configuration parameters, the Gateway sends MRA messages. In a similar way to ad hocrouting protocols, the Gateway can generate the messages on demand (reactively, periodically(proactively or combining both previous strategies in a hybrid gateway discovery. Specifically, in thehybrid gateway discovery, the Gateway periodically sends the MRA messages in a restricted area. Thenodes that are outside this area demand the Gateway information reactively. This gateway discoveryrequires the setting of the number of hops that define the proactive area, also called the TTL value.Network performance can be improved when the Gateway uses information such as the position of thesources to adjust the TTL value. In this paper, we transfer the decision about the dimensions of theproactive zone to the mobile nodes so more network conditions are taken into account. Simulation resultsshow that the proposed gateway discovery outperforms other hybrid gateway discovery schemes.
Combined Optimal Sizing and Control for a Hybrid Tracked Vehicle
Directory of Open Access Journals (Sweden)
Huei Peng
2012-11-01
Full Text Available The optimal sizing and control of a hybrid tracked vehicle is presented and solved in this paper. A driving schedule obtained from field tests is used to represent typical tracked vehicle operations. Dynamics of the diesel engine-permanent magnetic AC synchronous generator set, the lithium-ion battery pack, and the power split between them are modeled and validated through experiments. Two coupled optimizations, one for the plant parameters, forming the outer optimization loop and one for the control strategy, forming the inner optimization loop, are used to achieve minimum fuel consumption under the selected driving schedule. The dynamic programming technique is applied to find the optimal controller in the inner loop while the component parameters are optimized iteratively in the outer loop. The results are analyzed, and the relationship between the key parameters is observed to keep the optimal sizing and control simultaneously.
Cost Optimization Using Hybrid Evolutionary Algorithm in Cloud Computing
Directory of Open Access Journals (Sweden)
B. Kavitha
2015-07-01
Full Text Available The main aim of this research is to design the hybrid evolutionary algorithm for minimizing multiple problems of dynamic resource allocation in cloud computing. The resource allocation is one of the big problems in the distributed systems when the client wants to decrease the cost for the resource allocation for their task. In order to assign the resource for the task, the client must consider the monetary cost and computational cost. Allocation of resources by considering those two costs is difficult. To solve this problem in this study, we make the main task of client into many subtasks and we allocate resources for each subtask instead of selecting the single resource for the main task. The allocation of resources for the each subtask is completed through our proposed hybrid optimization algorithm. Here, we hybrid the Binary Particle Swarm Optimization (BPSO and Binary Cuckoo Search algorithm (BCSO by considering monetary cost and computational cost which helps to minimize the cost of the client. Finally, the experimentation is carried out and our proposed hybrid algorithm is compared with BPSO and BCSO algorithms. Also we proved the efficiency of our proposed hybrid optimization algorithm.
Operations Optimization of Hybrid Energy Systems under Variable Markets
Energy Technology Data Exchange (ETDEWEB)
Chen, Jun; Garcia, Humberto E.
2016-07-01
Hybrid energy systems (HES) have been proposed to be an important element to enable increasing penetration of clean energy. This paper investigates the operations flexibility of HES, and develops a methodology for operations optimization to maximize its economic value based on predicted renewable generation and market information. The proposed operations optimizer allows systematic control of energy conversion for maximal economic value, and is illustrated by numerical results.
Optimization and Analysis on Trajectory with Multiple Constraints for Hypersonic Air-vehicle
Directory of Open Access Journals (Sweden)
Wei Changzhu
2015-01-01
Full Text Available The trajectory optimization technology is one of the key technologies for hypersonic air-vehicle. There are multiple constraints in the process of hypersonic flight, such as uncertainty of flight environment, thermal current, dynamic pressure and overload. The trajectory optimization of hypersonic air-vehicle is facing with a great challenge. This article studies the direct shooting method, the Gauss pseudo spectral method and sequential gradient-restoration algorithm, among which the direct shooting method simply makes the control variables discrete in the time domain, and obtains the status value by explicit numerical integration; Gauss pseudo spectral method makes the status variable and control variable discrete in a series of Gauss points, and constructs multinomial to approximate to the status and control variable by taking the discrete points as the nodes; sequential gradient-restoration algorithm uses iteration to meet the constraints and minimize the increment of initial value of control and status variable in order to constantly approximate to the optimal solution on condition that the constraints meet first order approximation. Finally this article conducts a numerical simulation by taking the diving segment of hypersonic air-vehicle as an example for comparative analysis on those three algorithms respectively from, such as, the initial value selection, constraint handling, convergence speed and calculation accuracy. The simulation result indicates Gauss pseudo spectral method is a method with fairly good comprehensive performance.
Institute of Scientific and Technical Information of China (English)
Lu Wang,Qinghua Xing,; Yifan Mao
2015-01-01
To rapidly generate a reentry trajectory for hyper-sonic vehicle satisfying waypoint and no-fly zone constraints, a novel optimization method, which combines the improved parti-cle swarm optimization (PSO) algorithm with the improved Gauss pseudospectral method (GPM), is proposed. The improved PSO algorithm is used to generate a good initial value in a short time, and the mission of the improved GPM is to find the final solution with a high precision. In the improved PSO algorithm, by control-ling the entropy of the swarm in each dimension, the typical PSO algorithm’s weakness of being easy to fal into a local optimum can be overcome. In the improved GPM, two kinds of breaks are introduced to divide the trajectory into multiple segments, and the distribution of the Legendre-Gauss (LG) nodes can be altered, so that al the constraints can be satisfied strictly. Thereby the advan-tages of both the intel igent optimization algorithm and the direct method are combined. Simulation results demonstrate that the proposed method is insensitive to initial values, and it has more rapid convergence and higher precision than traditional ones.
Reuschel, Johanna; Rösler, Frank; Henriques, Denise Y P; Fiehler, Katja
2011-04-01
Many studies provide evidence that information from different modalities is integrated following the maximum likelihood estimation model (MLE). For instance, we recently found that visual and proprioceptive path trajectories are optimally combined (Reuschel et al. in Exp Brain Res 201:853-862, 2010). However, other studies have failed to reveal optimal integration of such dynamic information. In the present study, we aim to generalize our previous findings to different parts of the workspace (central, ipsilateral, or contralateral) and to different types of judgments (relative vs. absolute). Participants made relative judgments by judging whether an angular path was acute or obtuse, or they made absolute judgments by judging whether a one-segmented straight path was directed to left or right. Trajectories were presented in the visual, proprioceptive, or combined visual-proprioceptive modality. We measured the bias and the variance of these estimates and predicted both parameters using the MLE. In accordance with the MLE model, participants linearly combined and weighted the unimodal angular path information by their reliabilities irrespective of the side of workspace. However, the precision of bimodal estimates was not greater than that for unimodal estimates, which is inconsistent with the MLE. For the absolute judgment task, participants' estimates were highly accurate and did not differ across modalities. Thus, we were unable to test whether the bimodal percept resulted as a weighted average of the visual and proprioceptive input. Additionally, participants were not more precise in the bimodal compared with the unimodal conditions, which is inconsistent with the MLE. Current findings suggest that optimal integration of visual and proprioceptive information of path trajectory only applies in some conditions.
Non-binary Hybrid LDPC Codes: Structure, Decoding and Optimization
Sassatelli, Lucile
2007-01-01
In this paper, we propose to study and optimize a very general class of LDPC codes whose variable nodes belong to finite sets with different orders. We named this class of codes Hybrid LDPC codes. Although efficient optimization techniques exist for binary LDPC codes and more recently for non-binary LDPC codes, they both exhibit drawbacks due to different reasons. Our goal is to capitalize on the advantages of both families by building codes with binary (or small finite set order) and non-binary parts in their factor graph representation. The class of Hybrid LDPC codes is obviously larger than existing types of codes, which gives more degrees of freedom to find good codes where the existing codes show their limits. We give two examples where hybrid LDPC codes show their interest.
Hybrid Optimization in the Design of Reciprocal Structures
DEFF Research Database (Denmark)
Parigi, Dario; Kirkegaard, Poul Henning; Sassone, Mario
2012-01-01
The paper presents a method to generate the geometry of reciprocal structures by means of a hybrid optimization procedure. The geometry of reciprocal structures where elements are sitting on the top or in the bottom of each other is extremely difficult to predict because of the non...... is then applied to a recent example of free-form reciprocal structure....
Study of optimal control problems for hybrid dynamical systems
Institute of Scientific and Technical Information of China (English)
Gao Rui; Wang Lei; Wang Yuzhen
2006-01-01
From the viewpoint of continuous systems, optimal control problem is proposed for a class of controlled Hybrid dynamical systems. Then a mathematical method- HDS minimum principle is put forward, which can solve the above problem. The HDS minimum principle is proved by means of Ekeland's variational principle.
Multi-Objective Optimization of Spacecraft Trajectories for Small-Body Coverage Missions
Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren
2017-01-01
Visual coverage of surface elements of a small-body object requires multiple images to be taken that meet many requirements on their viewing angles, illumination angles, times of day, and combinations thereof. Designing trajectories capable of maximizing total possible coverage may not be useful since the image target sequence and the feasibility of said sequence given the rotation-rate limitations of the spacecraft are not taken into account. This work presents a means of optimizing, in a multi-objective manner, surface target sequences that account for such limitations.
A novel trajectory prediction control for proximate time-optimal digital control DC—DC converters
Qing, Wang; Ning, Chen; Shen, Xu; Weifeng, Sun; Longxing, Shi
2014-09-01
The purpose of this paper is to present a novel trajectory prediction method for proximate time-optimal digital control DC—DC converters. The control method provides pre-estimations of the duty ratio in the next several switching cycles, so as to compensate the computational time delay of the control loop and increase the control loop bandwidth, thereby improving the response speed. The experiment results show that the fastest transient response time of the digital DC—DC with the proposed prediction is about 8 μs when the load current changes from 0.6 to 0.1 A.
Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Contraints
Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren
2015-01-01
Interplanetary missions are often subject to difficult constraints, like solar phase angle upon arrival at the destination, velocity at arrival, and altitudes for flybys. Preliminary design of such missions is often conducted by solving the unconstrained problem and then filtering away solutions which do not naturally satisfy the constraints. However this can bias the search into non-advantageous regions of the solution space, so it can be better to conduct preliminary design with the full set of constraints imposed. In this work two stochastic global search methods are developed which are well suited to the constrained global interplanetary trajectory optimization problem.
Mann, F. I.; Horsewood, J. L.
1974-01-01
A performance-analysis computer program, that was developed explicitly to generate optimum electric propulsion trajectory data for missions of interest in the exploration of the solar system is presented. The program was primarily designed to evaluate the performance capabilities of electric propulsion systems, and in the simulation of a wide variety of interplanetary missions. A numerical integration of the two-body, three-dimensional equations of motion and the Euler-Lagrange equations was used in the program. Transversality conditions which permit the rapid generation of converged maximum-payload trajectory data, and the optimization of numerous other performance indices for which no transversality conditions exist are included. The ability to simulate constrained optimum solutions, including trajectories having specified propulsion time and constant thrust cone angle, is also in the program. The program was designed to handle multiple-target missions with various types of encounters, such as rendezvous, stopover, orbital capture, and flyby. Performance requirements for a variety of launch vehicles can be determined.
A Hybrid Mutation Chemical Reaction Optimization Algorithm for Global Numerical Optimization
Directory of Open Access Journals (Sweden)
Ransikarn Ngambusabongsopa
2015-01-01
Full Text Available This paper proposes a hybrid metaheuristic approach that improves global numerical optimization by increasing optimal quality and accelerating convergence. This algorithm involves a recently developed process for chemical reaction optimization and two adjustment operators (turning and mutation operators. Three types of mutation operators (uniform, nonuniform, and polynomial were combined with chemical reaction optimization and turning operator to find the most appropriate framework. The best solution among these three options was selected to be a hybrid mutation chemical reaction optimization algorithm for global numerical optimization. The optimal quality, convergence speed, and statistical hypothesis testing of our algorithm are superior to those previous high performance algorithms such as RCCRO, HP-CRO2, and OCRO.
An Optimization Framework for Dynamic Hybrid Energy Systems
Energy Technology Data Exchange (ETDEWEB)
Wenbo Du; Humberto E Garcia; Christiaan J.J. Paredis
2014-03-01
A computational framework for the efficient analysis and optimization of dynamic hybrid energy systems (HES) is developed. A microgrid system with multiple inputs and multiple outputs (MIMO) is modeled using the Modelica language in the Dymola environment. The optimization loop is implemented in MATLAB, with the FMI Toolbox serving as the interface between the computational platforms. Two characteristic optimization problems are selected to demonstrate the methodology and gain insight into the system performance. The first is an unconstrained optimization problem that optimizes the dynamic properties of the battery, reactor and generator to minimize variability in the HES. The second problem takes operating and capital costs into consideration by imposing linear and nonlinear constraints on the design variables. The preliminary optimization results obtained in this study provide an essential step towards the development of a comprehensive framework for designing HES.
Developing Optimized Trajectories Derived from Mission and Thermo-Structural Constraints
Lear, Matthew H.; McGrath, Brian E.; Anderson, Michael P.; Green, Peter W.
2008-01-01
In conjunction with NASA and the Department of Defense, the Johns Hopkins University Applied Physics Laboratory (JHU/APL) has been investigating analytical techniques to address many of the fundamental issues associated with solar exploration spacecraft and high-speed atmospheric vehicle systems. These issues include: thermo-structural response including the effects of thermal management via the use of surface optical properties for high-temperature composite structures; aerodynamics with the effects of non-equilibrium chemistry and gas radiation; and aero-thermodynamics with the effects of material ablation for a wide range of thermal protection system (TPS) materials. The need exists to integrate these discrete tools into a common framework that enables the investigation of interdisciplinary interactions (including analysis tool, applied load, and environment uncertainties) to provide high fidelity solutions. In addition to developing robust tools for the coupling of aerodynamically induced thermal and mechanical loads, JHU/APL has been studying the optimal design of high-speed vehicles as a function of their trajectory. Under traditional design methodology the optimization of system level mission parameters such as range and time of flight is performed independently of the optimization for thermal and mechanical constraints such as stress and temperature. A truly optimal trajectory should optimize over the entire range of mission and thermo-mechanical constraints. Under this research, a framework for the robust analysis of high-speed spacecraft and atmospheric vehicle systems has been developed. It has been built around a generic, loosely coupled framework such that a variety of readily available analysis tools can be used. The methodology immediately addresses many of the current analysis inadequacies and allows for future extension in order to handle more complex problems.
Hybrid intelligent optimization methods for engineering problems
Pehlivanoglu, Yasin Volkan
The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution methodologies; therefore, it is difficult to construct patterns. Also mathematical modeling of a natural phenomenon is almost based on differentials. Differential equations are constructed with relative increments among the factors related to yield. Therefore, the gradients of these increments are essential to search the yield space. However, the landscape of yield is not a simple one and mostly multi-modal. Another issue is differentiability. Engineering design problems are usually nonlinear and they sometimes exhibit discontinuous derivatives for the objective and constraint functions. Due to these difficulties, non-gradient-based algorithms have become more popular in recent decades. Genetic algorithms (GA) and particle swarm optimization (PSO) algorithms are popular, non-gradient based algorithms. Both are population-based search algorithms and have multiple points for initiation. A significant difference from a gradient-based method is the nature of the search methodologies. For example, randomness is essential for the search in GA or PSO. Hence, they are also called stochastic optimization methods. These algorithms are simple, robust, and have high fidelity. However, they suffer from similar defects, such as, premature convergence, less accuracy, or large computational time. The premature convergence is sometimes inevitable due to the lack of diversity. As the generations of particles or individuals in the population evolve, they may lose their diversity and become similar to each other. To overcome this issue, we studied the diversity concept in GA and PSO algorithms. Diversity is essential for a healthy search, and mutations are the basic operators to provide the necessary variety within a population. After having a close scrutiny of the diversity concept based on qualification and
PEMFC Optimization Strategy with Auxiliary Power Source in Fuel Cell Hybrid Vehicle
Directory of Open Access Journals (Sweden)
Tinton Dwi Atmaja
2012-02-01
Full Text Available Page HeaderOpen Journal SystemsJournal HelpUser You are logged in as...aulia My Journals My Profile Log Out Log Out as UserNotifications View (27 new ManageJournal Content SearchBrowse By Issue By Author By Title Other JournalsFont SizeMake font size smaller Make font size default Make font size largerInformation For Readers For Authors For LibrariansKeywords CBPNN Displacement FLC LQG/LTR Mixed PMA Ventilation bottom shear stress direct multiple shooting effective fuzzy logic geoelectrical method hourly irregular wave missile trajectory panoramic image predator-prey systems seawater intrusion segmentation structure development pattern terminal bunt manoeuvre Home About User Home Search Current Archives ##Editorial Board##Home > Vol 23, No 1 (2012 > AtmajaPEMFC Optimization Strategy with Auxiliary Power Source in Fuel Cell Hybrid VehicleTinton Dwi Atmaja, Amin AminAbstractone of the present-day implementation of fuel cell is acting as main power source in Fuel Cell Hybrid Vehicle (FCHV. This paper proposes some strategies to optimize the performance of Polymer Electrolyte Membrane Fuel Cell (PEMFC implanted with auxiliary power source to construct a proper FCHV hybridization. The strategies consist of the most updated optimization method determined from three point of view i.e. Energy Storage System (ESS, hybridization topology and control system analysis. The goal of these strategies is to achieve an optimum hybridization with long lifetime, low cost, high efficiency, and hydrogen consumption rate improvement. The energy storage system strategy considers battery, supercapacitor, and high-speed flywheel as the most promising alternative auxiliary power source. The hybridization topology strategy analyzes the using of multiple storage devices injected with electronic components to bear a higher fuel economy and cost saving. The control system strategy employs nonlinear control system to optimize the ripple factor of the voltage and the current
Directory of Open Access Journals (Sweden)
Michael A. Hurni
2015-12-01
Full Text Available The authors develop an approach to a “best” time path for Autonomous Underwater Vehicles conducting oceanographic measurements under uncertain current flows. The numerical optimization tool DIDO is used to compute hybrid minimum time and optimal survey paths for a sample of currents between ebb and flow. A simulated meta-experiment is performed where the vehicle traverses the resulting paths under different current strengths per run. The fastest elapsed time emerges from a payoff table. A multi-objective function is then used to weigh the time to complete a mission versus measurement inaccuracy due to deviation from the desired survey path.
Optimal Power Scheduling for an Islanded Hybrid Microgrid
DEFF Research Database (Denmark)
Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Savaghebi, Mehdi
2016-01-01
A microgrid is a system that integrates energy generation, energy storage, and loads and it is able to operate either in interconnected or islanded mode. Energy resources should be scheduled to supply the load properly in order to coordinate optimally the power exchange within the microgrid...... according to a defined objective function. In this paper, an optimal power scheduling for generation and demand side is presented to manage an islanded hybrid PV-wind-battery microgrid implemented in Shanghai-China. The optimization is addressed through a Mixed-Integer Linear Programming (MILP) mathematical......SPACE1006) in which a scaled down model of this microgrid is emulated....
A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution
Directory of Open Access Journals (Sweden)
Lijin Wang
2015-01-01
Full Text Available The backtracking search optimization algorithm (BSA is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.
OPTIMIZATION OF HYBRID FINAL ADDER FOR THE HIGH PERFORMANCE MULTIPLIER
Directory of Open Access Journals (Sweden)
RAMKUMAR B.
2013-04-01
Full Text Available In this work we evaluated arrival profile of the HPM based multiplier partial products reduction tree in two ways: 1.manual delay, area calculation through logical effort, 2.ASIC implementation. Based on the arrival profile, we worked with some recently proposed optimal adders and finally we proposed an optimal hybrid adder for the final addition in HPM based parallel multiplier. This work derives some mathematical expressions to find the size of different regions in the partial product arrival profile which helps to design optimal adder for each region. This work evaluates the performance of proposed hybrid adder in terms of area, power and delay using 90nm technology. This work deals with manual calculation for 8-b and ASIC simulation of different adder designs for 8-b, 16-b, 32-b and 64-b multiplier bit sizes.
Small Spacecraft System-Level Design and Optimization for Interplanetary Trajectories
Spangelo, Sara; Dalle, Derek; Longmier, Ben
2014-01-01
The feasibility of an interplanetary mission for a CubeSat, a type of miniaturized spacecraft, that uses an emerging technology, the CubeSat Ambipolar Thruster (CAT) is investigated. CAT is a large delta-V propulsion system that uses a high-density plasma source that has been miniaturized for small spacecraft applications. An initial feasibility assessment that demonstrated escaping Low Earth Orbit (LEO) and achieving Earth-escape trajectories with a 3U CubeSat and this thruster technology was demonstrated in previous work. We examine a mission architecture with a trajectory that begins in Earth orbits such as LEO and Geostationary Earth Orbit (GEO) which escapes Earth orbit and travels to Mars, Jupiter, or Saturn. The goal was to minimize travel time to reach the destinations and considering trade-offs between spacecraft dry mass, fuel mass, and solar power array size. Sensitivities to spacecraft dry mass and available power are considered. CubeSats are extremely size, mass, and power constrained, and their subsystems are tightly coupled, limiting their performance potential. System-level modeling, simulation, and optimization approaches are necessary to find feasible and optimal operational solutions to ensure system-level interactions are modeled. Thus, propulsion, power/energy, attitude, and orbit transfer models are integrated to enable systems-level analysis and trades. The CAT technology broadens the possible missions achievable with small satellites. In particular, this technology enables more sophisticated maneuvers by small spacecraft such as polar orbit insertion from an equatorial orbit, LEO to GEO transfers, Earth-escape trajectories, and transfers to other interplanetary bodies. This work lays the groundwork for upcoming CubeSat launch opportunities and supports future development of interplanetary and constellation CubeSat and small satellite mission concepts.
Duan, Hai-Bin; Xu, Chun-Fang; Xing, Zhi-Hui
2010-02-01
In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.
Flight orientation behaviors promote optimal migration trajectories in high-flying insects.
Chapman, Jason W; Nesbit, Rebecca L; Burgin, Laura E; Reynolds, Don R; Smith, Alan D; Middleton, Douglas R; Hill, Jane K
2010-02-01
Many insects undertake long-range seasonal migrations to exploit temporary breeding sites hundreds or thousands of kilometers apart, but the behavioral adaptations that facilitate these movements remain largely unknown. Using entomological radar, we showed that the ability to select seasonally favorable, high-altitude winds is widespread in large day- and night-flying migrants and that insects adopt optimal flight headings that partially correct for crosswind drift, thus maximizing distances traveled. Trajectory analyses show that these behaviors increase migration distances by 40% and decrease the degree of drift from seasonally optimal directions. These flight behaviors match the sophistication of those seen in migrant birds and help explain how high-flying insects migrate successfully between seasonal habitats.
Bernoulli substitution in the Ramsey model: Optimal trajectories under control constraints
Krasovskii, A. A.; Lebedev, P. D.; Tarasyev, A. M.
2017-05-01
We consider a neoclassical (economic) growth model. A nonlinear Ramsey equation, modeling capital dynamics, in the case of Cobb-Douglas production function is reduced to the linear differential equation via a Bernoulli substitution. This considerably facilitates the search for a solution to the optimal growth problem with logarithmic preferences. The study deals with solving the corresponding infinite horizon optimal control problem. We consider a vector field of the Hamiltonian system in the Pontryagin maximum principle, taking into account control constraints. We prove the existence of two alternative steady states, depending on the constraints. A proposed algorithm for constructing growth trajectories combines methods of open-loop control and closed-loop regulatory control. For some levels of constraints and initial conditions, a closed-form solution is obtained. We also demonstrate the impact of technological change on the economic equilibrium dynamics. Results are supported by computer calculations.
Galatzer-Levy, Isaac R; Bonanno, George A
2014-12-01
The course of depression in relation to myocardial infarction (MI), commonly known as heart attack, and the consequences for mortality are not well characterized. Further, optimism may predict both the effects of MI on depression as well as mortality secondary to MI. In the current study, we utilized a large population-based prospective sample of older adults (N=2,147) to identify heterogeneous trajectories of depression from 6 years prior to their first-reported MI to 4 years after. Findings indicated that individuals were at significantly increased risk for mortality when depression emerged after their first-reported MI, compared with resilient individuals who had no significant post-MI elevation in depression symptomatology. Individuals with chronic depression and those demonstrating pre-event depression followed by recovery after MI were not at increased risk. Further, optimism, measured before MI, prospectively differentiated all depressed individuals from participants who were resilient.
Zhao, Dang-Jun; Song, Zheng-Yu
2017-08-01
This study proposes a multiphase convex programming approach for rapid reentry trajectory generation that satisfies path, waypoint and no-fly zone (NFZ) constraints on Common Aerial Vehicles (CAVs). Because the time when the vehicle reaches the waypoint is unknown, the trajectory of the vehicle is divided into several phases according to the prescribed waypoints, rendering a multiphase optimization problem with free final time. Due to the requirement of rapidity, the minimum flight time of each phase index is preferred over other indices in this research. The sequential linearization is used to approximate the nonlinear dynamics of the vehicle as well as the nonlinear concave path constraints on the heat rate, dynamic pressure, and normal load; meanwhile, the convexification techniques are proposed to relax the concave constraints on control variables. Next, the original multiphase optimization problem is reformulated as a standard second-order convex programming problem. Theoretical analysis is conducted to show that the original problem and the converted problem have the same solution. Numerical results are presented to demonstrate that the proposed approach is efficient and effective.
Armentum: a hybrid direct search optimization methodology
Briones, Francisco Zorrilla
2016-07-01
Design of experiments (DOE) offers a great deal of benefits to any manufacturing organization, such as characterization of variables and sets the path for the optimization of the levels of these variables (settings) trough the Response surface methodology, leading to process capability improvement, efficiency increase, cost reduction. Unfortunately, the use of these methodologies is very limited due to various situations. Some of these situations involve the investment on production time, materials, personnel, equipment; most of organizations are not willing to invest in these resources or are not capable because of production demands, besides the fact that they will produce non-conformant product (scrap) during the process of experimentation. Other methodologies, in the form of algorithms, may be used to optimize a process. Known as direct search methods, these algorithms search for an optimum on an unknown function, trough the search of the best combination of the levels on the variables considered in the analysis. These methods have a very different application strategy, they search on the best combination of parameters, during the normal production run, calculating the change in the input variables and evaluating the results in small steps until an optimum is reached. These algorithms are very sensible to internal noise (variation of the input variables), among other disadvantages. In this paper it is made a comparison between the classical experimental design and one of these direct search methods, developed by Nelder and Mead (1965), known as the Nelder Mead simplex (NMS), trying to overcome the disadvantages and maximize the advantages of both approaches, trough a proposed combination of the two methodologies.
Hybrid Modeling and Optimization of Yogurt Starter Culture Continuous Fermentation
Directory of Open Access Journals (Sweden)
Silviya Popova
2009-10-01
Full Text Available The present paper presents a hybrid model of yogurt starter mixed culture fermentation. The main nonlinearities within a classical structure of continuous process model are replaced by neural networks. The new hybrid model accounts for the dependence of the two microorganisms' kinetics from the on-line measured characteristics of the culture medium - pH. Then the model was used further for calculation of the optimal time profile of pH. The obtained results are with agreement with the experimental once.
Advanced hybrid and electric vehicles system optimization and vehicle integration
2016-01-01
This contributed volume contains the results of the research program “Agreement for Hybrid and Electric Vehicles”, funded by the International Energy Agency. The topical focus lies on technology options for the system optimization of hybrid and electric vehicle components and drive train configurations which enhance the energy efficiency of the vehicle. The approach to the topic is genuinely interdisciplinary, covering insights from fields. The target audience primarily comprises researchers and industry experts in the field of automotive engineering, but the book may also be beneficial for graduate students.
Hybrid and adaptive meta-model-based global optimization
Gu, J.; Li, G. Y.; Dong, Z.
2012-01-01
As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for design problems involving computation intensive, black-box analyses and simulations.
Design Optimization of a Hybrid Electric Vehicle Powertrain
Mangun, Firdause; Idres, Moumen; Abdullah, Kassim
2017-03-01
This paper presents an optimization work on hybrid electric vehicle (HEV) powertrain using Genetic Algorithm (GA) method. It focused on optimization of the parameters of powertrain components including supercapacitors to obtain maximum fuel economy. Vehicle modelling is based on Quasi-Static-Simulation (QSS) backward-facing approach. A combined city (FTP-75)-highway (HWFET) drive cycle is utilized for the design process. Seeking global optimum solution, GA was executed with different initial settings to obtain sets of optimal parameters. Starting from a benchmark HEV, optimization results in a smaller engine (2 l instead of 3 l) and a larger battery (15.66 kWh instead of 2.01 kWh). This leads to a reduction of 38.3% in fuel consumption and 30.5% in equivalent fuel consumption. Optimized parameters are also compared with actual values for HEV in the market.
Rapid Optimal Generation Algorithm for Terrain Following Trajectory Based on Optimal Control
Institute of Scientific and Technical Information of China (English)
杨剑影; 张海; 谢邦荣; 尹健
2004-01-01
Based on the optimal control theory, a 3-dimensionnal direct generation algorithm is proposed for anti-ground low altitude penetration tasks under complex terrain. By optimizing the terrain following(TF) objective function,terrain coordinate system, missile dynamic model and control vector, the TF issue is turning into the improved optimal control problem whose mathmatical model is simple and need not solve the second order terrain derivative. Simulation results prove that this method is reasonable and feasible. The TF precision is in the scope from 0.3 m to 3.0 m,and the planning time is less than 30 min. This method have the strongpionts such as rapidness, precision and has great application value.
Optimal design of a hybridization scheme with a fuel cell using genetic optimization
Rodriguez, Marco A.
Fuel cell is one of the most dependable "green power" technologies, readily available for immediate application. It enables direct conversion of hydrogen and other gases into electric energy without any pollution of the environment. However, the efficient power generation is strictly stationary process that cannot operate under dynamic environment. Consequently, fuel cell becomes practical only within a specially designed hybridization scheme, capable of power storage and power management functions. The resultant technology could be utilized to its full potential only when both the fuel cell element and the entire hybridization scheme are optimally designed. The design optimization in engineering is among the most complex computational tasks due to its multidimensionality, nonlinearity, discontinuity and presence of constraints in the underlying optimization problem. this research aims at the optimal utilization of the fuel cell technology through the use of genetic optimization, and advance computing. This study implements genetic optimization in the definition of optimum hybridization rules for a PEM fuel cell/supercapacitor power system. PEM fuel cells exhibit high energy density but they are not intended for pulsating power draw applications. They work better in steady state operation and thus, are often hybridized. In a hybrid system, the fuel cell provides power during steady state operation while capacitors or batteries augment the power of the fuel cell during power surges. Capacitors and batteries can also be recharged when the motor is acting as a generator. Making analogies to driving cycles, three hybrid system operating modes are investigated: 'Flat' mode, 'Uphill' mode, and 'Downhill' mode. In the process of discovering the switching rules for these three modes, we also generate a model of a 30W PEM fuel cell. This study also proposes the optimum design of a 30W PEM fuel cell. The PEM fuel cell model and hybridization's switching rules are postulated
Energy Technology Data Exchange (ETDEWEB)
Alemi, Mallory; Loring, Roger F., E-mail: roger.loring@cornell.edu [Baker Laboratory, Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853 (United States)
2015-06-07
The optimized mean-trajectory (OMT) approximation is a semiclassical method for computing vibrational response functions from action-quantized classical trajectories connected by discrete transitions that represent radiation-matter interactions. Here, we extend the OMT to include additional vibrational coherence and energy transfer processes. This generalized approximation is applied to a pair of anharmonic chromophores coupled to a bath. The resulting 2D spectra are shown to reflect coherence transfer between normal modes.
A hybrid optimization approach in non-isothermal glass molding
Vu, Anh-Tuan; Kreilkamp, Holger; Krishnamoorthi, Bharathwaj Janaki; Dambon, Olaf; Klocke, Fritz
2016-10-01
Intensively growing demands on complex yet low-cost precision glass optics from the today's photonic market motivate the development of an efficient and economically viable manufacturing technology for complex shaped optics. Against the state-of-the-art replication-based methods, Non-isothermal Glass Molding turns out to be a promising innovative technology for cost-efficient manufacturing because of increased mold lifetime, less energy consumption and high throughput from a fast process chain. However, the selection of parameters for the molding process usually requires a huge effort to satisfy precious requirements of the molded optics and to avoid negative effects on the expensive tool molds. Therefore, to reduce experimental work at the beginning, a coupling CFD/FEM numerical modeling was developed to study the molding process. This research focuses on the development of a hybrid optimization approach in Non-isothermal glass molding. To this end, an optimal configuration with two optimization stages for multiple quality characteristics of the glass optics is addressed. The hybrid Back-Propagation Neural Network (BPNN)-Genetic Algorithm (GA) is first carried out to realize the optimal process parameters and the stability of the process. The second stage continues with the optimization of glass preform using those optimal parameters to guarantee the accuracy of the molded optics. Experiments are performed to evaluate the effectiveness and feasibility of the model for the process development in Non-isothermal glass molding.
A hybrid genetic algorithm to optimize simple distillation column sequences
Institute of Scientific and Technical Information of China (English)
GAN YongSheng; Andreas Linninger
2004-01-01
Based on the principles of Genetic Algorithms (GAs), a hybrid genetic algorithm used to optimize simple distillation column sequences was established. A new data structure, a novel arithmetic crossover operator and a dynamic mutation operator were proposed. Together with the feasibility test of distillation columns, they are capable to obtain the optimum simple column sequence at one time without the limitation of the number of mixture components, ideal or non-ideal mixtures and sloppy or sharp splits. Compared with conventional algorithms, this hybrid genetic algorithm avoids solving complicated nonlinear equations and demands less derivative information and computation time. Result comparison between this genetic algorithm and Underwood method and Doherty method shows that this hybrid genetic algorithm is reliable.
DUKSUP: A Computer Program for High Thrust Launch Vehicle Trajectory Design and Optimization
Spurlock, O. Frank; Williams, Craig H.
2015-01-01
From the late 1960s through 1997, the leadership of NASAs Intermediate and Large class unmanned expendable launch vehicle projects resided at the NASA Lewis (now Glenn) Research Center (LeRC). One of LeRCs primary responsibilities --- trajectory design and performance analysis --- was accomplished by an internally-developed analytic three dimensional computer program called DUKSUP. Because of its Calculus of Variations-based optimization routine, this code was generally more capable of finding optimal solutions than its contemporaries. A derivation of optimal control using the Calculus of Variations is summarized including transversality, intermediate, and final conditions. The two point boundary value problem is explained. A brief summary of the codes operation is provided, including iteration via the Newton-Raphson scheme and integration of variational and motion equations via a 4th order Runge-Kutta scheme. Main subroutines are discussed. The history of the LeRC trajectory design efforts in the early 1960s is explained within the context of supporting the Centaur upper stage program. How the code was constructed based on the operation of the AtlasCentaur launch vehicle, the limits of the computers of that era, the limits of the computer programming languages, and the missions it supported are discussed. The vehicles DUKSUP supported (AtlasCentaur, TitanCentaur, and ShuttleCentaur) are briefly described. The types of missions, including Earth orbital and interplanetary, are described. The roles of flight constraints and their impact on launch operations are detailed (such as jettisoning hardware on heating, Range Safety, ground station tracking, and elliptical parking orbits). The computer main frames on which the code was hosted are described. The applications of the code are detailed, including independent check of contractor analysis, benchmarking, leading edge analysis, and vehicle performance improvement assessments. Several of DUKSUPs many major impacts on
Optimal trajectories for an aerospace plane. Part 1: Formulation, results, and analysis
Miele, Angelo; Lee, W. Y.; Wu, G. D.
1990-01-01
The optimization of the trajectories of an aerospace plane is discussed. This is a hypervelocity vehicle capable of achieving orbital speed, while taking off horizontally. The vehicle is propelled by four types of engines: turbojet engines for flight at subsonic speeds/low supersonic speeds; ramjet engines for flight at moderate supersonic speeds/low hypersonic speeds; scramjet engines for flight at hypersonic speeds; and rocket engines for flight at near-orbital speeds. A single-stage-to-orbit (SSTO) configuration is considered, and the transition from low supersonic speeds to orbital speeds is studied under the following assumptions: the turbojet portion of the trajectory has been completed; the aerospace plane is controlled via the angle of attack and the power setting; the aerodynamic model is the generic hypersonic aerodynamics model example (GHAME). Concerning the engine model, three options are considered: (EM1), a ramjet/scramjet combination in which the scramjet specific impulse tends to a nearly-constant value at large Mach numbers; (EM2), a ramjet/scramjet combination in which the scramjet specific impulse decreases monotonically at large Mach numbers; and (EM3), a ramjet/scramjet/rocket combination in which, owing to stagnation temperature limitations, the scramjet operates only at M approx. less than 15; at higher Mach numbers, the scramjet is shut off and the aerospace plane is driven only by the rocket engines. Under the above assumptions, four optimization problems are solved using the sequential gradient-restoration algorithm for optimal control problems: (P1) minimization of the weight of fuel consumed; (P2) minimization of the peak dynamic pressure; (P3) minimization of the peak heating rate; and (P4) minimization of the peak tangential acceleration.
Directory of Open Access Journals (Sweden)
A. P. Karpenko
2014-01-01
Full Text Available We consider a class of stochastic search algorithms of global optimization which in various publications are called behavioural, intellectual, metaheuristic, inspired by the nature, swarm, multi-agent, population, etc. We use the last term.Experience in using the population algorithms to solve challenges of global optimization shows that application of one such algorithm may not always effective. Therefore now great attention is paid to hybridization of population algorithms of global optimization. Hybrid algorithms unite various algorithms or identical algorithms, but with various values of free parameters. Thus efficiency of one algorithm can compensate weakness of another.The purposes of the work are development of hybrid algorithm of global optimization based on known algorithms of harmony search (HS and swarm of particles (PSO, software implementation of algorithm, study of its efficiency using a number of known benchmark problems, and a problem of dimensional optimization of truss structure.We set a problem of global optimization, consider basic algorithms of HS and PSO, give a flow chart of the offered hybrid algorithm called PSO HS , present results of computing experiments with developed algorithm and software, formulate main results of work and prospects of its development.
Multiview coding mode decision with hybrid optimal stopping model.
Zhao, Tiesong; Kwong, Sam; Wang, Hanli; Wang, Zhou; Pan, Zhaoqing; Kuo, C-C Jay
2013-04-01
In a generic decision process, optimal stopping theory aims to achieve a good tradeoff between decision performance and time consumed, with the advantages of theoretical decision-making and predictable decision performance. In this paper, optimal stopping theory is employed to develop an effective hybrid model for the mode decision problem, which aims to theoretically achieve a good tradeoff between the two interrelated measurements in mode decision, as computational complexity reduction and rate-distortion degradation. The proposed hybrid model is implemented and examined with a multiview encoder. To support the model and further promote coding performance, the multiview coding mode characteristics, including predicted mode probability and estimated coding time, are jointly investigated with inter-view correlations. Exhaustive experimental results with a wide range of video resolutions reveal the efficiency and robustness of our method, with high decision accuracy, negligible computational overhead, and almost intact rate-distortion performance compared to the original encoder.
Hybrid Optimization in the Design of Reciprocal Structures
DEFF Research Database (Denmark)
Parigi, Dario; Kirkegaard, Poul Henning; Sassone, Mario
2012-01-01
that explore the global domain of solutions as genetic algorithms (GAs). The benchmark tests show that when the control on the topology is required the best result is obtained by a hybrid approach that combines the global search of the GA with the local search of a GB algorithm. The optimization method......The paper presents a method to generate the geometry of reciprocal structures by means of a hybrid optimization procedure. The geometry of reciprocal structures where elements are sitting on the top or in the bottom of each other is extremely difficult to predict because of the non....... In this paper it is shown that the geometrically compatible position of the elements could be determined by local search algorithm gradient-based (GB). However the control on which bar sit on the top or in the bottom at each connection can be regarded as a topological problem and require the use of algorithms...
Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.
Elhossini, Ahmed; Areibi, Shawki; Dony, Robert
2010-01-01
This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.
DEFF Research Database (Denmark)
Awasthi, Abhishek; Venkitusamy, Karthikeyan; Padmanaban, Sanjeevikumar
2017-01-01
, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site...... and the size of the station) which leads to an improvement in the algorithm functionality and enhances quality of solution. The genetic algorithm and improved version of conventional particle swarm optimization algorithm will also be compared with a conventional genetic algorithm and particle swarm...... optimization. Through simulation studies on a real time system of Allahabad city, the superior performance of the aforementioned technique with respect to genetic algorithm and particle swarm optimization in terms of improvement in voltage profile and quality....
Hybrid particle swarm optimization for solving resource-constrained FMS
Institute of Scientific and Technical Information of China (English)
Dongyun Wang; Liping Liu
2008-01-01
In this paper,an approach for resource-constrained flexible manufacturing system(FMS)scheduling was proposed,which is based on the particle swarm optimization(PSO)algorithm and simulated annealing(SA)algorithm.First,the formulation for resource-con-strained FMS scheduling problem was introduced and cost function for this problem was obtained.Then.a hybrid algorithm of PSO and SA was employed to obtain optimal solution.The simulated results show that the approach can dislodge a state from a local min-imum and guide it to the global minimum.
Hybrid Optimization of Support Vector Machine for Intrusion Detection
Institute of Scientific and Technical Information of China (English)
XI Fu-li; YU Song-nian; HAO Wei
2005-01-01
Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it's an effective method and can improve the perfornance of SVM-based intrusion detection system further.
Optimizing Hybrid Wind/Diesel Generator System Using BAT Algorithm
Directory of Open Access Journals (Sweden)
Sudhir Sharma,
2016-01-01
Full Text Available Hybrid system comprising of Wind/Diesel generation system for a practical standalone application considers Wind turbine generators and diesel generator as primary power sources for generating electricity. Battery banks are considered as a backup power source. The total value of cost is reduced by meeting energy demand required by the customers. Bat optimization technique is implemented to optimize wind and battery modules. Wind and battery banks are considered as primary sources and diesel generator as a secondary power source for the system
A New Class of Hybrid Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
Da-Qing Guo; Yong-Jin Zhao; Hui Xiong; Xiao Li
2007-01-01
A new class of hybrid particle swarm optimization (PSO) algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique (LDIW) and the mutative scale chaos optimization algorithm (MSCOA) are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.
Hybrid Algorithm for the Optimization of Training Convolutional Neural Network
Directory of Open Access Journals (Sweden)
Hayder M. Albeahdili
2015-10-01
Full Text Available The training optimization processes and efficient fast classification are vital elements in the development of a convolution neural network (CNN. Although stochastic gradient descend (SGD is a Prevalence algorithm used by many researchers for the optimization of training CNNs, it has vast limitations. In this paper, it is endeavor to diminish and tackle drawbacks inherited from SGD by proposing an alternate algorithm for CNN training optimization. A hybrid of genetic algorithm (GA and particle swarm optimization (PSO is deployed in this work. In addition to SGD, PSO and genetic algorithm (PSO-GA are also incorporated as a combined and efficient mechanism in achieving non trivial solutions. The proposed unified method achieves state-of-the-art classification results on the different challenge benchmark datasets such as MNIST, CIFAR-10, and SVHN. Experimental results showed that the results outperform and achieve superior results to most contemporary approaches.
Improved hybrid optimization algorithm for 3D protein structure prediction.
Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang
2014-07-01
A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.
Multiobjective muffler shape optimization with hybrid acoustics modeling.
Airaksinen, Tuomas; Heikkola, Erkki
2011-09-01
This paper considers the combined use of a hybrid numerical method for the modeling of acoustic mufflers and a genetic algorithm for multiobjective optimization. The hybrid numerical method provides accurate modeling of sound propagation in uniform waveguides with non-uniform obstructions. It is based on coupling a wave based modal solution in the uniform sections of the waveguide to a finite element solution in the non-uniform component. Finite element method provides flexible modeling of complicated geometries, varying material parameters, and boundary conditions, while the wave based solution leads to accurate treatment of non-reflecting boundaries and straightforward computation of the transmission loss (TL) of the muffler. The goal of optimization is to maximize TL at multiple frequency ranges simultaneously by adjusting chosen shape parameters of the muffler. This task is formulated as a multiobjective optimization problem with the objectives depending on the solution of the simulation model. NSGA-II genetic algorithm is used for solving the multiobjective optimization problem. Genetic algorithms can be easily combined with different simulation methods, and they are not sensitive to the smoothness properties of the objective functions. Numerical experiments demonstrate the accuracy and feasibility of the model-based optimization method in muffler design.
Wu, Hao; Mey, Antonia S J S; Rosta, Edina; Noé, Frank
2014-12-07
We propose a discrete transition-based reweighting analysis method (dTRAM) for analyzing configuration-space-discretized simulation trajectories produced at different thermodynamic states (temperatures, Hamiltonians, etc.) dTRAM provides maximum-likelihood estimates of stationary quantities (probabilities, free energies, expectation values) at any thermodynamic state. In contrast to the weighted histogram analysis method (WHAM), dTRAM does not require data to be sampled from global equilibrium, and can thus produce superior estimates for enhanced sampling data such as parallel/simulated tempering, replica exchange, umbrella sampling, or metadynamics. In addition, dTRAM provides optimal estimates of Markov state models (MSMs) from the discretized state-space trajectories at all thermodynamic states. Under suitable conditions, these MSMs can be used to calculate kinetic quantities (e.g., rates, timescales). In the limit of a single thermodynamic state, dTRAM estimates a maximum likelihood reversible MSM, while in the limit of uncorrelated sampling data, dTRAM is identical to WHAM. dTRAM is thus a generalization to both estimators.
Shanechi, Maryam M; Williams, Ziv M; Wornell, Gregory W; Hu, Rollin C; Powers, Marissa; Brown, Emery N
2013-01-01
Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.
Directory of Open Access Journals (Sweden)
Maryam M Shanechi
Full Text Available Real-time brain-machine interfaces (BMI have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.
Institute of Scientific and Technical Information of China (English)
彭祺擘; 张海联
2015-01-01
Lunar emergency ascent trajectory during soft landing was studied and a lunar emergency ascent trajectory optimization design model was established .A hybrid optimization strategy combi-ning GPM and shooting method was designed to obtain the optimal solution of the ascent trajectory parameters satisfying the emergency constraints .The simulation results showed that the proposed method could solve the complex optimization problem effectively , and the methodology and strategy for the optimal trajectory design have good robustness and strong convergence .%研究了月面软着陆过程发生故障后的应急上升轨道，建立了整个应急上升过程的优化设计模型，并针对构建的强约束轨道优化问题，设计了将Gauss伪谱法和直接打靶法结合的混合优化策略，求解得到了满足应急条件的上升轨道参数。仿真分析表明该方法可以有效解决月面应急上升轨道优化问题，且求解收敛性和鲁棒性较好，可为同类问题的求解提供参考。
A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization
Directory of Open Access Journals (Sweden)
Daqing Wu
2012-01-01
Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.
Optimal estimates of the diffusion coefficient of a single Brownian trajectory.
Boyer, Denis; Dean, David S; Mejía-Monasterio, Carlos; Oshanin, Gleb
2012-03-01
Modern developments in microscopy and image processing are revolutionizing areas of physics, chemistry, and biology as nanoscale objects can be tracked with unprecedented accuracy. The goal of single-particle tracking is to determine the interaction between the particle and its environment. The price paid for having a direct visualization of a single particle is a consequent lack of statistics. Here we address the optimal way to extract diffusion constants from single trajectories for pure Brownian motion. It is shown that the maximum likelihood estimator is much more efficient than the commonly used least-squares estimate. Furthermore, we investigate the effect of disorder on the distribution of estimated diffusion constants and show that it increases the probability of observing estimates much smaller than the true (average) value.
Optimal Trajectory Planning For Design of a Crawling Gait in a Robot Using Genetic Algorithm
Directory of Open Access Journals (Sweden)
SMRS. Noorani
2011-03-01
Full Text Available This paper describes a new locomotion mode to use in a crawling robot, inspired of real inchworm. The crawling device is modelled as a mobile manipulator, and for each step of its motion, the associated dynamics relations are derived using Euler-Lagrange equations. Next, the Genetic Algorithm (GA is utilized to optimize the trajectory of the free joints (active actuators in order to minimize the consumed effort (e.g. integral of square of torques over the step time. In this way, the results show a reduction of 5 to 37 percent in torque consumption in comparison with the gradient based method. Finally, numerical simulation for each step motion is presented to validate the proposed algorithm.
Heliocentric interplanetary low thrust trajectory optimization program, supplement 1, part 2
Mann, F. I.; Horsewood, J. L.
1978-01-01
The improvements made to the HILTOP electric propulsion trajectory computer program are described. A more realistic propulsion system model was implemented in which various thrust subsystem efficiencies and specific impulse are modeled as variable functions of power available to the propulsion system. The number of operating thrusters are staged, and the beam voltage is selected from a set of five (or less) constant voltages, based upon the application of variational calculus. The constant beam voltages may be optimized individually or collectively. The propulsion system logic is activated by a single program input key in such a manner as to preserve the HILTOP logic. An analysis describing these features, a complete description of program input quantities, and sample cases of computer output illustrating the program capabilities are presented.
A Hybrid Approach to the Optimization of Multiechelon Systems
Directory of Open Access Journals (Sweden)
Paweł Sitek
2015-01-01
Full Text Available In freight transportation there are two main distribution strategies: direct shipping and multiechelon distribution. In the direct shipping, vehicles, starting from a depot, bring their freight directly to the destination, while in the multiechelon systems, freight is delivered from the depot to the customers through an intermediate points. Multiechelon systems are particularly useful for logistic issues in a competitive environment. The paper presents a concept and application of a hybrid approach to modeling and optimization of the Multi-Echelon Capacitated Vehicle Routing Problem. Two ways of mathematical programming (MP and constraint logic programming (CLP are integrated in one environment. The strengths of MP and CLP in which constraints are treated in a different way and different methods are implemented and combined to use the strengths of both. The proposed approach is particularly important for the discrete decision models with an objective function and many discrete decision variables added up in multiple constraints. An implementation of hybrid approach in the ECLiPSe system using Eplex library is presented. The Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP and its variants are shown as an illustrative example of the hybrid approach. The presented hybrid approach will be compared with classical mathematical programming on the same benchmark data sets.
OPTIMIZATION APPROACH FOR HYBRID ELECTRIC VEHICLE POWERTRAIN DESIGN
Institute of Scientific and Technical Information of China (English)
Zhu Zhengli; Zhang Jianwu; Yin Chengliang
2005-01-01
According to bench test results of fuel economy and engine emission for the real powertrain system of EQ7200HEV car, a 3-D performance map oriented quasi-linear model is developed for the configuration of the powertrain components such as internal combustion engine, traction electric motor, transmission, main retarder and energy storage unit. A genetic algorithm based on optimization procedure is proposed and applied for parametric optimization of the key components by consideration of requirements of some driving cycles. Through comparison of numerical results obtained by the genetic algorithm with those by traditional optimization methods, it is shown that the present approach is quite effective and efficient in emission reduction and fuel economy for the design of the hybrid electric car powertrain.
Hybrid Quantum-Classical Approach to Quantum Optimal Control.
Li, Jun; Yang, Xiaodong; Peng, Xinhua; Sun, Chang-Pu
2017-04-14
A central challenge in quantum computing is to identify more computational problems for which utilization of quantum resources can offer significant speedup. Here, we propose a hybrid quantum-classical scheme to tackle the quantum optimal control problem. We show that the most computationally demanding part of gradient-based algorithms, namely, computing the fitness function and its gradient for a control input, can be accomplished by the process of evolution and measurement on a quantum simulator. By posing queries to and receiving answers from the quantum simulator, classical computing devices update the control parameters until an optimal control solution is found. To demonstrate the quantum-classical scheme in experiment, we use a seven-qubit nuclear magnetic resonance system, on which we have succeeded in optimizing state preparation without involving classical computation of the large Hilbert space evolution.
Design Optimization of Gas Generator Hybrid Propulsion Boosters
Weldon, Vincent; Phillips, Dwight; Fink, Larry
1990-01-01
A methodology used in support of a study for NASA/MSFC to optimize the design of gas generator hybrid propulsion booster for uprating the National Space Transportation System (NSTS) is presented. The objective was to compare alternative configurations for this booster approach, optimizing each candidate concept on different bases, in order to develop data for a trade table on which a final decision was based. The methodology is capable of processing a large number of independent and dependent variables, adjusting the overall subsystems characteristics to arrive at a best compromise integrated design to meet various specific optimization criteria subject to selected constraints. For each system considered, a detailed weight statement was generated along with preliminary cost and reliability estimates.
Libraro, Paola
The general electric propulsion orbit-raising maneuver of a spacecraft must contend with four main limiting factors: the longer time of flight, multiple eclipses prohibiting continuous thrusting, long exposure to radiation from the Van Allen belt and high power requirement of the electric engines. In order to optimize a low-thrust transfer with respect to these challenges, the choice of coordinates and corresponding equations of motion used to describe the kinematical and dynamical behavior of the satellite is of critical importance. This choice can potentially affect the numerical optimization process as well as limit the set of mission scenarios that can be investigated. To increase the ability to determine the feasible set of mission scenarios able to address the challenges of an all-electric orbit-raising, a set of equations free of any singularities is required to consider a completely arbitrary injection orbit. For this purpose a new quaternion-based formulation of a spacecraft translational dynamics that is globally nonsingular has been developed. The minimum-time low-thrust problem has been solved using the new set of equations of motion inside a direct optimization scheme in order to investigate optimal low-thrust trajectories over the full range of injection orbit inclinations between 0 and 90 degrees with particular focus on high-inclinations. The numerical results consider a specific mission scenario in order to analyze three key aspects of the problem: the effect of the initial guess on the shape and duration of the transfer, the effect of Earth oblateness on transfer time and the role played by, radiation damage and power degradation in all-electric minimum-time transfers. Finally trade-offs between mass and cost savings are introduced through a test case.
A Hybrid Optimization Approach for SRM FINOCYL Grain Design
Institute of Scientific and Technical Information of China (English)
Khurram Nisar; Liang Guozhu; Qasim Zeeshan
2008-01-01
This article presents a method to design and optimize 3D FINOCYL grain (FCG) configuration for solid rocket motors (SRMs). The design process of FCG configuration involves mathematical modeling of the geometry and parametric evaluation of various inde-pendent geometric variables that define the complex configuration. Vh'tually infinite combinations of these variables will satisfy the requirements of mass of propellant, thrust, and burning time in addition to satisfying basic needs for volumetric loading fraction and web fraction. In order to ensure the acquisition of the best possible design to be acquired, a sound approach of design and optimization is essentially demanded. To meet this need, a method is introduced to acquire the finest possible performance. A series of computations are carried out to formulate the grain geometry in terms of various combinations of key shapes inclusive of ellipsoid, cone, cylinder, sphere, torus, and inclined plane. A hybrid optimization (HO) technique is established by associating genetic algorithm (GA) for global solution convergence with sequential quadratic programming (SQP) for further local convergence of the solution, thus achieving the final optimal design. A comparison of the optimal design results derived from SQP, GA, and HO algorithms is presented. By using HO technique, the parameter of propellant mass is optimized to the minimum value with the required level of thrust staying within the constrained burning time, nozzle and propellant parameters, and a fixed length and outer diameter of grain,
Wind hybrid electrical supply system: behaviour simulation and sizing optimization
Notton, G.; Cristofari, C.; Poggi, P.; Muselli, M.
2001-04-01
Using a global approach, a wind hybrid system operation is simulated and the evolution of several parameters is analysed, such as the wasted energy, the fuel consumption and the role of the wind turbine subsystem in the global production. This analysis shows that all the energies which take part in the system operation are more dependent on the wind turbine size than on the battery storage capacity. A storage of 2 or 3 days is sufficient, because an increase in storage beyond these values does not have a notable impact on the performance of the wind hybrid system. Finally, a cost study is performed to determine the optimal configuration of the system conducive to the lowest cost of electricity production.
Directory of Open Access Journals (Sweden)
Bankov Dimitrov Nikolay
2012-01-01
Full Text Available This work examines a series resonant DC/DC optimal trajectory controlled converter during operation above resonant frequency, taking into account the influence of the snubbers and matching transformer. We obtain expressions for the load characteristics, boundary curves between possible modes and limits of the soft commutation area. Computer simulation and experimental observation confirm the theoretical results.
Bankov Dimitrov Nikolay; Vuchev Stoyanov Aleksandar
2012-01-01
This work examines a series resonant DC/DC optimal trajectory controlled converter during operation above resonant frequency, taking into account the influence of the snubbers and matching transformer. We obtain expressions for the load characteristics, boundary curves between possible modes and limits of the soft commutation area. Computer simulation and experimental observation confirm the theoretical results.
Optimal Control of Engine Warmup in Hybrid Vehicles
Directory of Open Access Journals (Sweden)
van Reeven Vital
2016-01-01
Full Text Available An Internal Combustion Engine (ICE under cold conditions experiences increased friction losses due to a high viscosity of the lubricant. With the additional control freedom present in hybrid electric vehicles, the losses during warmup can be minimized and fuel can be saved. In this paper, firstly, a control-oriented model of the ICE, describing the warmup behavior, is developed and validated on measured vehicle data. Secondly, the two-state, non-autonomous fuel optimization, for a parallel hybrid electric vehicle with stop-start functionality, is solved using optimal control theory. The principal behavior of the Lagrange multipliers is explicitly derived, including the discontinuities (jumps that are caused by the constraints on the lubricant temperature and the energy in the battery system. The minimization of the Hamiltonian for this two-state problem is also explicitly solved, resulting in a computationally efficient algorithm. The optimal controller shows the fuel benefit, as a function of the initial temperature, for a long-haul truck simulated on the FTP-75.
Series hybrid vehicles and optimized hydrogen engine design
Energy Technology Data Exchange (ETDEWEB)
Smith, J.R.; Aceves, S. [Lawrence Livermore National Lab., CA (United States); Van Blarigan, P. [Sandia National Labs., Livermore, CA (United States)
1995-05-10
Lawrence Livermore, Sandia Livermore and Los Alamos National Laboratories have a joint project to develop an optimized hydrogen fueled engine for series hybrid automobiles. The major divisions of responsibility are: system analysis, engine design and kinetics modeling by LLNL; performance and emission testing, and friction reduction by SNL; computational fluid mechanics and combustion modeling by LANL. This project is a component of the Department of Energy, Office of Utility Technology, National Hydrogen Program. We report here on the progress on system analysis and preliminary engine testing. We have done system studies of series hybrid automobiles that approach the PNGV design goal of 34 km/liter (80 mpg), for 384 km (240 mi) and 608 km (380 mi) ranges. Our results indicate that such a vehicle appears feasible using an optimized hydrogen engine. The impact of various on-board storage options on fuel economy are evaluated. Experiments with an available engine at the Sandia Combustion Research Facility demonstrated NO{sub x} emissions of 10 to 20 ppm at an equivalence ratio of 0.4, rising to about 500 ppm at 0.5 equivalence ratio using neat hydrogen. Hybrid vehicle simulation studies indicate that exhaust NO{sub x} concentrations must be less than 180 ppm to meet the 0.2 g/mile California Air Resources Board ULEV or Federal Tier II emissions regulations. We have designed and fabricated a first generation optimized hydrogen engine head for use on an existing single cylinder Onan engine. This head currently features 14.8:1 compression ratio, dual ignition, water cooling, two valves and open quiescent combustion chamber to minimize heat transfer losses.
A fully adaptive hybrid optimization of aircraft engine blades
Dumas, L.; Druez, B.; Lecerf, N.
2009-10-01
A new fully adaptive hybrid optimization method (AHM) has been developed and applied to an industrial problem in the field of the aircraft engine industry. The adaptivity of the coupling between a global search by a population-based method (Genetic Algorithms or Evolution Strategies) and the local search by a descent method has been particularly emphasized. On various analytical test cases, the AHM method overperforms the original global search method in terms of computational time and accuracy. The results obtained on the industrial case have also confirmed the interest of AHM for the design of new and original solutions in an affordable time.
Optimal traffic light control method for a single intersection based on hybrid systems
Institute of Scientific and Technical Information of China (English)
赵晓华; 陈阳舟; 崔平远
2003-01-01
A single intersection of two phases is selected as a model to put forward a new optimal time-planning scheme for traffic light based on the model of hybrid automata for single intersection. A method of optimization is proposed for hybrid systems, and the average queue length over all queues is used as an objective function to find an optimal switching scheme for traffic light. It is illustrated that traffic light control for single intersection is a typical hybrid system, and the optimal planning-time scheme can be obtained using the optimal hybrid systems control based on the two stages method.
Wang, Yan; Huang, Song; Ji, Zhicheng
2017-07-01
This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.
Mozaffari, Ahmad; Vajedi, Mahyar; Azad, Nasser L.
2015-06-01
The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge (SOC) trajectory in plug-in hybrid electric vehicles (PHEVs). The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categorizing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm (AIA). The objective function of the optimizer is derived from a computationally efficient artificial neural network (ANN) which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomie software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.
Fuel optimal control of parallel hybrid electric vehicles
Institute of Scientific and Technical Information of China (English)
Jinhuan PU; Chenliang YIN; Jianwu ZHANG
2008-01-01
A mathematical model for fuel optimal control and its corresponding dynamic programming (DP) recurs-ive equation were established for an existing parallel hybrid electric vehicle (HEV). Two augmented cost func-tions for gear shifting and engine stop-starting were designed to limit their frequency. To overcome the prob-lem of numerical DP dimensionality, an algorithm to restrict the exploring region was proposed. The algorithm significantly reduced the computational complexity. The system model was converted into real-time simulation code by using MATLAB/RTW to improve computation efficiency. Comparison between the results of a chassis dynamometer test, simulation, and DP proves that the proposed method can compute the performance limita-tion of the HEV within an acceptable time period and can be used to evaluate and optimize the control strategy.
Hybrid genetic optimization for design of photonic crystal emitters
Rammohan, R. R.; Farfan, B. G.; Su, M. F.; El-Kady, I.; Reda Taha, M. M.
2010-09-01
A unique hybrid-optimization technique is proposed, based on genetic algorithms (GA) and gradient descent (GD) methods, for the smart design of photonic crystal (PhC) emitters. The photonic simulation is described and the granularity of photonic crystal dimensions is considered. An innovative sliding-window method for performing local heuristic search is demonstrated. Finally, the application of the proposed method on two case studies for the design of a multi-pixel photonic crystal emitter and the design of thermal emitter in thermal photovoltaic is demonstrated. Discussion in the report includes the ability of the optimal PhC structures designed using the proposed method, to produce unprecedented high emission efficiencies of 54.5% in a significantly long wavelength region and 84.9% at significantly short wavelength region.
A dynamic hybrid framework for constrained evolutionary optimization.
Wang, Yong; Cai, Zixing
2012-02-01
Based on our previous work, this paper presents a dynamic hybrid framework, called DyHF, for solving constrained optimization problems. This framework consists of two major steps: global search model and local search model. In the global and local search models, differential evolution serves as the search engine, and Pareto dominance used in multiobjective optimization is employed to compare the individuals in the population. Unlike other existing methods, the above two steps are executed dynamically according to the feasibility proportion of the current population in this paper, with the purpose of reasonably distributing the computational resource for the global and local search during the evolution. The performance of DyHF is tested on 22 benchmark test functions. The experimental results clearly show that the overall performance of DyHF is highly competitive with that of a number of state-of-the-art approaches from the literature.
Time-optimal Trajectories for a Car-like Robot%车型机器人的时间最优轨迹
Institute of Scientific and Technical Information of China (English)
王慧芳; 陈阳舟
2008-01-01
This paper provides a new geometric method for achieving the sufficient family of the time-optimal trajectories to connect any two configurations of the robot in a 3-dimensional manifold based on the geometric optimal control theory. We provide a new perspective for analyzing this special type of nonlinear problems. Based on the structural characteristics of the switching functions and their derivatives from the Pontryagin's minimum principle (PMP) and the Lie algebra, we build a special coordinate system and introduce a new vector. We discover the one-to-one mapping between the rotation trajectory of this new vector and the optimal control trajectory. Furthermore, we define a switching vector that denotes the position and rotation direction of this vector, and reach a conclusion that the specified initial and final switching vectors can uniquely determine an optimal trajectory. In addition, it is the first time a condition that can be used directly for selecting a time-optimal trajectory is provided.
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The technique of cutting slabstone with stone-sawi ng machine is analyzed completely. A new kind of cutting movement trajectory is gi ven whose actual cutting efficiency is near to 100%. It can reduce the energy w earing greatly, and the surface quality of the product is improved to the utmost extent. The design mechanism of the optimal cutting movement trajectory system structure is analyzed incisively. At the same time, the principle of the complex movement of horizontal movement and swing is resear...
Directory of Open Access Journals (Sweden)
Fan Wu
2017-01-01
Full Text Available Trajectory simplification has become a research hotspot since it plays a significant role in the data preprocessing, storage, and visualization of many offline and online applications, such as online maps, mobile health applications, and location-based services. Traditional heuristic-based algorithms utilize greedy strategy to reduce time cost, leading to high approximation error. An Optimal Trajectory Simplification Algorithm based on Graph Model (OPTTS is proposed to obtain the optimal solution in this paper. Both min-# and min-ε problems are solved by the construction and regeneration of the breadth-first spanning tree and the shortest path search based on the directed acyclic graph (DAG. Although the proposed OPTTS algorithm can get optimal simplification results, it is difficult to apply in real-time services due to its high time cost. Thus, a new Online Trajectory Simplification Algorithm based on Directed Acyclic Graph (OLTS is proposed to deal with trajectory stream. The algorithm dynamically constructs the breadth-first spanning tree, followed by real-time minimizing approximation error and real-time output. Experimental results show that OPTTS reduces the global approximation error by 82% compared to classical heuristic methods, while OLTS reduces the error by 77% and is 32% faster than the traditional online algorithm. Both OPTTS and OLTS have leading superiority and stable performance on different datasets.
Chai, Runqi; Savvaris, Al; Tsourdos, Antonios; Chai, Senchun
2017-07-01
Highly constrained trajectory optimization for Space Manoeuvre Vehicles (SMV) is a challenging problem. In practice, this problem becomes more difficult when multiple mission requirements are taken into account. Because of the nonlinearity in the dynamic model and even the objectives, it is usually hard for designers to generate a compromised trajectory without violating strict path and box constraints. In this paper, a new multi-objective SMV optimal control model is formulated and parameterized using combined shooting-collocation technique. A modified game theory approach, coupled with an adaptive differential evolution algorithm, is designed in order to generate the pareto front of the multi-objective trajectory optimization problem. In addition, to improve the quality of obtained solutions, a control logic is embedded in the framework of the proposed approach. Several existing multi-objective evolutionary algorithms are studied and compared with the proposed method. Simulation results indicate that without driving the solution out of the feasible region, the proposed method can perform better in terms of convergence ability and convergence speed than its counterparts. Moreover, the quality of the pareto set generated using the proposed method is higher than other multi-objective evolutionary algorithms, which means the newly proposed algorithm is more attractive for solving multi-criteria SMV trajectory planning problem.
Ivanyukhin, A. V.; Petukhov, V. G.
2016-12-01
The problem of optimizing the interplanetary trajectories of a spacecraft (SC) with a solar electric propulsion system (SEPS) is examined. The problem of investigating the permissible power minimum of the solar electric propulsion power plant required for a successful flight is studied. Permissible ranges of thrust and exhaust velocity are analyzed for the given range of flight time and final mass of the spacecraft. The optimization is performed according to Portnyagin's maximum principle, and the continuation method is used for reducing the boundary problem of maximal principle to the Cauchy problem and to study the solution/ parameters dependence. Such a combination results in the robust algorithm that reduces the problem of trajectory optimization to the numerical integration of differential equations by the continuation method.
Directory of Open Access Journals (Sweden)
Sean Fritz
2017-01-01
Full Text Available This paper discusses the creation of a genetic algorithm to locate and optimize interplanetary trajectories using gravity assist maneuvers to improve fuel efficiency of the mission. The algorithm is implemented on two cases: (i a Centaur-class target close to the ecliptic plane and (ii a Centaur-class target with a high inclination to the ecliptic plane. Cases for multiple numbers of flybys (up to three are discussed and compared. It is shown that, for the targets considered here, a single flyby of Jupiter is the most efficient trajectory to either target with the conditions and limitations discussed in this paper. In this paper, we also iterate on possible reasons for certain results seen in the analysis and show how these previously observed behaviors could be present in any trajectory found. The parameters and methods used in the algorithm are explained and justified over multiple real-life interplanetary missions to provide deeper insights into the development choices.
Directory of Open Access Journals (Sweden)
David Schlipf
2015-11-01
Full Text Available Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as lidar, the information about the incoming wind field - the main disturbance to the system - can be made available ahead of time. Initial field testing of collective pitch feedforward control shows, that lidar measurements are only beneficial if they are filtered properly to avoid harmful control action. However, commercial lidar systems developed for site assessment are usually unable to provide a usable signal for real time control. Recent research shows, that the correlation between the measurement of rotor effective wind speed and the turbine reaction can be modeled and that the model can be used to optimize a scan pattern. This correlation depends on several criteria such as turbine size, position of the measurements, measurement volume, and how the wind evolves on its way towards the rotor. In this work the longitudinal wind evolution is identified with the line-of-sight measurements of a pulsed lidar system installed on a large commercial wind turbine. This is done by staring directly into the inflowing wind during operation of the turbine and fitting the coherence between the wind at different measurement distances to an exponential model taking into account the yaw misalignment, limitation to line-of-sight measurements and the pulse volume. The identified wind evolution is then used to optimize the scan trajectory of a scanning lidar for lidar-assisted feedforward control in order to get the best correlation possible within the constraints of the system. Further, an adaptive filer is fitted to the modeled correlation to avoid negative impact of feedforward control because of uncorrelated frequencies of the wind measurement. The main results of the presented work are a first estimate of the wind evolution in front of operating wind turbines and an approach which manufacturers of
Directory of Open Access Journals (Sweden)
Hao Zhu
2017-04-01
Full Text Available Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters. This paper presents a hybrid uncertainty-based design optimization (UDO method developed from probability theory and interval theory. Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory, while the others are defined as interval variables with interval theory. Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncertainty propagation from the design parameters to system responses. Three design optimization strategies, including deterministic design optimization (DDO, probabilistic UDO and hybrid UDO, are applied to the conceptual design of a hybrid rocket motor (HRM used as the ascent propulsion system in Apollo lunar module. By comparison, the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.
Optimal operation of hybrid-SITs under a SBO accident
Energy Technology Data Exchange (ETDEWEB)
Jeon, In Seop, E-mail: inseopjeon@kaist.ac.kr [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); Heo, Sun, E-mail: sunnysunny@khnp.co.kr [Central Research Institute, Korea Hydro & Nuclear Power Co., 70 Yuseong-daero 1312 beon-gil, Yuseong-gu, Daejeon 305-343 (Korea, Republic of); Kang, Hyun Gook, E-mail: hyungook@kaist.ac.kr [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of)
2016-02-15
Highlights: • Operation strategy of hybrid-SIT (H-SIT) in station blackout (SBO) is developed. • There are five main factors which have to be carefully treated in the development of the operation strategy. • Optimal value of each main factor is investigated analytically and then through thermal-hydraulic analysis using computer code. • The optimum operation strategy is suggested based on the optimal value of the main factors. - Abstract: A hybrid safety injection tank (H-SIT) is designed to enhance the capability of pressurized water reactors against high-pressure accidents which might be caused by the combined accidents accompanied by station blackout (SBO), and is suggested as a useful alternative to electricity-driven motor injection pumps. The main purpose of the H-SIT is to provide coolant to the core so that core safety can be maintained for a longer period. As H-SITs have a limited inventory, their efficient use in cooling down the core is paramount to maximize the available time for long-term cooling component restoration. Therefore, an optimum operation strategy must be developed to support the operators for the most efficient H-SIT use. In this study, the main factors which have to be carefully treated in the development of an operation strategy are first identified. Then the optimal value of each main factor is investigated analytically, a process useful to get the basis of the global optimum points. Based on these analytical optimum points, a thermal-hydraulic analysis using MARS code is performed to get more accurate values and to verify the results of the analytical study. The available time for long-term cooling component restoration is also estimated. Finally, an integrated optimum operation strategy for H-SITs in SBO is suggested.
Directory of Open Access Journals (Sweden)
Dębski Roman
2014-09-01
Full Text Available Effective, simulation-based trajectory optimization algorithms adapted to heterogeneous computers are studied with reference to the problem taken from alpine ski racing (the presented solution is probably the most general one published so far. The key idea behind these algorithms is to use a grid-based discretization scheme to transform the continuous optimization problem into a search problem over a specially constructed finite graph, and then to apply dynamic programming to find an approximation of the global solution. In the analyzed example it is the minimum-time ski line, represented as a piecewise-linear function (a method of elimination of unfeasible solutions is proposed. Serial and parallel versions of the basic optimization algorithm are presented in detail (pseudo-code, time and memory complexity. Possible extensions of the basic algorithm are also described. The implementation of these algorithms is based on OpenCL. The included experimental results show that contemporary heterogeneous computers can be treated as μ-HPC platforms-they offer high performance (the best speedup was equal to 128 while remaining energy and cost efficient (which is crucial in embedded systems, e.g., trajectory planners of autonomous robots. The presented algorithms can be applied to many trajectory optimization problems, including those having a black-box represented performance measure
Optimal design of near-Earth asteroid sample-return trajectories in the Sun-Earth-Moon system
He, Shengmao; Zhu, Zhengfan; Peng, Chao; Ma, Jian; Zhu, Xiaolong; Gao, Yang
2016-08-01
In the 6th edition of the Chinese Space Trajectory Design Competition held in 2014, a near-Earth asteroid sample-return trajectory design problem was released, in which the motion of the spacecraft is modeled in multi-body dynamics, considering the gravitational forces of the Sun, Earth, and Moon. It is proposed that an electric-propulsion spacecraft initially parking in a circular 200-km-altitude low Earth orbit is expected to rendezvous with an asteroid and carry as much sample as possible back to the Earth in a 10-year time frame. The team from the Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences has reported a solution with an asteroid sample mass of 328 tons, which is ranked first in the competition. In this article, we will present our design and optimization methods, primarily including overall analysis, target selection, escape from and capture by the Earth-Moon system, and optimization of impulsive and low-thrust trajectories that are modeled in multi-body dynamics. The orbital resonance concept and lunar gravity assists are considered key techniques employed for trajectory design. The reported solution, preliminarily revealing the feasibility of returning a hundreds-of-tons asteroid or asteroid sample, envisions future space missions relating to near-Earth asteroid exploration.
Optimal design of near-Earth asteroid sample-return trajectories in the Sun-Earth-Moon system
Institute of Scientific and Technical Information of China (English)
Shengmao He; Zhengfan Zhu; Chao Peng; Jian Ma; Xiaolong Zhu; Yang Gao
2016-01-01
In the 6th edition of the Chinese Space Trajec-tory Design Competition held in 2014, a near-Earth asteroid sample-return trajectory design problem was released, in which the motion of the spacecraft is modeled in multi-body dynamics, considering the gravitational forces of the Sun, Earth, and Moon. It is proposed that an electric-propulsion spacecraft initially parking in a circular 200-km-altitude low Earth orbit is expected to rendezvous with an asteroid and carry as much sample as possible back to the Earth in a 10-year time frame. The team from the Technology and Engi-neering Center for Space Utilization, Chinese Academy of Sciences has reported a solution with an asteroid sample mass of 328 tons, which is ranked first in the competition. In this article, we will present our design and optimization methods, primarily including overall analysis, target selec-tion, escape from and capture by the Earth–Moon system, and optimization of impulsive and low-thrust trajectories that are modeled in multi-body dynamics. The orbital res-onance concept and lunar gravity assists are considered key techniques employed for trajectory design. The reported solution, preliminarily revealing the feasibility of returning a hundreds-of-tons asteroid or asteroid sample, envisions future space missions relating to near-Earth asteroid explo-ration.
A Global Approach to the Optimal Trajectory Based on an Improved Ant Colony Algorithm for Cold Spray
Cai, Zhenhua; Chen, Tingyang; Zeng, Chunnian; Guo, Xueping; Lian, Huijuan; Zheng, You; Wei, Xiaoxu
2016-12-01
This paper is concerned with finding a global approach to obtain the shortest complete coverage trajectory on complex surfaces for cold spray applications. A slicing algorithm is employed to decompose the free-form complex surface into several small pieces of simple topological type. The problem of finding the optimal arrangement of the pieces is translated into a generalized traveling salesman problem (GTSP). Owing to its high searching capability and convergence performance, an improved ant colony algorithm is then used to solve the GTSP. Through off-line simulation, a robot trajectory is generated based on the optimized result. The approach is applied to coat real components with a complex surface by using the cold spray system with copper as the spraying material.
Parametric optimization of a new hybrid pneumatic-combustion engine
Energy Technology Data Exchange (ETDEWEB)
Higelin, P.; Vasile, I.; Charlet, A.; Chamaillard, Y. [Universite d' Orleans, LME-ESEM, Orleans (France)
2004-06-01
Although internal combustion engines display high overall maximum global efficiencies, this potential cannot be fully exploited in automotive applications: in real conditions, the average engine load (and thus efficiency) is quite low and the kinetic energy during a braking phase is lost. One solution to this problem is to switch to a new hybrid pneumatic-combustion engine concept, which is able to store energy in the form of compressed air. This energy can be issued from a braking phase or from a combustion phase at low power. The potential energy from the air tank can then be restored to start the engine, use the stored air to drive the engine as a pneumatic motor at low load or charge the engine at full load. Optimization of the compressed air tank maximum pressure and volume as well as the operating mode switching strategy provides an improvement in terms of fuel economy as high as 31 per cent if combined with engine downsizing. (Author)
Indoor Wireless Localization-hybrid and Unconstrained Nonlinear Optimization Approach
Directory of Open Access Journals (Sweden)
R. Jayabharathy
2013-07-01
Full Text Available In this study, a hybrid TOA/RSSI wireless localization is proposed for accurate positioning in indoor UWB systems. The major problem in indoor localization is the effect of Non-Line of Sight (NLOS propagation. To mitigate the NLOS effects, an unconstrained nonlinear optimization approach is utilized to process Time-of-Arrival (TOA and Received Signal Strength (RSS in the location system.TOA range measurements and path loss model are used to discriminate LOS and NLOS conditions. The weighting factors assigned by hypothesis testing, is used for solving the objective function in the proposed approach. This approach is used for describing the credibility of the TOA range measurement. Performance of the proposed technique is done based on MATLAB simulation. The result shows that the proposed technique performs well and achieves improved positioning under severe NLOS conditions.
Hybrid Neural Network and Support Vector Machine Method for Optimization
Rai, Man Mohan (Inventor)
2007-01-01
System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.
Optimal Control Policies of Pests for Hybrid Dynamical Systems
Directory of Open Access Journals (Sweden)
Baolin Kang
2013-01-01
Full Text Available We improve the traditional integrated pest management (IPM control strategies and formulate three specific management strategies, which can be described by hybrid dynamical systems. These strategies can not only effectively control pests but also reduce the abuse of pesticides and protect the natural enemies. The aim of this work is to study how the factors, such as natural enemies optimum choice in the two kinds of different pests, timings of natural enemy releases, dosages and timings of insecticide applications, and instantaneous killing rates of pesticides on both pests and natural enemies, can affect the success of IPM control programmes. The results indicate that the pests outbreak period or frequency largely depends on the optimal selective feeding of the natural enemy between one of the pests and the control tactics. Ultimately, we obtain the only pest needs to be controlled below a certain threshold while not supervising pest .
Hybrid vehicle system studies and optimized hydrogen engine design
Energy Technology Data Exchange (ETDEWEB)
Smith, J.R.; Aceves, S.
1995-04-26
We have done system studies of series hydrogen hybrid automobiles that approach the PNGV design goal of 34 km/liter (80 mpg), for 384 km (240 mi) and 608 km (380 mi) ranges. Our results indicate that such a vehicle appears feasible using an optimized hydrogen engine. We have evaluated the impact of various on-board storage options on fuel economy. Experiments in an available engine at the Sandia CRF demonstrated NO{sub x} emissions of 10 to 20 ppM at an equivalence ratio of 0.4, rising to about 500 ppm at 0.5 equivalence ratio using neat hydrogen. Hybrid simulation studies indicate that exhaust NO{sub x} concentrations must be less than 180 ppM to meet the 0.2 g/mile ULEV or Federal Tier II emissions regulations. LLNL has designed and fabricated a first generation optimized hydrogen engine head for use on an existing Onan engine. This head features 15:1 compression ratio, dual ignition, water cooling, two valves and open quiescent combustion chamber to minimize heat transfer losses. Initial testing shows promise of achieving an indicated efficiency of nearly 50% and emissions of less than 100 ppM NO{sub x}. Hydrocarbons and CO are to be measured, but are expected to be very low since their only source is engine lubricating oil. A successful friction reduction program on the Onan engine should result in a brake thermal efficiency of about 42% compared to today`s gasoline engines of 32%. Based on system studies requirements, the next generation engine will be about 2 liter displacement and is projected to achieve 46% brake thermal efficiency with outputs of 15 kW for cruise and 40 kW for hill climb.
Directory of Open Access Journals (Sweden)
Mohamed Zellagui
2017-09-01
Full Text Available The paper presents a new hybrid global optimization algorithm based on Chemical Reaction based Optimization (CRO and Di¤erential evolution (DE algorithm for nonlinear constrained optimization problems. This approach proposed for the optimal coordination and setting relays of directional overcurrent relays in complex power systems. In protection coordination problem, the objective function to be minimized is the sum of the operating time of all main relays. The optimization problem is subject to a number of constraints which are mainly focused on the operation of the backup relay, which should operate if a primary relay fails to respond to the fault near to it, Time Dial Setting (TDS, Plug Setting (PS and the minimum operating time of a relay. The hybrid global proposed optimization algorithm aims to minimize the total operating time of each protection relay. Two systems are used as case study to check the effeciency of the optimization algorithm which are IEEE 4-bus and IEEE 6-bus models. Results are obtained and presented for CRO and DE and hybrid CRO-DE algorithms. The obtained results for the studied cases are compared with those results obtained when using other optimization algorithms which are Teaching Learning-Based Optimization (TLBO, Chaotic Differential Evolution Algorithm (CDEA and Modiffied Differential Evolution Algorithm (MDEA, and Hybrid optimization algorithms (PSO-DE, IA-PSO, and BFOA-PSO. From analysing the obtained results, it has been concluded that hybrid CRO-DO algorithm provides the most optimum solution with the best convergence rate.
Directory of Open Access Journals (Sweden)
Dębski Roman
2016-06-01
Full Text Available A new dynamic programming based parallel algorithm adapted to on-board heterogeneous computers for simulation based trajectory optimization is studied in the context of “high-performance sailing”. The algorithm uses a new discrete space of continuously differentiable functions called the multi-splines as its search space representation. A basic version of the algorithm is presented in detail (pseudo-code, time and space complexity, search space auto-adaptation properties. Possible extensions of the basic algorithm are also described. The presented experimental results show that contemporary heterogeneous on-board computers can be effectively used for solving simulation based trajectory optimization problems. These computers can be considered micro high performance computing (HPC platforms-they offer high performance while remaining energy and cost efficient. The simulation based approach can potentially give highly accurate results since the mathematical model that the simulator is built upon may be as complex as required. The approach described is applicable to many trajectory optimization problems due to its black-box represented performance measure and use of OpenCL.
基于遗传算法的四自由度混联机器人轨迹规划%Trajectory Planning of Four DOF Hybrid Robot Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
陈小立; 严宏志; 温广旭
2014-01-01
研究机器人轨迹优化控制问题，为了兼顾机器人高速运动的平稳性与工作效率，以四自由度混联机器人为研究对象，提出了一种基于遗传算法的轨迹规划方案。运用坐标变换法建立混联机器人的运动学模型；在考虑机器人位置、速度、加速度约束的基础上，采用"3-5-3"法在关节空间对机器人进行轨迹规划。通过遗传算法对各路径点间的时间间隔进行最优规划，获得各关节的位置、速度、加速度轨迹图像。仿真结果表明，所设计的方案能保证机器人运行时间最优以及轨迹连续平滑。%In order to improve hybrid robot's productivity and get a smooth high-speed movement, a method for trajectory planning based on genetic algorithm was presented. The kinematic model of hybrid robot was established by coordinate transformation method;the robot's trajectory in joint space was planned by"3-5-3" method while consid-ering constraints of position,velocity and acceleration. In order to obtain the trajectory picture of each point's posi-tion, velocity and acceleration, the time intervals between each path point were planned with the genetic algorithm. The simulation results show that the method can make the working time optimal and the robot's trajectory smooth.
OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD
Directory of Open Access Journals (Sweden)
Dhananjay Kumar
2016-01-01
Full Text Available Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider’s resources aren’t enough to satisfy the customer’s demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO. These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.
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Qingxuan Jia
2016-01-01
Full Text Available Aiming at reducing joint velocity jumps caused by an unexpected joint-locked failure during space manipulator on-orbit operations without shutting down manipulator, trajectory optimization strategy considering the unexpectedness characteristics of joint-locked failure is proposed in the paper, which can achieve velocity jumps reduction in both operation space and joint space simultaneously. In the strategy, velocity in operation space concerning task completion directly is treated as equality constraints, and velocity in joint space concerning motion performance is treated as objective function. Global compensation vector which consists of coefficient, gradient of manipulability, and orthogonal matrix of null space is constructed to minimize the objective function. For each particular failure time, unique optimal coefficient can be obtained when the objective function is minimal. As a basis, a method for optimal coefficient function fitting is proposed based on a priori failure information (possible failure time and the corresponding optimal coefficient to guarantee the unexpectedness characteristics of joint-locked failure. Simulations are implemented to validate the efficiency of trajectory optimization strategy in reducing velocity jumps in both joint space and operation space. And the feasibility of coefficient function is also verified in reducing velocity jump no matter when joint-locked failure occurs.
Ellison, Donald H.; Englander, Jacob A.; Conway, Bruce A.
2017-01-01
The multiple gravity assist low-thrust (MGALT) trajectory model combines the medium-fidelity Sims-Flanagan bounded-impulse transcription with a patched-conics flyby model and is an important tool for preliminary trajectory design. While this model features fast state propagation via Keplers equation and provides a pleasingly accurate estimation of the total mass budget for the eventual flight suitable integrated trajectory it does suffer from one major drawback, namely its temporal spacing of the control nodes. We introduce a variant of the MGALT transcription that utilizes the generalized anomaly from the universal formulation of Keplers equation as a decision variable in addition to the trajectory phase propagation time. This results in two improvements over the traditional model. The first is that the maneuver locations are equally spaced in generalized anomaly about the orbit rather than time. The second is that the Kepler propagator now has the generalized anomaly as its independent variable instead of time and thus becomes an iteration-free propagation method. The new algorithm is outlined, including the impact that this has on the computation of Jacobian entries for numerical optimization, and a motivating application problem is presented that illustrates the improvements that this model has over the traditional MGALT transcription.
Taniai, Yoshiaki; Nishii, Jun
2015-08-01
When we move our body to perform a movement task, our central nervous system selects a movement trajectory from an infinite number of possible trajectories under constraints that have been acquired through evolution and learning. Minimization of the energy cost has been suggested as a potential candidate for a constraint determining locomotor parameters, such as stride frequency and stride length; however, other constraints have been proposed for a human upper-arm reaching task. In this study, we examined whether the minimum metabolic energy cost model can also explain the characteristics of the upper-arm reaching trajectories. Our results show that the optimal trajectory that minimizes the expected value of energy cost under the effect of signal-dependent noise on motor commands expresses not only the characteristics of reaching movements of typical speed but also those of slower movements. These results suggest that minimization of the energy cost would be a basic constraint not only in locomotion but also in upper-arm reaching.
Directory of Open Access Journals (Sweden)
José F. Herbert-Acero
2014-01-01
Full Text Available This work presents a novel framework for the aerodynamic design and optimization of blades for small horizontal axis wind turbines (WT. The framework is based on a state-of-the-art blade element momentum model, which is complemented with the XFOIL 6.96 software in order to provide an estimate of the sectional blade aerodynamics. The framework considers an innovative nested-hybrid solution procedure based on two metaheuristics, the virtual gene genetic algorithm and the simulated annealing algorithm, to provide a near-optimal solution to the problem. The objective of the study is to maximize the aerodynamic efficiency of small WT (SWT rotors for a wide range of operational conditions. The design variables are (1 the airfoil shape at the different blade span positions and the radial variation of the geometrical variables of (2 chord length, (3 twist angle, and (4 thickness along the blade span. A wind tunnel validation study of optimized rotors based on the NACA 4-digit airfoil series is presented. Based on the experimental data, improvements in terms of the aerodynamic efficiency, the cut-in wind speed, and the amount of material used during the manufacturing process were achieved. Recommendations for the aerodynamic design of SWT rotors are provided based on field experience.
Hybrid NN/SVM Computational System for Optimizing Designs
Rai, Man Mohan
2009-01-01
A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily
Directory of Open Access Journals (Sweden)
Jixiang Fan
2015-09-01
Full Text Available In this paper, a map-based optimal energy management strategy is proposed to improve the consumption economy of a plug-in parallel hybrid electric vehicle. In the design of the maps, which provide both the torque split between engine and motor and the gear shift, not only the current vehicle speed and power demand, but also the optimality based on the predicted trajectory of vehicle dynamics are considered. To seek the optimality, the equivalent consumption, which trades off the fuel and electricity usages, is chosen as the cost function. Moreover, in order to decrease the model errors in the process of optimization conducted in the discrete time domain, the variational integrator is employed to calculate the evolution of the vehicle dynamics. To evaluate the proposed energy management strategy, the simulation results performed on a professional GT-Suit simulator are demonstrated and the comparison to a real-time optimization method is also given to show the advantage of the proposed off-line optimization approach.
Directory of Open Access Journals (Sweden)
Cong Zhang
2015-01-01
Full Text Available Although both battery and super-capacitor are important power sources for hybrid electric vehicles, there is no accurate configuration theory to match the above two kinds of power sources which have significantly different characteristics on energy and power storage for the goal of making good use of their individual features without size wasting. In this paper, a new performance is presented that is used for analysis and optimal design method of battery and super-capacitor for hybrid energy storage system of a parallel hybrid electrical vehicle. In order to achieve optimal design with less consumption, the power-energy function is applied to establish direct mathematical relationship between demand power and the performance. During matching process, firstly, three typical operating conditions are chosen as the basis of design; secondly, the energy and power capacity evaluation methods for the parameters of battery and super-capacitor in hybrid energy storage system are proposed; thirdly, the mass, volume, and cost of the system are optimized simultaneously by using power-energy function. As a result, there are significant advantages on mass, volume, and cost for the hybrid energy storage system with the matching method. Simulation results fit well with the results of analysis, which confirms that the optimized design can meet the demand of hybrid electric vehicle well.
Hybrid Design Optimization of High Voltage Pulse Transformers for Klystron Modulators
Sylvain, Candolfi; Davide, Aguglia; Jerome, Cros
2015-01-01
This paper presents a hybrid optimization methodology for the design of high voltage pulse transformers used in klystron modulators. The optimization process is using simplified 2D FEA design models of the 3D transformer structure. Each intermediate optimal solution is evaluated by 3D FEA and correction coefficients of the 2D FEA models are derived. A new optimization process using 2D FEA models is then performed. The convergence of this hybrid optimal design methodology is obtained with a limited number of time consuming 3D FEA simulations. The method is applied to the optimal design of a monolithic high voltage pulse transformer for the CLIC klystron modulator.
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.
A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems
Institute of Scientific and Technical Information of China (English)
Yong WANG; Zixing CAI
2009-01-01
In the real-world applications, most optimization problems are subject to different types of constraints. These problems are known as constrained optimization problems (COPs). Solving COPs is a very important area in the optimization field. In this paper, a hybrid multi-swarm particle swarm optimization (HMPSO) is proposed to deal with COPs. This method adopts a parallel search operator in which the current swarm is partitioned into several subswarms and particle swarm optimization (PSO) is severed as the search engine for each sub-swarm. Moreover, in order to explore more promising regions of the search space, differential evolution (DE) is incorporated to improve the personal best of each particle. First, the method is tested on 13 benchmark test functions and compared with three stateof-the-art approaches. The simulation results indicate that the proposed HMPSO is highly competitive in solving the 13 benchmark test functions. Afterward, the effectiveness of some mechanisms proposed in this paper and the effect of the parameter setting were validated by various experiments. Finally, HMPSO is further applied to solve 24 benchmark test functions collected in the 2006 IEEE Congress on Evolutionary Computation (CEC2006) and the experimental results indicate that HMPSO is able to deal with 22 test functions.
Directory of Open Access Journals (Sweden)
Ranganathan Mohanasundaram
2015-01-01
Full Text Available The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.
A Comparative Study of Several Hybrid Particle Swarm Algorithms for Function Optimization
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Yanhua Zhong
2012-11-01
Full Text Available Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm optimization, GA particle swarm optimization, quantum particle swarm optimization and the standard particle swarm optimization to test with three objective functions. We compare evolutionary algorithm performance by a fixed number of iterations of the convergence speed and accuracy and the number of iterations under the fixed convergence precision; analyzing these types of hybrid particle swarm optimization results and practical performance. Test results show hybrid particle algorithm performance has improved significantly.
A Comparative Study of Several Hybrid Particle Swarm Algorithms for Function Optimization
Directory of Open Access Journals (Sweden)
Yanhua Zhong
2013-01-01
Full Text Available Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm optimization, GA particle swarm optimization, quantum particle swarm optimization and the standard particle swarm optimization to test with three objective functions. We compare evolutionary algorithm performance by a fixed number of iterations of the convergence speed and accuracy and the number of iterations under the fixed convergence precision, analyzing these types of hybrid particle swarm optimization results and practical performance. Test results show hybrid particle algorithm performance has improved significantly.
Chen, Robert T. N.; Zhao, Yi-Yuan; Aiken, Edwin W. (Technical Monitor)
1995-01-01
Engine failure represents a major safety concern to helicopter operations, especially in the critical flight phases of takeoff and landing from/to small, confined areas. As a result, the JAA and FAA both certificate a transport helicopter as either Category-A or Category-B according to the ability to continue its operations following engine failures. A Category-B helicopter must be able to land safely in the event of one or all engine failures. There is no requirement, however, for continued flight capability. In contrast, Category-A certification, which applies to multi-engine transport helicopters with independent engine systems, requires that they continue the flight with one engine inoperative (OEI). These stringent requirements, while permitting its operations from rooftops and oil rigs and flight to areas where no emergency landing sites are available, restrict the payload of a Category-A transport helicopter to a value safe for continued flight as well as for landing with one engine inoperative. The current certification process involves extensive flight tests, which are potentially dangerous, costly, and time consuming. These tests require the pilot to simulate engine failures at increasingly critical conditions, Flight manuals based on these tests tend to provide very conservative recommendations with regard to maximum takeoff weight or required runway length. There are very few theoretical studies on this subject to identify the fundamental parameters and tradeoff factors involved. Furthermore, a capability for real-time generation of OEI optimal trajectories is very desirable for providing timely cockpit display guidance to assist the pilot in reducing his workload and to increase safety in a consistent and reliable manner. A joint research program involving NASA Ames Research Center, the FAA, and the University of Minnesota is being conducted to determine OEI optimal control strategies and the associated optimal,trajectories for continued takeoff (CTO
Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar
2016-09-09
A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm.
Verma, Harish Kumar; Jain, Cheshta
2016-09-01
In this article, a hybrid algorithm of particle swarm optimization (PSO) with statistical parameter (HSPSO) is proposed. Basic PSO for shifted multimodal problems have low searching precision due to falling into a number of local minima. The proposed approach uses statistical characteristics to update the velocity of the particle to avoid local minima and help particles to search global optimum with improved convergence. The performance of the newly developed algorithm is verified using various standard multimodal, multivariable, shifted hybrid composition benchmark problems. Further, the comparative analysis of HSPSO with variants of PSO is tested to control frequency of hybrid renewable energy system which comprises solar system, wind system, diesel generator, aqua electrolyzer and ultra capacitor. A significant improvement in convergence characteristic of HSPSO algorithm over other variants of PSO is observed in solving benchmark optimization and renewable hybrid system problems.
Modeling, hybridization, and optimal charging of electrical energy storage systems
Parvini, Yasha
The rising rate of global energy demand alongside the dwindling fossil fuel resources has motivated research for alternative and sustainable solutions. Within this area of research, electrical energy storage systems are pivotal in applications including electrified vehicles, renewable power generation, and electronic devices. The approach of this dissertation is to elucidate the bottlenecks of integrating supercapacitors and batteries in energy systems and propose solutions by the means of modeling, control, and experimental techniques. In the first step, the supercapacitor cell is modeled in order to gain fundamental understanding of its electrical and thermal dynamics. The dependence of electrical parameters on state of charge (SOC), current direction and magnitude (20-200 A), and temperatures ranging from -40°C to 60°C was embedded in this computationally efficient model. The coupled electro-thermal model was parameterized using specifically designed temporal experiments and then validated by the application of real world duty cycles. Driving range is one of the major challenges of electric vehicles compared to combustion vehicles. In order to shed light on the benefits of hybridizing a lead-acid driven electric vehicle via supercapacitors, a model was parameterized for the lead-acid battery and combined with the model already developed for the supercapacitor, to build the hybrid battery-supercapacitor model. A hardware in the loop (HIL) setup consisting of a custom built DC/DC converter, micro-controller (muC) to implement the power management strategy, 12V lead-acid battery, and a 16.2V supercapacitor module was built to perform the validation experiments. Charging electrical energy storage systems in an efficient and quick manner, motivated to solve an optimal control problem with the objective of maximizing the charging efficiency for supercapacitors, lead-acid, and lithium ion batteries. Pontryagins minimum principle was used to solve the problems
Improved Aerodynamic Analysis for Hybrid Wing Body Conceptual Design Optimization
Gern, Frank H.
2012-01-01
This paper provides an overview of ongoing efforts to develop, evaluate, and validate different tools for improved aerodynamic modeling and systems analysis of Hybrid Wing Body (HWB) aircraft configurations. Results are being presented for the evaluation of different aerodynamic tools including panel methods, enhanced panel methods with viscous drag prediction, and computational fluid dynamics. Emphasis is placed on proper prediction of aerodynamic loads for structural sizing as well as viscous drag prediction to develop drag polars for HWB conceptual design optimization. Data from transonic wind tunnel tests at the Arnold Engineering Development Center s 16-Foot Transonic Tunnel was used as a reference data set in order to evaluate the accuracy of the aerodynamic tools. Triangularized surface data and Vehicle Sketch Pad (VSP) models of an X-48B 2% scale wind tunnel model were used to generate input and model files for the different analysis tools. In support of ongoing HWB scaling studies within the NASA Environmentally Responsible Aviation (ERA) program, an improved finite element based structural analysis and weight estimation tool for HWB center bodies is currently under development. Aerodynamic results from these analyses are used to provide additional aerodynamic validation data.
Beam Pattern Synthesis Based on Hybrid Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
YU Yan-li; WANG Ying-min; LI Lei
2010-01-01
As conventional methods for beam pattern synthesis can not always obtain the desired optimum pattern for the arbitrary underwater acoustic sensor arrays, a hybrid numerical synthesis method based on adaptive principle and genetic algorithm was presented in this paper. First, based on the adaptive theory, a given array was supposed as an adaptive array and its sidelobes were reduced by assigning a number of interference signals in the sidelobe region. An initial beam pattern was obtained after several iterations and adjustments of the interference intensity, and based on its parameters, a desired pattern was created. Then, an objective function based on the difference between the designed and desired patterns can be constructed. The pattern can be optimized by using the genetic algorithm to minimize the objective function. A design example for a double-circular array demonstrates the effectiveness of this method. Compared with the approaches existing before, the proposed method can reduce the sidelobe effectively and achieve less synthesis magnitude error in the mainlobe.The method can search for optimum attainable pattern for the specific elements if the desired pattern can not be found.
Optimal State Estimation of Ballistic Trajectories with Angle-Only Measurements
1979-01-24
An iterative least square estimation algorithm is dervied and applied to the problem of state estimation of ballistic trajectories with angle-only... least square filter achieves the Cramer-Rao bound and it performs better than the extended Kalman filter when the sensor is on a free-falling platform
Optimal planning of LEO active debris removal based on hybrid optimal control theory
Yu, Jing; Chen, Xiao-qian; Chen, Li-hu
2015-06-01
The mission planning of Low Earth Orbit (LEO) active debris removal problem is studied in this paper. Specifically, the Servicing Spacecraft (SSc) and several debris exist on near-circular near-coplanar LEOs. The SSc should repeatedly rendezvous with the debris, and de-orbit them until all debris are removed. Considering the long-duration effect of J2 perturbation, a linear dynamics model is used for each rendezvous. The purpose of this paper is to find the optimal service sequence and rendezvous path with minimum total rendezvous cost (Δv) for the whole mission, and some complex constraints (communication time window constraint, terminal state constraint, and time distribution constraint) should be satisfied meanwhile. Considering this mission as a hybrid optimal control problem, a mathematical model is proposed, as well as the solution method. The proposed approach is demonstrated by a typical active debris removal problem. Numerical experiments show that (1) the model and solution method proposed in this paper can effectively address the planning problem of LEO debris removal; (2) the communication time window constraint and the J2 perturbation have considerable influences on the optimization results; and (3) under the same configuration, some suboptimal sequences are equivalent to the optimal one since their difference in Δv cost is very small.
Jackson, Mark Charles
1994-01-01
Spacecraft proximity operations are complicated by the fact that exhaust plume impingement from the reaction control jets of space vehicles can cause structural damage, contamination of sensitive arrays and instruments, or attitude misalignment during docking. The occurrence and effect of jet plume impingement can be reduced by planning approach trajectories with plume effects considered. An A* node search is used to find plume-fuel optimal trajectories through a discretized six dimensional attitude-translation space. A plume cost function which approximates jet plume isopressure envelopes is presented. The function is then applied to find relative costs for predictable 'trajectory altering' firings and unpredictable 'deadbanding' firings. Trajectory altering firings are calculated by running the spacecraft jet selection algorithm and summing the cost contribution from each jet fired. A 'deadbanding effects' function is defined and integrated to determine the potential for deadbanding impingement along candidate trajectories. Plume costs are weighed against fuel costs in finding the optimal solution. A* convergence speed is improved by solving approach trajectory problems in reverse time. Results are obtained on a high fidelity space shuttle/space station simulation. Trajectory following is accomplished by a six degree of freedom autopilot. Trajectories planned with, and without, plume costs are compared in terms of force applied to the target structure.
Optimal Trajectory Planning for Glass-Handing Robot Based on Execution Time Acceleration and Jerk
Directory of Open Access Journals (Sweden)
Honggang Duan
2016-01-01
Full Text Available This study describes a trajectory planning method based on execution time, acceleration, and jerk to ensure that a glass-handing robot runs smoothly at execution time. The minimised objective function consists of the weighted sum of the square of the integral of the execution time, the integral of the acceleration, and the integral of the jerk, all of which are obtained through the weighted coefficient method. A three-dimensional kinematics model of the glass-handing robot is then established and nonuniform fifth-order B-splines are used to interpolate its path points. The acceleration and jerk are expressed as functions of time through mathematical simulation. Simulation results show that the designed method for robot trajectory planning not only improves the working efficiency of the glass-handing robot but also ensures that it runs smoothly.
Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method
Institute of Scientific and Technical Information of China (English)
Eysa Salajegheh; Saeed Gholizadeh; Mohsen Khatibina
2008-01-01
The optimal seismic design of structures requires that time history analyses (THA) be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function (RBF) neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization (BPSO) is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.
Application of parallel algorithmic differentiation to optimal CubeSat trajectory design
Ghosh, Alexander; Coverstone, Victoria
2017-01-01
CubeSats, the class of small standardized satellites, are becoming a viable scientific research platform. At present, a variety of high value Earth Science missions require multiple collecting instruments on separate platforms maintained in precise configurations. A new software tool was created to compute propellant-minimizing maneuvers for multiple CubeSats for use with mission preliminary design. This tool incorporates parallelization of the derivative calculations, and demonstrates speed improvements over previous parallel formulations of small satellite cooperative trajectory design problems.
CONGESTION MANAGEMENT BY OPTIMAL ALLOCATION OF FACTS CONTROLLERS USING HYBRID FISH BEE OPTIMIZATION
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S. Thangalakshmi
2014-01-01
Full Text Available The role of Independent System Operator (ISO in the restructured power industry includes system control, capacity planning, transmission tariff and congestion management; the challenging task being minimizing the congestion. One of the popular techniques used to alleviate congestion is using Flexible AC Transmission Systems (FACTS devices. The power system generally operates near its rated capacity in deregulated market because of intensive usage of transmission grids. So, the major issues that need to be addressed are improving the voltage profile and reducing the power loss in the electrical network. Motivation: The location of FACTS devices can improve the power flow in the line, maintain the bus profile and reduce the losses. However locating the ideal location is a NP problem. This study presents a novel heuristic method to determine the types of FACTS devices and its optimal location in a power system without violating the thermal and voltage limits. Power flow sensitivity index to find the optimal location of UPFC is suggested in this study. A hybrid fish bee swarm optimization is proposed which is based on Artificial Bee Colony (ABC and Fish School Search (FSS methods. This proposed algorithm is tested based on IEEE 30 bus system and line performances are studied.
Institute of Scientific and Technical Information of China (English)
CHEN Jie; XIN Bin; PENG ZhiHong; PAN Feng
2009-01-01
This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics-differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.
Energy and Propulsion Optimization of Solid-Propellant Grain of a Hybrid Power Device
Bondarchuk Sergey S.; Bondarchuk Iliya S.; Borisov Boris V.; Zhukov Alexandr S.
2016-01-01
A method of distribution of an additional solid-phase component (oxidizer) providing uniformity of grain burning for the purpose of evaluation and optimization of energy and propulsion parameters of hybrid solid-propellant motor is proposed in the paper.
Energy and Propulsion Optimization of Solid-Propellant Grain of a Hybrid Power Device
Bondarchuk, Sergey S.; Bondarchuk, Iliya S.; Borisov, Boris V.; Zhukov, Alexandr S.
2016-02-01
A method of distribution of an additional solid-phase component (oxidizer) providing uniformity of grain burning for the purpose of evaluation and optimization of energy and propulsion parameters of hybrid solid-propellant motor is proposed in the paper.
POWER OPTIMIZED DATAPATH UNITS OF HYBRID EMBEDDED CORE ARCHITECTURE USING CLOCK GATING TECHNIQUE
National Research Council Canada - National Science Library
T.Subhashini; M.Kamaraju
2015-01-01
...% of the total power dissipation. The main goal of this work is to implement a prototype power optimized datapath unit and ALU of Hybrid Embedded Controller Architecture targeted on to the FPGA chip and analyze the power consumption...
Optimization of Hybrid PV/Wind Energy System Using Genetic Algorithm (GA
Directory of Open Access Journals (Sweden)
Satish Kumar Ramoji
2014-01-01
Full Text Available In this paper, a new approach of optimum design for a Hybrid PV/Wind energy system is presented in order to assist the designers to take into consideration both the economic and ecological aspects. When the stand alone energy system having photovoltaic panels only or wind turbine only are compared with the hybrid PV/wind energy systems, the hybrid systems are more economical and reliable according to climate changes. This paper presents an optimization technique to design the hybrid PV/wind system. The hybrid system consists of photovoltaic panels, wind turbines and storage batteries. Genetic Algorithm (GA optimization technique is utilized to minimize the formulated objective function, i.e. total cost which includes initial costs, yearly replacement cost, yearly operating costs and maintenance costs and salvage value of the proposed hybrid system. A computer program is designed, using MATLAB code to formulate the optimization problem by computing the coefficients of the objective function. The method mentioned in this article is proved to be effective using an example of hybrid energy system. Finally, the optimal solution is achieved by Genetic Algorithm (GA optimization method.
Xu, Y; Li, N
2014-09-01
Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.
Momoh, James; Chattopadhyay, Deb; Basheer, Omar Ali AL
1996-01-01
The space power system has two sources of energy: photo-voltaic blankets and batteries. The optimal power management problem on-board has two broad operations: off-line power scheduling to determine the load allocation schedule of the next several hours based on the forecast of load and solar power availability. The nature of this study puts less emphasis on speed requirement for computation and more importance on the optimality of the solution. The second category problem, on-line power rescheduling, is needed in the event of occurrence of a contingency to optimally reschedule the loads to minimize the 'unused' or 'wasted' energy while keeping the priority on certain type of load and minimum disturbance of the original optimal schedule determined in the first-stage off-line study. The computational performance of the on-line 'rescheduler' is an important criterion and plays a critical role in the selection of the appropriate tool. The Howard University Center for Energy Systems and Control has developed a hybrid optimization-expert systems based power management program. The pre-scheduler has been developed using a non-linear multi-objective optimization technique called the Outer Approximation method and implemented using the General Algebraic Modeling System (GAMS). The optimization model has the capability of dealing with multiple conflicting objectives viz. maximizing energy utilization, minimizing the variation of load over a day, etc. and incorporates several complex interaction between the loads in a space system. The rescheduling is performed using an expert system developed in PROLOG which utilizes a rule-base for reallocation of the loads in an emergency condition viz. shortage of power due to solar array failure, increase of base load, addition of new activity, repetition of old activity etc. Both the modules handle decision making on battery charging and discharging and allocation of loads over a time-horizon of a day divided into intervals of 10
Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational complexity. A new hybrid approximation algorithm is developed in this work to solve the problem. In the hybrid algorithm, discrete particle swarm optimization (DPSO) combines global search and local search to search for the optimal results and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The computational study showed that the proposed algorithm is a feasible and effective approach for capacitated vehicle routing problem, especially for large scale problems.
A Hybrid Intelligent Algorithm for Optimal Birandom Portfolio Selection Problems
Directory of Open Access Journals (Sweden)
Qi Li
2014-01-01
Full Text Available Birandom portfolio selection problems have been well developed and widely applied in recent years. To solve these problems better, this paper designs a new hybrid intelligent algorithm which combines the improved LGMS-FOA algorithm with birandom simulation. Since all the existing algorithms solving these problems are based on genetic algorithm and birandom simulation, some comparisons between the new hybrid intelligent algorithm and the existing algorithms are given in terms of numerical experiments, which demonstrate that the new hybrid intelligent algorithm is more effective and precise when the numbers of the objective function computations are the same.
2009-09-01
solving Optimal Control problems is found in [57, 58]. Many other numerical solvers exist, such as FSQP [59], NPSOL [60], and routines in the NAG...clarity). Figure 49. Obstacle Avoidance Problem with 0.5m buffer. 88 3. Computational Complexity The biggest concern in solving Optimal Control problems is...56] I.M. Ross, A Beginner’s Guide to DIDO: A MATLAB Application Package for Solving Optimal Control Problems , Elissar Technical
SFC Optimization for Aero Engine Based on Hybrid GA-SQP Method
Li, Jie; Fan, Ding; Sreeram, Victor
2013-12-01
This study focuses on on-line specific fuel consumption (SFC) optimization of aero engines. For solving this optimization problem, a nonlinear pneumatic and thermodynamics model of the aero engine is built and a hybrid optimization technique which is formed by combining the genetic algorithm (GA) and the sequential quadratic programming (SQP) is presented. The ability of standard GA and standard SQP in solving this type of problem is investigated. It has been found that, although the SQP is fast, very little SFC reductions can be obtained. The GA is able to solve the problem well but a lot of computational time is needed. The presented hybrid GA-SQP gives a good SFC optimization effect and saves 76.6% computational time when compared to the standard GA. It has been shown that the hybrid GA-SQP is a more effective and higher real-time method for SFC on-line optimization of the aero engine.
Energy Technology Data Exchange (ETDEWEB)
Centioli, C. [Associazione Euratom/ENEA Sulla Fusione, Centro Ricerche Frascati, Via E. Fermi 45, CP 65, 00044 Frascati, Rome (Italy); Iannone, F. [Associazione Euratom/ENEA Sulla Fusione, Centro Ricerche Frascati, Via E. Fermi 45, CP 65, 00044 Frascati, Rome (Italy); Mazza, G. [Associazione Euratom/ENEA Sulla Fusione, Centro Ricerche Frascati, Via E. Fermi 45, CP 65, 00044 Frascati, Rome (Italy); Panella, M. [Associazione Euratom/ENEA Sulla Fusione, Centro Ricerche Frascati, Via E. Fermi 45, CP 65, 00044 Frascati, Rome (Italy); Pangione, L. [Dipartimento di Informatica, Sistemi e Produzione, Universita di Roma, Tor Vergata, Via del Politecnico 1, 00133 Rome (Italy)]. E-mail: pangione@frascati.enea.it; Podda, S. [Associazione Euratom/ENEA Sulla Fusione, Centro Ricerche Frascati, Via E. Fermi 45, CP 65, 00044 Frascati, Rome (Italy); Tuccillo, A. [Associazione Euratom/ENEA Sulla Fusione, Centro Ricerche Frascati, Via E. Fermi 45, CP 65, 00044 Frascati, Rome (Italy); Vitale, V. [Associazione Euratom/ENEA Sulla Fusione, Centro Ricerche Frascati, Via E. Fermi 45, CP 65, 00044 Frascati, Rome (Italy); Zaccarian, L. [Dipartimento di Informatica, Sistemi e Produzione, Universita di Roma, Tor Vergata, Via del Politecnico 1, 00133 Rome (Italy)
2005-11-15
In this paper, we will report on the experimental results arising from the implementation of optimization techniques to maximize the RF power coupling versus the plasma conditions in the FTU experimental facility. These experiments are carried out by employing the open-source Linux-RTAI control system currently running on the FTU digital feedback loop. The RF power source under consideration is a lower hybrid system (LH) based on six gyrotrons with a nominal power output capability of 1.1 MW each. The optimization of the coupling level between the plasma and the emitting antenna reduces the reflected power, thus maximizing the heating effects in addition to avoiding danger to the emitter (equivalently, annoying safety shutdowns of the system). To this aim, the plasma displacement is modified by suitably adjusting the reference input to the stabilizing feedback, according to a steepest descent algorithm. It will be shown in the paper how this algorithm achieves a satisfactory level of robustness with respect to measurement errors and well performs both in simulation and in experimental tests, thus leading to an improved effectiveness of the RF heating system.
Optimization of the fission--fusion hybrid concept
Energy Technology Data Exchange (ETDEWEB)
Saltmarsh, M.J.; Grimes, W.R.; Santoro, R.T.
1979-04-01
One of the potentially attractive applications of controlled thermonuclear fusion is the fission--fusion hybrid concept. In this report we examine the possible role of the hybrid as a fissile fuel producer. We parameterize the advantages of the concept in terms of the performance of the fusion device and the breeding blanket and discuss some of the more troublesome features of existing design studies. The analysis suggests that hybrids based on deuterium--tritium (D--T) fusion devices are unlikely to be economically attractive and that they present formidable blanket technology problems. We suggest an alternative approach based on a semicatalyzed deuterium--deuterium (D--D) fusion reactor and a molten salt blanket. This concept is shown to emphasize the desirable features of the hybrid, to have considerably greater economic potential, and to mitigate many of the disadvantages of D--T-based systems.
Approximate dynamic programming recurrence relations for a hybrid optimal control problem
Lu, W.; Ferrari, S.; Fierro, R.; Wettergren, T. A.
2012-06-01
This paper presents a hybrid approximate dynamic programming (ADP) method for a hybrid dynamic system (HDS) optimal control problem, that occurs in many complex unmanned systems which are implemented via a hybrid architecture, regarding robot modes or the complex environment. The HDS considered in this paper is characterized by a well-known three-layer hybrid framework, which includes a discrete event controller layer, a discrete-continuous interface layer, and a continuous state layer. The hybrid optimal control problem (HOCP) is to nd the optimal discrete event decisions and the optimal continuous controls subject to a deterministic minimization of a scalar function regarding the system state and control over time. Due to the uncertainty of environment and complexity of the HOCP, the cost-to-go cannot be evaluated before the HDS explores the entire system state space; as a result, the optimal control, neither continuous nor discrete, is not available ahead of time. Therefore, ADP is adopted to learn the optimal control while the HDS is exploring the environment, because of the online advantage of ADP method. Furthermore, ADP can break the curses of dimensionality which other optimizing methods, such as dynamic programming (DP) and Markov decision process (MDP), are facing due to the high dimensions of HOCP.
Stability Optimization of a Disc Brake System with Hybrid Uncertainties for Squeal Reduction
Directory of Open Access Journals (Sweden)
Hui Lü
2016-01-01
Full Text Available A hybrid uncertain model is introduced to deal with the uncertainties existing in a disc brake system in this paper. By the hybrid uncertain model, the uncertain parameters of the brake with enough sampling data are treated as probabilistic variables, while the uncertain parameters with limited data are treated as interval probabilistic variables whose distribution parameters are expressed as interval variables. Based on the hybrid uncertain model, the reliability-based design optimization (RBDO of a disc brake with hybrid uncertainties is proposed to explore the optimal design for squeal reduction. In the optimization, the surrogate model of the real part of domain unstable eigenvalue of the brake system is established, and the upper bound of its expectation is adopted as the optimization objective. The lower bounds of the functions related to system stability, the mass, and the stiffness of design component are adopted as the optimization constraints. The combinational algorithm of Genetic Algorithm and Monte-Carlo method is employed to perform the optimization. The results of a numerical example demonstrate the effectiveness of the proposed optimization on improving system stability and reducing squeal propensity of a disc brake under hybrid uncertainties.
2007-09-01
Guidance, Control, and Dynamics, 21(2):193–207, March-April 1998. 7. Betts, John T. “A Direct Approach to Solving Optimal Control Problems ,” CSE in...Paul. “Jacobi Pseudospectral Method for Solving Optimal Control Problems ,” Journal of Guidance, Control, and Dynamics, 27(2):293–297, 2004. 117
2013-08-01
dynamic pressure, time- rate of change of flight path angle, loads and a terminal phase target. Furthermore, the optimal control problem uses derived... rate of change of altitude. The optimal control variables are specified to be the guidance variable derivatives; this allows for constraining attitude
Trehan, Sumeet; Durlofsky, Louis J.
2016-12-01
A new reduced-order model based on trajectory piecewise quadratic (TPWQ) approximations and proper orthogonal decomposition (POD) is introduced and applied for subsurface oil-water flow simulation. The method extends existing techniques based on trajectory piecewise linear (TPWL) approximations by incorporating second-derivative terms into the reduced-order treatment. Both the linear and quadratic reduced-order methods, referred to as POD-TPWL and POD-TPWQ, entail the representation of new solutions as expansions around previously simulated high-fidelity (full-order) training solutions, along with POD-based projection into a low-dimensional space. POD-TPWQ entails significantly more offline preprocessing than POD-TPWL as it requires generating and projecting several third-order (Hessian-type) terms. The POD-TPWQ method is implemented for two-dimensional systems. Extensive numerical results demonstrate that it provides consistently better accuracy than POD-TPWL, with speedups of about two orders of magnitude relative to high-fidelity simulations for the problems considered. We demonstrate that POD-TPWQ can be used as an error estimator for POD-TPWL, which motivates the development of a trust-region-based optimization framework. This procedure uses POD-TPWL for fast function evaluations and a POD-TPWQ error estimator to determine when retraining, which entails a high-fidelity simulation, is required. Optimization results for an oil-water problem demonstrate the substantial speedups that can be achieved relative to optimizations based on high-fidelity simulation.
A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization
Sessarego, M.; Dixon, K. R.; Rival, D. E.; Wood, D. H.
2015-08-01
A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.
Delta 并联机器人抓放轨迹优化%Delta Parallel Robot Pick and Place Trajectory Optimization
Institute of Scientific and Technical Information of China (English)
李云辉
2016-01-01
On the basis of Delta parallel robot trajectory of gate-shaped picking and placing,we described the method of trajectory planning and gate-shaped path synthesis by using the sinusoidal correcting keystone acceleration curve.From the perspective of mechanical vibration and shock reduction mechanism,taking the initiative to reduce Delta robot arm maximum angular shaft as the goal,the relevant operating parameters of trajectory were optimized.For the presence of jerk mutations on the acceleration curve approach to planning the start and end-side movement,resulting in a flexible shock problem,it is proposed that modified sinusoidal correcting keystone acceleration curve,making the entire operating cycle jerk curve become continuous,eliminating the system flexible impact.%在 Delta 并联机器人门形抓放轨迹的基础上，阐述了采用正弦修正梯形加速度曲线进行轨迹规划和门形路径合成的方法。从减小机构震动和冲击的角度出发，以降低 Delta 机器人主动臂驱动轴端的最大角加速度为目标，对门形轨迹的相关运行参数进行了优化。针对正弦修正梯形加速度曲线规划方式中运动始末端存在跃度突变，导致柔性冲击的问题，提出了改进型的正弦修正梯形加速度曲线，使得整个运行周期内跃度曲线变得连续，消除了系统的柔性冲击。
A Hybrid Ant Colony Optimization for the Prediction of Protein Secondary Structure
Institute of Scientific and Technical Information of China (English)
Chao CHEN; Yuan Xin TIAN; Xiao Yong ZOU; Pei Xiang CAI; Jin Yuan MO
2005-01-01
Based on the concept of ant colony optimization and the idea of population in genetic algorithm, a novel global optimization algorithm, called the hybrid ant colony optimization (HACO), is proposed in this paper to tackle continuous-space optimization problems. It was compared with other well-known stochastic methods in the optimization of the benchmark functions and was also used to solve the problem of selecting appropriate dilation efficiently by optimizing the wavelet power spectrum of the hydrophobic sequence of protein, which is thc key step on using continuous wavelet transform (CWT) to predict a-helices and connecting peptides.
Calise, A. J.; Flandro, G. A.; Corban, J. E.
1990-01-01
General problems associated with on-board trajectory optimization, propulsion system cycle selection, and with the synthesis of guidance laws were addressed for an ascent to low-earth-orbit of an air-breathing single-stage-to-orbit vehicle. The NASA Generic Hypersonic Aerodynamic Model Example and the Langley Accelerator aerodynamic sets were acquired and implemented. Work related to the development of purely analytic aerodynamic models was also performed at a low level. A generic model of a multi-mode propulsion system was developed that includes turbojet, ramjet, scramjet, and rocket engine cycles. Provisions were made in the dynamic model for a component of thrust normal to the flight path. Computational results, which characterize the nonlinear sensitivity of scramjet performance to changes in vehicle angle of attack, were obtained and incorporated into the engine model. Additional trajectory constraints were introduced: maximum dynamic pressure; maximum aerodynamic heating rate per unit area; angle of attack and lift limits; and limits on acceleration both along and normal to the flight path. The remainder of the effort focused on required modifications to a previously derived algorithm when the model complexity cited above was added. In particular, analytic switching conditions were derived which, under appropriate assumptions, govern optimal transition from one propulsion mode to another for two cases: the case in which engine cycle operations can overlap, and the case in which engine cycle operations are mutually exclusive. The resulting guidance algorithm was implemented in software and exercised extensively. It was found that the approximations associated with the assumed time scale separation employed in this work are reasonable except over the Mach range from roughly 5 to 8. This phenomenon is due to the very large thrust capability of scramjets in this Mach regime when sized to meet the requirement for ascent to orbit. By accounting for flight path
Simulation modeling and tracing optimal trajectory of robotic mining machine effector
Fryanov, VN; Pavlova, LD
2017-02-01
Within the framework of the robotic coal mine design for deep-level coal beds with the high gas content in the seismically active areas in the southern Kuzbass, the motion path parameters for an effector of a robotic mining machine are evaluated. The simulation model is meant for selection of minimum energy-based optimum trajectory for the robot effector, calculation of stresses and strains in a coal bed in a variable perimeter shortwall in the course of coal extraction, determination of coordinates of a coal bed edge area with the maximum disintegration of coal, and for choice of direction of the robot effector to get in contact with the mentioned area and to break coal at the minimum energy input. It is suggested to use the model in the engineering of the robot intelligence.
DEFF Research Database (Denmark)
Liu, Chengxi; Qin, Nan; Bak, Claus Leth;
2015-01-01
This paper proposes a hybrid optimization method to optimally control the voltage and reactive power with minimum power loss in transmission grid. This approach is used for the Danish automatic voltage control (AVC) system which is typically a non-linear non-convex problem mixed with both continu...
Keulen, T. van; Mullem, D. van; Jager, B. van; Kessels, J.T.B.A.; Steinbuch, M.
2012-01-01
Hybrid electric vehicles require an algorithm that controls the power split between the internal combustion engine and electric machine(s), and the opening and closing of the clutch. Optimal control theory is applied to derive a methodology for a real-time optimal-control-based power split
Directory of Open Access Journals (Sweden)
Weichao Zhuang
2016-05-01
Full Text Available Hybrid powertrain technologies are successful in the passenger car market and have been actively developed in recent years. Optimal topology selection, component sizing, and controls are required for competitive hybrid vehicles, as multiple goals must be considered simultaneously: fuel efficiency, emissions, performance, and cost. Most of the previous studies explored these three design dimensions separately. In this paper, two novel frameworks combining these three design dimensions together are presented and compared. One approach is nested optimization which searches through the whole design space exhaustively. The second approach is called enhanced iterative optimization, which executes the topology optimization and component sizing alternately. A case study shows that the later method can converge to the global optimal design generated from the nested optimization, and is much more computationally efficient. In addition, we also address a known issue of optimal designs: their sensitivity to parameters, such as varying vehicle weight, which is a concern especially for the design of hybrid buses. Therefore, the iterative optimization process is applied to design a robust multi-mode hybrid electric bus under different loading scenarios as the final design challenge of this paper.
Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm
Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang
2017-09-01
Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.
A novel hybrid algorithm of GSA with Kepler algorithm for numerical optimization
Directory of Open Access Journals (Sweden)
Soroor Sarafrazi
2015-07-01
Full Text Available It is now well recognized that pure algorithms can be promisingly improved by hybridization with other techniques. One of the relatively new metaheuristic algorithms is Gravitational Search Algorithm (GSA which is based on the Newton laws. In this paper, to enhance the performance of GSA, a novel algorithm called “Kepler”, inspired by the astrophysics, is introduced. The Kepler algorithm is based on the principle of the first Kepler law. The hybridization of GSA and Kepler algorithm is an efficient approach to provide much stronger specialization in intensification and/or diversification. The performance of GSA–Kepler is evaluated by applying it to 14 benchmark functions with 20–1000 dimensions and the optimal approximation of linear system as a practical optimization problem. The results obtained reveal that the proposed hybrid algorithm is robust enough to optimize the benchmark functions and practical optimization problems.
Topology optimization of bounded acoustic problems using the hybrid finite element-wave based method
DEFF Research Database (Denmark)
Goo, Seongyeol; Wang, Semyung; Kook, Junghwan
2017-01-01
This paper presents an alternative topology optimization method for bounded acoustic problems that uses the hybrid finite element-wave based method (FE-WBM). The conventional method for the topology optimization of bounded acoustic problems is based on the finite element method (FEM), which...... is limited to low frequency applications due to considerable computational efforts. To this end, we propose a gradient-based topology optimization method that uses the hybrid FE-WBM whereby the entire domain of a problem is partitioned into design and non-design domains. In this respect, the FEM is used...... as a design domain of topology optimization, and the WBM is used as a non-design domain to increase computational efficiency. The adjoint variable method based on the hybrid FE-WBM is also proposed as a means of computing design sensitivities. Numerical examples are presented to demonstrate the effectiveness...
Miele, A.; Wang, T.; Melvin, W. W.
1987-01-01
The near-optimum guidance of an aircraft from quasi-steady flight to quasi-steady flight in a windshear is studied. The take-off problem is considered with reference to flight in a vertical plane; allowance is made for the presence of a downdraft as well as horizontal shear. It is assumed that the power setting is held at the maximum value and that the aircraft is controlled through the angle of attack. While the shear guidance and the initial aftershear guidance use constant gain coefficients, the final aftershear guidance employs a variable gain coefficient. The results show that the guidance scheme for quasi-steady flight recovery yields a transition from quasi-steady flight to quasi-steady flight which is close to that of the optimal trajectory; it guarantees the restoration of the initial quasi-steady state and has good stability properties.
Design of optimal fast scanning trajectory for the mechanical scanner of measurement instruments.
Ju, Bing-Feng; Bai, Xiaolong; Chen, Jian; Ge, Yaozheng
2014-01-01
This paper focuses on the design of the optimal scanning mode for the family of scanning probe microscopes. Based on different values of the maximum acceleration (deceleration) rate and maximum speed of X- and Y- axes of the mechanical scanner encountered in practice due to different mechanical design and loads, the design procedure of the optimal fast scanning mode is presented, which is found to be sensitive to the specific parameters of the scanning motion. By utilizing the simultaneous motion of the two axes, the fast raster scanning mode proposed can improve the scanning efficiency by 29% when comparing with the conventional raster (CR) scanning mode, if the scanning speeds of both axes are identical. In addition, the optimal fast mode provided by us has no effects on the image accuracy such as image degradation, image distortion when the efficiency is evaluated. No further difficulties are introduced to the control of the mechanical scanner and the data acquisition process. This optimal scanning mode is useful when the response time of the probe is very fast (such as ultrasonic probe in scanning acoustic microscope (SAM)), and the main limitations are due to the mechanical scanner. By applying different loads for both axes, the experiments with different scanning areas and scanning modes are conducted in a self-developed SAM. Experimental results coincide with the theoretical analysis and confirm the validation of our proposed optimal fast scanning mode and its superiority over the CR scanning mode.
A Hybrid Bacterial Foraging - PSO Algorithm Based Tuning of Optimal FOPI Speed Controller
Directory of Open Access Journals (Sweden)
Rajasekhar Anguluri
2011-11-01
Full Text Available Bacterial Foraging Optimization Algorithm (BFOA has recently emerged as a very powerful technique for real parameteroptimization. In order to overcome the delay in optimization and to further enhance the performance of BFO, this paper proposeda new hybrid algorithm combining the features of BFOA and Particle Swarm Optimization (PSO for tuning a Fractional orderspeed controller in a Permanent Magnet Synchronous Motor (PMSM Drive. Computer simulations illustrate the effectiveness of theproposed approach compared to that of basic versions of PSO and BFO.
Kanagaraj, G.; Ponnambalam, S. G.; Jawahar, N.; Mukund Nilakantan, J.
2014-10-01
This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.
AN HYBRID STOCHASTIC-DETERMINISTIC OPTIMIZATION ALGORITHM FOR STRUCTURAL DAMAGE IDENTIFICATION
Nhamage, Idilson António; Lopez, Rafael Holdorf; Miguel, Leandro Fleck Fadel; Miguel, Letícia Fleck Fadel; Torii, André Jacomel
2017-01-01
Abstract. This paper presents a hybrid stochastic/deterministic optimization algorithm to solve the target optimization problem of vibration-based damage detection. The use of a numerical solution of the representation formula to locate the region of the global solution, i.e., to provide a starting point for the local optimizer, which is chosen to be the Nelder-Mead algorithm (NMA), is proposed. A series of numerical examples with different damage scenarios and noise levels was performed unde...
Optimal Control of a Mackerel-Mimicking Robot for Energy Efficient Trajectory Tracking
Institute of Scientific and Technical Information of China (English)
Seunghee Lee; Jounghyun Park; Cheolheui Han
2007-01-01
A robotic fish, BASEMACK1, is designed and fabricated by mimicking the shape of a live mackerel. Three DC servo-motors are serially linked together and actuated to mimic the mackerel's Carangiform motion. Hydrodynamic characteristics of a fish-mimetic test model are experimentally identified and utilized in order to numerically simulate fish swimming.The discrete set of kinematic and dynamic parameters are obtained by considering required horizontal and lateral forces and minimum energy consumption. Using the optimized parameter set, optimal control of the robot is studied.
Directory of Open Access Journals (Sweden)
Paweł Sitek
2016-01-01
Full Text Available This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP and constraint logic programming (CLP, were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems. The ECLiPSe system with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.
Kubota, Shigeru; Kanomata, Kensaku; Suzuki, Takahiko; Hirose, Fumihiko
2014-10-01
The antireflection structure (ARS) for solar cells is categorized to mainly two different techniques, i.e., the surface texturing and the single or multi-layer antireflection interference coating. In this study, we propose a novel hybrid ARS, which integrates moth eye texturing and multi-layer coat, for application to organic photovoltaics (OPVs). Using optical simulations based on the finite-difference time-domain (FDTD) method, we conduct nearly global optimization of the geometric parameters characterizing the hybrid ARS. The proposed optimization algorithm consists of two steps: in the first step, we optimize the period and height of moth eye array, in the absence of multi-layer coating. In the second step, we optimize the whole structure of hybrid ARS by using the solution obtained by the first step as the starting search point. The methods of the simple grid search and the Hooke and Jeeves pattern search are used for global and local searches, respectively. In addition, we study the effects of deviations in the geometric parameters of hybrid ARS from their optimized values. The design concept of hybrid ARS is highly beneficial for broadband light trapping in OPVs.
非均匀参数化方法在弹道优化中的应用%Application of Non-uniform Parameterization Approach to Trajectory Optimization
Institute of Scientific and Technical Information of China (English)
郭尚生; 杨荣军; 王良明
2013-01-01
为获得最优滑翔方案弹道,基于制导炮弹质点弹道模型、飞行状态约束条件与最大射程目标函数,建立了增程弹道优化模型.针对传统的均匀参数化方法难以精确逼近最优弹道的问题,将控制时域非均匀离散化,各时间段长度作为优化参数.采用变尺度的非均匀参数化方法和序列二次规划方法相结合,对滑翔方案弹道进行优化.仿真结果显示:相比传统方法,该方法设计的方案弹道的射程更远、终端速度更大,具有更强的弹道性能优势.%In order to obtain the optimal glide project trajectory, the extend-range trajectory optimal model was established based on the partile trajectory model of guided projectile,the flight state constraint conditions and the objective function of maximum range. Accurate optimal trajectory can ' t be obtained by traditional uniform parameterization method. Aiming at this problem, the control horizon was divided into several time stages of varying lengths and the length of each time period was defined as optimization parameters. The glide project trajectory was optimized by time-scaling non-uniform parametric method combined with sequadratic programming. The simulation results show that, compared with traditional methods, the project trajectory designed by this approach has a farther range, a higher terminal speed and more advantages of ballistic performance.
High-Fidelity Real-Time Trajectory Optimization for Reusable Launch Vehicles
2006-12-01
Pure Teleoperations Adaptive Behaviors Situational AwarenessFeature ID Mission PlanningPath Planning Obstacle Detection/ Disturbance Estimation Path...Following Obstacle Detection/ Disturbance Rejection Figure 1.8 Autonomy Spectrum Adapted from Ref. [92]. Since optimal solutions are rarely...these angular relationships. The first approach consists of using trigonometry based on the physical geometry of the flight angles. This is done by
Dynamic optimization of a dead-end filtration trajectory : Blocking filtration laws
Blankert, Bastiaan; Betlem, Ben H.L.; Roffel, Brian
2006-01-01
An operating model for dead-end membrane filtration is proposed based on the well-known blocking laws. The resulting model contains three parameters representing, the operating strategy, the fouling mechanism and the fouling potential of the feed. The optimal control strategy is determined by minimi
Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Constraints
Hinckley, David; Englander, Jacob; Hitt, Darren
2015-01-01
Single trial evaluations Trial creation by Phase-wise GA-style or DE-inspired recombination Bin repository structure requires an initialization period Non-exclusionary Kill Distance Population collapse mechanic Main loop Creation Probabilistic switch between GA and DE creation types Locally optimize Submit to repository Repeat.
Using rapidly-exploring random tree-based algorithms to find smooth and optimal trajectories
CSIR Research Space (South Africa)
Matebese, B
2012-10-01
Full Text Available feasible solution faster than other algorithms. The drawback of using RRT is that, as the number of samples increases, the probability that the algorithm converges to a sub-optimal solution increases. Furthermore, the path generated by this algorithm...
Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System
Farouk Odeim; Jürgen Roes; Angelika Heinzel
2015-01-01
In this paper, an experimental fuel cell/battery/supercapacitor hybrid system is investigated in terms of modeling and power management design and optimization. The power management strategy is designed based on the role that should be played by each component of the hybrid power source. The supercapacitor is responsible for the peak power demands. The battery assists the supercapacitor in fulfilling the transient power demand by controlling its state-of-energy, whereas the fuel cell system, ...
1995-05-01
A HYBRID ANALYTICAL/ SIMULATION MODELING APPROACH FOR PLANNING AND OPTIMIZING MASS TACTICAL AIRBORNE OPERATIONS by DAVID DOUGLAS BRIGGS M.S.B.A...COVERED MAY 1995 TECHNICAL REPORT THESIS 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS A HYBRID ANALYTICAL SIMULATION MODELING APPROACH FOR PLANNING AND...are present. Thus, simulation modeling presents itself as an excellent alternate tool for planning because it allows for the modeling of highly complex
Review of the Optimal Design on a Hybrid Renewable Energy System
Directory of Open Access Journals (Sweden)
Wu Yuan-Kang
2016-01-01
Full Text Available Hybrid renewable energy systems, combining various kinds of technologies, have shown relatively high capabilities to solve reliability problems and have reduced cost challenges. The use of hybrid electricity generation/storage technologies is reasonable to overcome related shortcomings. While the hybrid renewable energy system is attractive, its design, specifically the determination of the size of PV, wind, and diesel power generators and the size of energy storage system in each power station, is very challenging. Therefore, this paper will focus on the system planning and operation of hybrid generation systems, and several corresponding topics and papers by using intelligent computing methods will be reviewed. They include typical case studies, modeling and system simulation, control and management, reliability and economic studies, and optimal design on a reliable hybrid generation system.
Efficient Hybrid Optimal Design Method for Power Electronics Converters
AUTHOR|(SzGeCERN)697719; Aguglia, Davide; Viarouge, Philippe; Cros, Jérôme
2015-01-01
This paper presents a novel design methodology for dimensioning optimal power-electronic converters, which is able to achieve the precision of numerical simulation-based optimization procedures, however minimizing the overall computation time. The approach is based on the utilization of analytical and frequency-domain design models for a numerical optimization process, a validation with numerical simulations of the intermediate optimal solutions, and the correction of the analytical design models precision from the numerical simulation results. This method allows using the numerical simulation in an efficient way, where typically less than ten correction iterations are required. In order to demonstrate the performances of the proposed methodology, the calculation of the control parameters for an H-bridge DC-DC converter and the optimal dimensioning of a damped output filter for a buck converter using the proposed approach is presented.
A hybrid optimization method for biplanar transverse gradient coil design
Energy Technology Data Exchange (ETDEWEB)
Qi Feng [Key Laboratory for Quantum Information and Measurements, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 (China); Tang Xin [Beijing Key Laboratory of Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871 (China); Jin Zhe [Key Laboratory for Quantum Information and Measurements, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 (China); Jiang Zhongde [Beijing Key Laboratory of Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871 (China); Shen Yifei [Key Laboratory for Quantum Information and Measurements, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 (China); Meng Bin [Key Laboratory for Quantum Information and Measurements, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 (China); Zu Donglin [Beijing Key Laboratory of Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871 (China); Wang Weimin [Key Laboratory for Quantum Information and Measurements, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 (China)
2007-05-07
The optimization of transverse gradient coils is one of the fundamental problems in designing magnetic resonance imaging gradient systems. A new approach is presented in this paper to optimize the transverse gradient coils' performance. First, in the traditional spherical harmonic target field method, high order coefficients, which are commonly ignored, are used in the first stage of the optimization process to give better homogeneity. Then, some cosine terms are introduced into the series expansion of stream function. These new terms provide simulated annealing optimization with new freedoms. Comparison between the traditional method and the optimized method shows that the inhomogeneity in the region of interest can be reduced from 5.03% to 1.39%, the coil efficiency increased from 3.83 to 6.31 mT m{sup -1} A{sup -1} and the minimum distance of these discrete coils raised from 1.54 to 3.16 mm.
2015-12-24
Quadrature Collocation Method and Sparse Nonlinear Programming,” ACM Transactions on Math. Software , Vol. 39, No. 3, Jul 13. 8Patterson, M. A., R. A. V., GPOPS...careful seeding,” Proceedings of the eighteenth annual ACM -SIAM sympo- sium on Discrete algorithms, Society for Industrial and Applied Mathematics , 2007...Optimal Control Problems Using hp-Adaptive Gaussian Quadrature Collocation Method and Sparse Nonlinear Programming,” ACM Transactions on Math. Software
Optimum Performance-Based Seismic Design Using a Hybrid Optimization Algorithm
Directory of Open Access Journals (Sweden)
S. Talatahari
2014-01-01
Full Text Available A hybrid optimization method is presented to optimum seismic design of steel frames considering four performance levels. These performance levels are considered to determine the optimum design of structures to reduce the structural cost. A pushover analysis of steel building frameworks subject to equivalent-static earthquake loading is utilized. The algorithm is based on the concepts of the charged system search in which each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Comparison of the results of the hybrid algorithm with those of other metaheuristic algorithms shows the efficiency of the hybrid algorithm.
Directory of Open Access Journals (Sweden)
Hyo Seon Park
2014-01-01
Full Text Available Since genetic algorithm-based optimization methods are computationally expensive for practical use in the field of structural optimization, a resizing technique-based hybrid genetic algorithm for the drift design of multistory steel frame buildings is proposed to increase the convergence speed of genetic algorithms. To reduce the number of structural analyses required for the convergence, a genetic algorithm is combined with a resizing technique that is an efficient optimal technique to control the drift of buildings without the repetitive structural analysis. The resizing technique-based hybrid genetic algorithm proposed in this paper is applied to the minimum weight design of three steel frame buildings. To evaluate the performance of the algorithm, optimum weights, computational times, and generation numbers from the proposed algorithm are compared with those from a genetic algorithm. Based on the comparisons, it is concluded that the hybrid genetic algorithm shows clear improvements in convergence properties.
Englander, Jacob A.; Englander, Arnold C.
2014-01-01
Trajectory optimization methods using monotonic basin hopping (MBH) have become well developed during the past decade [1, 2, 3, 4, 5, 6]. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing random variable (RV)s from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by J. Englander [3, 6]) significantly improves monotonic basin hopping (MBH) performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness. Efficiency is finding better solutions in less time. Robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive random walks (RWs) originally developed in the field of statistical physics.
Fuzzy portfolio optimization advances in hybrid multi-criteria methodologies
Gupta, Pankaj; Inuiguchi, Masahiro; Chandra, Suresh
2014-01-01
This monograph presents a comprehensive study of portfolio optimization, an important area of quantitative finance. Considering that the information available in financial markets is incomplete and that the markets are affected by vagueness and ambiguity, the monograph deals with fuzzy portfolio optimization models. At first, the book makes the reader familiar with basic concepts, including the classical mean–variance portfolio analysis. Then, it introduces advanced optimization techniques and applies them for the development of various multi-criteria portfolio optimization models in an uncertain environment. The models are developed considering both the financial and non-financial criteria of investment decision making, and the inputs from the investment experts. The utility of these models in practice is then demonstrated using numerical illustrations based on real-world data, which were collected from one of the premier stock exchanges in India. The book addresses both academics and professionals pursuin...
Fang, Jiancheng; Wang, Tao; Zhang, Hong; Li, Yang; Zou, Sheng
2014-12-01
The hybrid optical pumping atomic magnetometers have not realized its theoretical sensitivity, the optimization is critical for optimal performance. The optimizations proposed in this paper are suitable for hybrid optical pumping atomic magnetometer, which contains two alkali species. To optimize the parameters, the dynamic equations of spin evolution with two alkali species were solved, whose steady-state solution is used to optimize the parameters. The demand of the power of the pump beam is large for hybrid optical pumping. Moreover, the sensitivity of the hybrid optical pumping magnetometer increases with the increase of the power density of the pump beam. The density ratio between the two alkali species is especially important for hybrid optical pumping magnetometer. A simple expression for optimizing the density ratio is proposed in this paper, which can help to determine the mole faction of the alkali atoms in fabricating the hybrid cell before the cell is sealed. The spin-exchange rate between the two alkali species is proportional to the saturated density of the alkali vapor, which is highly dependent on the temperature of the cell. Consequently, the sensitivity of the hybrid optical pumping magnetometer is dependent on the temperature of the cell. We proposed the thermal optimization of the hybrid cell for a hybrid optical pumping magnetometer, which can improve the sensitivity especially when the power of the pump beam is low. With these optimizations, a sensitivity of approximately 5 fT/Hz(1/2) is achieved with gradiometer arrangement.
Serial robot for the trajectory optimization and error compensation of TMT mask exchange system
Wang, Jianping; Zhang, Feifan; Zhou, Zengxiang; Zhai, Chao
2015-10-01
Mask exchange system is the main part of Multi-Object Broadband Imaging Echellette (MOBIE) on the Thirty Meter Telescope (TMT). According to the conception of the TMT mask exchange system, the pre-design was introduced in the paper which was based on IRB 140 robot. The stiffness model of IRB 140 in SolidWorks was analyzed under different gravity vectors for further error compensation. In order to find the right location and path planning, the robot and the mask cassette model was imported into MOBIE model to perform different schemes simulation. And obtained the initial installation position and routing. Based on these initial parameters, IRB 140 robot was operated to simulate the path and estimate the mask exchange time. Meanwhile, MATLAB and ADAMS software were used to perform simulation analysis and optimize the route to acquire the kinematics parameters and compare with the experiment results. After simulation and experimental research mentioned in the paper, the theoretical reference was acquired which could high efficient improve the structure of the mask exchange system parameters optimization of the path and precision of the robot position.
Igeta, Hideki; Hasegawa, Mikio
Chaotic dynamics have been effectively applied to improve various heuristic algorithms for combinatorial optimization problems in many studies. Currently, the most used chaotic optimization scheme is to drive heuristic solution search algorithms applicable to large-scale problems by chaotic neurodynamics including the tabu effect of the tabu search. Alternatively, meta-heuristic algorithms are used for combinatorial optimization by combining a neighboring solution search algorithm, such as tabu, gradient, or other search method, with a global search algorithm, such as genetic algorithms (GA), ant colony optimization (ACO), or others. In these hybrid approaches, the ACO has effectively optimized the solution of many benchmark problems in the quadratic assignment problem library. In this paper, we propose a novel hybrid method that combines the effective chaotic search algorithm that has better performance than the tabu search and global search algorithms such as ACO and GA. Our results show that the proposed chaotic hybrid algorithm has better performance than the conventional chaotic search and conventional hybrid algorithms. In addition, we show that chaotic search algorithm combined with ACO has better performance than when combined with GA.
A hybrid nonlinear programming method for design optimization
Rajan, S. D.
1986-01-01
Solutions to engineering design problems formulated as nonlinear programming (NLP) problems usually require the use of more than one optimization technique. Moreover, the interaction between the user (analysis/synthesis) program and the NLP system can lead to interface, scaling, or convergence problems. An NLP solution system is presented that seeks to solve these problems by providing a programming system to ease the user-system interface. A simple set of rules is used to select an optimization technique or to switch from one technique to another in an attempt to detect, diagnose, and solve some potential problems. Numerical examples involving finite element based optimal design of space trusses and rotor bearing systems are used to illustrate the applicability of the proposed methodology.
A New Chaotic Genetic Hybrid Algorithm and Its Applications in Mechanical Optimization Design
Institute of Scientific and Technical Information of China (English)
WANG Zhong-min; DAI Yi
2010-01-01
A new chaotic genetic hybrid algorithm (CGHA) based on float point coding was put forward in this paper.Firstly, it used chaos optimization to search coarsely and produced a better initial population. Then, a power function carri-er was adopted to improve the ergodicity and the sufficiency of the chaos optimization. Secondly, the genetic algorithm (GA) was used to search finely and guaranteed the population's evolution. To avoid the search being trapped in local minimum, a chaos degenerate mutation operator was designed to make the search converge to a global optimum quickly. Finally, CGHA was used to solve a typical mechanical optimization problem of shear stress checking for a cylinder helix spring.Compared with traditional penalty function method, chaos-Powell hybrid algorithm and standard GA, CGHA shows better performance in solution precision and convergence speed than those of the algorithms. Therefore, CGHA is a new effective way to solve the problems in mechanical optimization design.
Chiadamrong, N.; Piyathanavong, V.
2017-04-01
Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.
Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes.
Tumuluru, Jaya Shankar; McCulloch, Richard
2016-11-09
Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article demonstrates the novelty of the hybrid genetic algorithm (HGA), which combines both stochastic and deterministic routines for improved optimization results. The new hybrid genetic algorithm developed is applied to the Ackley benchmark function as well as case studies in food, biofuel, and biotechnology processes. For each case study, the hybrid genetic algorithm found a better optimum candidate than reported by the sources. In the case of food processing, the hybrid genetic algorithm improved the anthocyanin yield by 6.44%. Optimization of bio-oil production using HGA resulted in a 5.06% higher yield. In the enzyme production process, HGA predicted a 0.39% higher xylanase yield. Hybridization of the genetic algorithm with a deterministic algorithm resulted in an improved optimum compared to statistical methods.
Application of Hybrid Optimization Algorithm in the Synthesis of Linear Antenna Array
Directory of Open Access Journals (Sweden)
Ezgi Deniz Ülker
2014-01-01
Full Text Available The use of hybrid algorithms for solving real-world optimization problems has become popular since their solution quality can be made better than the algorithms that form them by combining their desirable features. The newly proposed hybrid method which is called Hybrid Differential, Particle, and Harmony (HDPH algorithm is different from the other hybrid forms since it uses all features of merged algorithms in order to perform efficiently for a wide variety of problems. In the proposed algorithm the control parameters are randomized which makes its implementation easy and provides a fast response. This paper describes the application of HDPH algorithm to linear antenna array synthesis. The results obtained with the HDPH algorithm are compared with three merged optimization techniques that are used in HDPH. The comparison shows that the performance of the proposed algorithm is comparatively better in both solution quality and robustness. The proposed hybrid algorithm HDPH can be an efficient candidate for real-time optimization problems since it yields reliable performance at all times when it gets executed.
Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm
García-Segura,Tatiana; Yepes, Víctor; Martí, José V.; Alcalá,Julián
2014-01-01
In this paper a new hybrid glowworm swarm algorithm (SAGSO) for solving structural optimization problems is presented. The structure proposed to be optimized here is a simply-supported concrete I-beam defined by 20 variables. Eight different concrete mixtures are studied, varying the compressive strength grade and compacting system. The solutions are evaluated following the Spanish Code for structural concrete. The algorithm is applied to two objective functions, namely the embedded CO2 emiss...
2015-01-01
Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a recently developed SPSO algorithm is exploited to search the optimal parameter values of radial basis function (RBF) kernel of the SVM. The new algori...
Hybrid constraint programming and metaheuristic methods for large scale optimization problems
2011-01-01
This work presents hybrid Constraint Programming (CP) and metaheuristic methods for the solution of Large Scale Optimization Problems; it aims at integrating concepts and mechanisms from the metaheuristic methods to a CP-based tree search environment in order to exploit the advantages of both approaches. The modeling and solution of large scale combinatorial optimization problem is a topic which has arisen the interest of many researcherers in the Operations Research field; combinatori...
Yamamoto, Takeyoshi; Cingoski, Vlatko; Kaneda, Kazufumi; Yamashita, Hideo
1996-01-01
In this paper, a hybrid method for inverse optimization of electromagnetic coils utilizing the multi-transition neural network and the Hopfield neural network is proposed. Due to the discrete character of the neural network, an optimization problem is transformed into a discrete problem through the division of the entire coil area into elemental coils with constant current density. The minimization of the objective function is performed by the multi-transition neural network and the Hopfield ...
Directory of Open Access Journals (Sweden)
Zengqiang Mi
2016-01-01
Full Text Available A novel control strategy based on the optimization of transfer trajectory at operation points for DFIG is proposed. Aim of this control strategy is to reduce the mechanical fatigue of DFIG caused by the frequent adjustment of rotating speed and pitch angle when operating in the islanded power system. Firstly, the stability of DFIG at different operation points is analyzed. Then an optimization model of transfer trajectory at operation points is established, with the minimum synthetic adjustment amount of rotating speed and pitch angle as the objective function and with the balance of active power and the stability of operation points as the constraint conditions. Secondly, the wind speed estimator is designed, and the control strategy of pitch system is improved to cooperate with the indirect stator flux orientation control technology for rotor-side inverter control. Then by the coordination control of its rotating speed and pitch angle, an operation trajectory controller is established to ensure the islanded operation of DFIG along the optimal transfer trajectory. Finally, the simulation results show that the proposed control strategy is technical feasibility with good performance.
Doubly Optimal Secure Multicasting: Hierarchical Hybrid Communication Network : Disaster Relief
Garimella, Rama Murthy; Singhal, Deepti
2011-01-01
Recently, the world has witnessed the increasing occurrence of disasters, some of natural origin and others caused by man. The intensity of the phenomenon that cause such disasters, the frequency in which they occur, the number of people affected and the material damage caused by them have been growing substantially. Disasters are defined as natural, technological, and human-initiated events that disrupt the normal functioning of the economy and society on a large scale. Areas where disasters have occurred bring many dangers to rescue teams and the communication network infrastructure is usually destroyed. To manage these hazards, different wireless technologies can be launched in the area of disaster. This paper discusses the innovative wireless technologies for Disaster Management. Specifically, issues related to the design of Hierarchical Hybrid Communication Network (arising in the communication network for disaster relief) are discussed.
Progress toward an optimized hydrogen series hybrid engine
Smith, J. Ray; Aceves, Salvador M.; Johnson, Norman L.; Amsden, Anthony A.
1995-06-01
The design considerations and computational fluid dynamics (CFD) modeling of a high efficiency, low emissions, hydrogen-fueled engine for use as the prime mover of a series hybrid automobile is described. The series hybrid automobile uses the engine to generate electrical energy via a lightweight generator, the electrical energy is stored in a power peaking device (like a flywheel or ultracapacitor) and used as required to meet the tractive drive requirements (plus accessory loads) through an electrical motor. The engine/generator is stopped whenever the energy storage device is fully charged. Engine power output required was determined with a vehicle simulation code to be 15 to 20 kW steady state with peak output of 40 to 45 kW for hill climb. Combustion chamber and engine geometry were determined from a critical review of the hydrogen engine experiments in the literature combined with a simplified global engine model. Two different engine models are employed to guide engine design. The models are a simplified global engine performance model that relies strongly on correlations with literature data for heat transfer and friction losses, and a state-of-the-art CFD combustion model, KIVA-3, to elucidate fluid mechanics and combustion details through full three-dimensional modeling. Both intake and exhaust processes as well as hydrogen combustion chemistry and thermal NO(sub x) production are simulated. Ultimately, a comparison between the simulation and experimental results will lead to improved modeling and will give guidance to changes required in the next generation engine to achieve the goal of 45% brake thermal efficiency.
Progress toward an optimized hydrogen series hybrid engine
Energy Technology Data Exchange (ETDEWEB)
Smith, J.R.; Aceves, S.M. [Lawrence Livermore National Lab., CA (United States); Johnson, N.L.; Amsden, A.A. [Los Alamos National Lab., NM (United States)
1995-06-01
The design considerations and computational fluid dynamics (CFD) modeling of a high efficiency, low emissions, hydrogen-fueled engine for use as the prime mover of a series hybrid automobile is described. The series hybrid automobile uses the engine to generate electrical energy via a lightweight generator, the electrical energy is stored in a power peaking device (like a flywheel or ultracapacitor) and used as required to meet the tractive drive requirements (plus accessory loads) through an electrical motor. The engine/generator is stopped whenever the energy storage device is fully charged. Engine power output required was determined with a vehicle simulation code to be 15 to 20 kW steady state with peak output of 40 to 45 kW for hill climb. Combustion chamber and engine geometry were determined from a critical review of the hydrogen engine experiments in the literature combined with a simplified global engine model. Two different engine models are employed to guide engine design. The models are a simplified global engine performance model that relies strongly on correlations with literature data for heat transfer and friction losses, and a state-of-the-art CFD combustion model, KIVA-3, to elucidate fluid mechanics and combustion details through full three-dimensional modeling. Both intake and exhaust processes as well as hydrogen combustion chemistry and thermal NO{sub x} production are simulated. Ultimately, a comparison between the simulation and experimental results will lead to improved modeling and will give guidance to changes required in the next generation engine to achieve the goal of 45% brake thermal efficiency.
A New Hybrid MGBPSO-GSA Variant for Improving Function Optimization Solution in Search Space
Directory of Open Access Journals (Sweden)
Narinder Singh
2017-03-01
Full Text Available In this article, a newly hybrid nature-inspired approach (MGBPSO-GSA is developed with a combination of Mean Gbest Particle Swarm Optimization (MGBPSO and Gravitational Search Algorithm (GSA. The basic inspiration is to integrate the ability of exploitation in MGBPSO with the ability of exploration in GSA to synthesize the strength of both approaches. As a result, the presented approach has the automatic balance capability between local and global searching abilities. The performance of the hybrid approach is tested on a variety of classical functions, ie, unimodal, multimodal, and fixed-dimension multimodal functions. Furthermore, Iris data set, Heart data set, and economic dispatch problems are used to compare the hybrid approach with several metaheuristics. Experimental statistical solutions prove empirically that the new hybrid approach outperforms significantly a number of metaheuristics in terms of solution stability, solution quality, capability of local and global optimum, and convergence speed.
Optimization of ultrasonic array inspections using an efficient hybrid model and real crack shapes
Energy Technology Data Exchange (ETDEWEB)
Felice, Maria V., E-mail: maria.felice@bristol.ac.uk [Department of Mechanical Engineering, University of Bristol, Bristol, U.K. and NDE Laboratory, Rolls-Royce plc., Bristol (United Kingdom); Velichko, Alexander, E-mail: p.wilcox@bristol.ac.uk; Wilcox, Paul D., E-mail: p.wilcox@bristol.ac.uk [Department of Mechanical Engineering, University of Bristol, Bristol (United Kingdom); Barden, Tim; Dunhill, Tony [NDE Laboratory, Rolls-Royce plc., Bristol (United Kingdom)
2015-03-31
Models which simulate the interaction of ultrasound with cracks can be used to optimize ultrasonic array inspections, but this approach can be time-consuming. To overcome this issue an efficient hybrid model is implemented which includes a finite element method that requires only a single layer of elements around the crack shape. Scattering Matrices are used to capture the scattering behavior of the individual cracks and a discussion on the angular degrees of freedom of elastodynamic scatterers is included. Real crack shapes are obtained from X-ray Computed Tomography images of cracked parts and these shapes are inputted into the hybrid model. The effect of using real crack shapes instead of straight notch shapes is demonstrated. An array optimization methodology which incorporates the hybrid model, an approximate single-scattering relative noise model and the real crack shapes is then described.
Optimal design of damping layers in SMA/GFRP laminated hybrid composites
Haghdoust, P.; Cinquemani, S.; Lo Conte, A.; Lecis, N.
2017-10-01
This work describes the optimization of the shape profiles for shape memory alloys (SMA) sheets in hybrid layered composite structures, i.e. slender beams or thinner plates, designed for the passive attenuation of flexural vibrations. The paper starts with the description of the material and architecture of the investigated hybrid layered composite. An analytical method, for evaluating the energy dissipation inside a vibrating cantilever beam is developed. The analytical solution is then followed by a shape profile optimization of the inserts, using a genetic algorithm to minimize the SMA material layer usage, while maintaining target level of structural damping. Delamination problem at SMA/glass fiber reinforced polymer interface is discussed. At the end, the proposed methodology has been applied to study the hybridization of a wind turbine layered structure blade with SMA material, in order to increase its passive damping.
Zhang, Jiapu
2010-01-01
Evolutionary algorithms are parallel computing algorithms and simulated annealing algorithm is a sequential computing algorithm. This paper inserts simulated annealing into evolutionary computations and successful developed a hybrid Self-Adaptive Evolutionary Strategy $\\mu+\\lambda$ method and a hybrid Self-Adaptive Classical Evolutionary Programming method. Numerical results on more than 40 benchmark test problems of global optimization show that the hybrid methods presented in this paper are very effective. Lennard-Jones potential energy minimization is another benchmark for testing new global optimization algorithms. It is studied through the amyloid fibril constructions by this paper. To date, there is little molecular structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins.This region belongs to the N-terminal unstructured region (1-123) of prion proteins, the structure of which has proved hard to determine using NMR spectroscopy or X-ray crystallography ...
A hybrid method for optimal load shedding and improving voltage stability
Directory of Open Access Journals (Sweden)
V. Tamilselvan
2016-03-01
Full Text Available In this paper, a hybrid method is proposed for reducing the amount of load shedding and voltage collapse. The hybrid method is the combination of Genetic Algorithm (GA and Neural Network (NN. The GA is used by two stages, one is to frame the optimization model and other stage is to generate data set for developing the NN based intelligent load shedding model. The appropriate buses for load shedding are selected based on the sensitivity of minimum eigenvalue of load flow Jacobian with respect to the load shed. The proposed method is implemented in MATLAB working platform and the performance is tested with 6 bus and IEEE 14 bus bench mark system. The result of the proposed hybrid method is compared with the GA based optimization algorithm. The comparison shows that, the proposed method ensures voltage stability with minimum loading shedding.
HYBRID APPROACH FOR OPTIMAL CLUSTER HEAD SELECTION IN WSN USING LEACH AND MONKEY SEARCH ALGORITHMS
Directory of Open Access Journals (Sweden)
T. SHANKAR
2017-02-01
Full Text Available Wireless Sensor Networks (WSNs are being widely used with low-cost, lowpower, multifunction sensors based on the development of wireless communication, which has enabled a wide variety of new applications. In WSN, the main concern is that it contains a limited power battery and is constrained in energy consumption hence energy and lifetime are of paramount importance. To achieve high energy efficiency and prolong network lifetime in WSNs, clustering techniques have been widely adopted. The proposed algorithm is hybridization of well-known Low-Energy Adaptive Clustering Hierarchy (LEACH algorithm with a distinctive Monkey Search (MS algorithm, which is an optimization algorithm used for optimal cluster head selection. The proposed hybrid algorithm exhibit high throughput, residual energy and improved lifetime. Comparison of the proposed hybrid algorithm is made with the well-known cluster-based protocols for WSNs, namely, LEACH and monkey search algorithm, individually.
Energy Optimization for a Weak Hybrid Power System of an Automobile Exhaust Thermoelectric Generator
Fang, Wei; Quan, Shuhai; Xie, Changjun; Tang, Xinfeng; Ran, Bin; Jiao, Yatian
2017-07-01
An integrated starter generator (ISG)-type hybrid electric vehicle (HEV) scheme is proposed based on the automobile exhaust thermoelectric generator (AETEG). An eddy current dynamometer is used to simulate the vehicle's dynamic cycle. A weak ISG hybrid bench test system is constructed to test the 48 V output from the power supply system, which is based on engine exhaust-based heat power generation. The thermoelectric power generation-based system must ultimately be tested when integrated into the ISG weak hybrid mixed power system. The test process is divided into two steps: comprehensive simulation and vehicle-based testing. The system's dynamic process is simulated for both conventional and thermoelectric powers, and the dynamic running process comprises four stages: starting, acceleration, cruising and braking. The quantity of fuel available and battery pack energy, which are used as target vehicle energy functions for comparison with conventional systems, are simplified into a single energy target function, and the battery pack's output current is used as the control variable in the thermoelectric hybrid energy optimization model. The system's optimal battery pack output current function is resolved when its dynamic operating process is considered as part of the hybrid thermoelectric power generation system. In the experiments, the system bench is tested using conventional power and hybrid thermoelectric power for the four dynamic operation stages. The optimal battery pack curve is calculated by functional analysis. In the vehicle, a power control unit is used to control the battery pack's output current and minimize energy consumption. Data analysis shows that the fuel economy of the hybrid power system under European Driving Cycle conditions is improved by 14.7% when compared with conventional systems.
亚轨道飞行器返回轨迹快速优化%The Rapid Optimization of Entry Trajectory for Suborbital Launch Vehicle
Institute of Scientific and Technical Information of China (English)
王文虎
2012-01-01
The rapid, accurate and robust trajectory generation methods can substantially improve safety and reliability while reducing cost. For entry trajectory characteristics of suborbital launch vehicle ( SLV ) , the concepts of "pseudo-control variables" and "final approach corridor (FAC) " are introduced to formulate optimal control problem. The Gauss pseudospectral method and forward Radau pseudospectral method are applied to rapid optimization of entry trajectory for SLV. The abilities of these two pseudospectral methods to handle complex trajectory optimization problems are compared. The results of this study show that the forward Radau pseudospectral method is not suited to deal with problems with control variable constraints, but the Gauss pseudospectral can rapidly and accurately generate SLV entry trajectory with various constraints. The feasibility and optimality are verified through the final results.%快速、准确、鲁棒的轨迹生成方法可以增加任务的安全性与可靠性,极大地降低成本.针对亚轨道飞行器返回段特点,引入“伪控制量”、“末端进场走廊”等概念,分别采用高斯伪谱法和向前拉道伪谱法进行了返回轨迹快速优化研究,比较了两种伪谱法在处理复杂问题时的能力.仿真结果表明,向前拉道伪谱法不适合处理含控制量约束的问题,而高斯伪谱法在满足各种约束条件下,能够快速准确地生成亚轨道飞行器返回轨迹,同时验证了结果的可行性与最优性.
A Hybrid Genetic-Algorithm Space-Mapping Tool for the Optimization of Antennas
DEFF Research Database (Denmark)
Pantoja, Mario Fernández; Meincke, Peter; Bretones, Amelia Rubio
2007-01-01
A hybrid global-local optimization technique for the design of antennas is presented. It consists of the subsequent application of a genetic algorithm (GA) that employs coarse models in the simulations and a space mapping (SM) that refines the solution found in the previous stage. The technique...
Optimization of Antennas using a Hybrid Genetic-Algorithm Space-Mapping Algorithm
DEFF Research Database (Denmark)
Pantoja, M.F.; Bretones, A.R.; Meincke, Peter;
2006-01-01
A hybrid global-local optimization technique for the design of antennas is presented. It consists of the subsequent application of a Genetic Algorithm (GA) that employs coarse models in the simulations and a space mapping (SM) that refines the solution found in the previous stage. The technique...
Energy and Propulsion Optimization of Solid-Propellant Grain of a Hybrid Power Device
Directory of Open Access Journals (Sweden)
Bondarchuk Sergey S.
2016-01-01
Full Text Available A method of distribution of an additional solid-phase component (oxidizer providing uniformity of grain burning for the purpose of evaluation and optimization of energy and propulsion parameters of hybrid solid-propellant motor is proposed in the paper.
HYBRID OPTIMIZING GRIFFON-VULTURE ALGORITHM BASED ON SWARM INTELLIGENCE MECHANISMS
Directory of Open Access Journals (Sweden)
Chastikova V. A.
2014-06-01
Full Text Available Griffon-vultures with input parameters minimal value for compound functions optimization that change during the time searching hybrid algorithm offered in this article. Researches of its efficiency and comparing analysis with some other systems have been performed
A study on optimization of hybrid drive train using Advanced Vehicle Simulator (ADVISOR)
Same, Adam; Stipe, Alex; Grossman, David; Park, Jae Wan
This study investigates the advantages and disadvantages of three hybrid drive train configurations: series, parallel, and "through-the-ground" parallel. Power flow simulations are conducted with the MATLAB/Simulink-based software ADVISOR. These simulations are then applied in an application for the UC Davis SAE Formula Hybrid vehicle. ADVISOR performs simulation calculations for vehicle position using a combined backward/forward method. These simulations are used to study how efficiency and agility are affected by the motor, fuel converter, and hybrid configuration. Three different vehicle models are developed to optimize the drive train of a vehicle for three stages of the SAE Formula Hybrid competition: autocross, endurance, and acceleration. Input cycles are created based on rough estimates of track geometry. The output from these ADVISOR simulations is a series of plots of velocity profile and energy storage State of Charge that provide a good estimate of how the Formula Hybrid vehicle will perform on the given course. The most noticeable discrepancy between the input cycle and the actual velocity profile of the vehicle occurs during deceleration. A weighted ranking system is developed to organize the simulation results and to determine the best drive train configuration for the Formula Hybrid vehicle. Results show that the through-the-ground parallel configuration with front-mounted motors achieves an optimal balance of efficiency, simplicity, and cost. ADVISOR is proven to be a useful tool for vehicle power train design for the SAE Formula Hybrid competition. This vehicle model based on ADVISOR simulation is applicable to various studies concerning performance and efficiency of hybrid drive trains.
A study on optimization of hybrid drive train using Advanced Vehicle Simulator (ADVISOR)
Energy Technology Data Exchange (ETDEWEB)
Same, Adam; Stipe, Alex; Grossman, David; Park, Jae Wan [Department of Mechanical and Aeronautical Engineering, University of California, Davis, One Shields Ave, Davis, CA 95616 (United States)
2010-10-01
This study investigates the advantages and disadvantages of three hybrid drive train configurations: series, parallel, and ''through-the-ground'' parallel. Power flow simulations are conducted with the MATLAB/Simulink-based software ADVISOR. These simulations are then applied in an application for the UC Davis SAE Formula Hybrid vehicle. ADVISOR performs simulation calculations for vehicle position using a combined backward/forward method. These simulations are used to study how efficiency and agility are affected by the motor, fuel converter, and hybrid configuration. Three different vehicle models are developed to optimize the drive train of a vehicle for three stages of the SAE Formula Hybrid competition: autocross, endurance, and acceleration. Input cycles are created based on rough estimates of track geometry. The output from these ADVISOR simulations is a series of plots of velocity profile and energy storage State of Charge that provide a good estimate of how the Formula Hybrid vehicle will perform on the given course. The most noticeable discrepancy between the input cycle and the actual velocity profile of the vehicle occurs during deceleration. A weighted ranking system is developed to organize the simulation results and to determine the best drive train configuration for the Formula Hybrid vehicle. Results show that the through-the-ground parallel configuration with front-mounted motors achieves an optimal balance of efficiency, simplicity, and cost. ADVISOR is proven to be a useful tool for vehicle power train design for the SAE Formula Hybrid competition. This vehicle model based on ADVISOR simulation is applicable to various studies concerning performance and efficiency of hybrid drive trains. (author)
SAR Image Segmentation Based On Hybrid PSOGSA Optimization Algorithm
Directory of Open Access Journals (Sweden)
Amandeep Kaur
2014-09-01
Full Text Available Image segmentation is useful in many applications. It can identify the regions of interest in a scene or annotate the data. It categorizes the existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing algorithms, the JSEG, and the fast scanning algorithm. Due to the presence of speckle noise, segmentation of Synthetic Aperture Radar (SAR images is still a challenging problem. We proposed a fast SAR image segmentation method based on Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA. In this method, threshold estimation is regarded as a search procedure that examinations for an appropriate value in a continuous grayscale interval. Hence, PSO-GSA algorithm is familiarized to search for the optimal threshold. Experimental results indicate that our method is superior to GA based, AFS based and ABC based methods in terms of segmentation accuracy, segmentation time, and Thresholding.
Coordinated Target Tracking via a Hybrid Optimization Approach
Wang, Yin; Cao, Yan
2017-01-01
Recent advances in computer science and electronics have greatly expanded the capabilities of unmanned aerial vehicles (UAV) in both defense and civil applications, such as moving ground object tracking. Due to the uncertainties of the application environments and objects’ motion, it is difficult to maintain the tracked object always within the sensor coverage area by using a single UAV. Hence, it is necessary to deploy a group of UAVs to improve the robustness of the tracking. This paper investigates the problem of tracking ground moving objects with a group of UAVs using gimbaled sensors under flight dynamic and collision-free constraints. The optimal cooperative tracking path planning problem is solved using an evolutionary optimization technique based on the framework of chemical reaction optimization (CRO). The efficiency of the proposed method was demonstrated through a series of comparative simulations. The results show that the cooperative tracking paths determined by the newly developed method allows for longer sensor coverage time under flight dynamic restrictions and safety conditions. PMID:28264425
Coordinated Target Tracking via a Hybrid Optimization Approach
Directory of Open Access Journals (Sweden)
Yin Wang
2017-02-01
Full Text Available Recent advances in computer science and electronics have greatly expanded the capabilities of unmanned aerial vehicles (UAV in both defense and civil applications, such as moving ground object tracking. Due to the uncertainties of the application environments and objects’ motion, it is difficult to maintain the tracked object always within the sensor coverage area by using a single UAV. Hence, it is necessary to deploy a group of UAVs to improve the robustness of the tracking. This paper investigates the problem of tracking ground moving objects with a group of UAVs using gimbaled sensors under flight dynamic and collision-free constraints. The optimal cooperative tracking path planning problem is solved using an evolutionary optimization technique based on the framework of chemical reaction optimization (CRO. The efficiency of the proposed method was demonstrated through a series of comparative simulations. The results show that the cooperative tracking paths determined by the newly developed method allows for longer sensor coverage time under flight dynamic restrictions and safety conditions.
Directory of Open Access Journals (Sweden)
Bendali Nadir
2014-05-01
Full Text Available A new method for time-jerk optimal planning under Kino-dynamic constraints of robot manipulators in pick-and-place operations is described in this paper. In order to ensure that the resulting trajectory is smooth enough, a cost function containing a term proportional to the integral of the squared jerk (defined as the derivative of the acceleration along the trajectory is considered. Moreover, a second term, proportional to the total execution time, is added to the expression of the cost function. A Cubic Spline functions are then used to compose overall trajectory. This method makes it possible to deal with the kinematic constraints as well as the dynamic constraints imposed on the robot manipulator. The algorithm has been tested in simulation yielding good results.
Institute of Scientific and Technical Information of China (English)
SUN Fan; DU Wenli; QI Rongbin; QIAN Feng; ZHONG Weimin
2013-01-01
The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature.Genetic algorithm(GA)has been proved to be a feasible method when the gradient is difficult to calculate.Its advantage is that the control profiles at all time stages are optimized simultaneously,but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum.In this study,a hybrid improved genetic algorithm(HIGA)for solving dynamic optimization problems is proposed to overcome these defects.Simplex method(SM)is used to perform the local search in the neighborhood of the optimal solution.By using SM,the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved.The hybrid algorithm presents some improvements,such as protecting the best individual,accepting immigrations,as well as employing adaptive crossover and Gaussian mutation operators.The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems.At last,HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.
Englander, Arnold C.; Englander, Jacob A.
2017-01-01
Interplanetary trajectory optimization problems are highly complex and are characterized by a large number of decision variables and equality and inequality constraints as well as many locally optimal solutions. Stochastic global search techniques, coupled with a large-scale NLP solver, have been shown to solve such problems but are inadequately robust when the problem constraints become very complex. In this work, we present a novel search algorithm that takes advantage of the fact that equality constraints effectively collapse the solution space to lower dimensionality. This new approach walks the filament'' of feasibility to efficiently find the global optimal solution.
Stochastic Optimal Control of Parallel Hybrid Electric Vehicles
Directory of Open Access Journals (Sweden)
Feiyan Qin
2017-02-01
Full Text Available Energy management strategies (EMSs in hybrid electric vehicles (HEVs are highly related to the fuel economy and emission performances. However, EMS constitutes a challenging problem due to the complex structure of a HEV and the unknown or partially known driving cycles. To meet this problem, this paper adopts a stochastic dynamic programming (SDP method for the EMS of a specially designed vehicle, a pre-transmission single-shaft torque-coupling parallel HEV. In this parallel HEV, the auto clutch output is connected to the transmission input through an electric motor, which benefits an efficient motor assist operation. In this EMS, demanded torque of driver is modeled as a one-state Markov process to represent the uncertainty of future driving situations. The obtained EMS has been evaluated with ADVISOR2002 over two standard government drive cycles and a self-defined one, and compared with a dynamic programming (DP one and a rule-based one. Simulation results have shown the real-time performance of the proposed approach, and potential vehicle performance improvement relative to the rule-based one.
DEFF Research Database (Denmark)
Bang-Møller, Christian; Rokni, Masoud; Elmegaard, Brian
2011-01-01
A hybrid plant producing combined heat and power (CHP) from biomass by use of a two-stage gasification concept, solid oxide fuel cells (SOFC) and a micro gas turbine was considered for optimization. The hybrid plant represents a sustainable and efficient alternative to conventional decentralized...... and exergy analyses were applied. Focus in this optimization study was heat management, and the optimization efforts resulted in a substantial gain of approximately 6% in the electrical efficiency of the plant. The optimized hybrid plant produced approximately 290 kWe at an electrical efficiency of 58...
Fuzzy Inspired Hybrid Genetic Approach to Optimize Travelling Salesman Problem
Directory of Open Access Journals (Sweden)
Bindu
2012-06-01
Full Text Available One of the category of algorithm Problems are basically exponential problems. These problems are basically exponential problems and take time to find the solution. In the present work we are optimising one of the common NP complete problem called Travelling Salesman Problem. In our work we have defined a genetic approach by combining fuzzy approach along with genetics. In this work we have implemented the modified DPX crossover to improve genetic approach. The work is implemented in MATLAB environment and obtained results shows the define approach has optimized the existing genetic algorithm results
Energy supply chain optimization of hybrid feedstock processes: a review.
Elia, Josephine A; Floudas, Christodoulos A
2014-01-01
The economic, environmental, and social performances of energy systems depend on their geographical locations and the surrounding market infrastructure for feedstocks and energy products. Strategic decisions to locate energy conversion facilities must take all upstream and downstream operations into account, prompting the development of supply chain modeling and optimization methods. This article reviews the contributions of energy supply chain studies that include heat, power, and liquid fuels production. Studies are categorized based on specific features of the mathematical model, highlighting those that address energy supply chain models with and without considerations of multiperiod decisions. Studies that incorporate uncertainties are discussed, and opportunities for future research developments are outlined.
Optimal Power Scheduling for a Grid-Connected Hybrid PV-Wind-Battery Microgrid System
DEFF Research Database (Denmark)
Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Savaghebi, Mehdi
2016-01-01
In this paper, a lineal mathematical model is proposed to schedule optimally the power references of the distributed energy resources in a grid-connected hybrid PVwind-battery microgrid. The optimization of the short term scheduling problem is addressed through a mixed-integer linear programming...... mathematical model, wherein the cost of energy purchased from the main grid is minimized and profits for selling energy generated by photovoltaic arrays are maximized by considering both physical constraints and requirements for a feasible deployment in the real system. The optimization model is tested...
A hybrid genetic algorithm based on mutative scale chaos optimization strategy
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In order to avoid such problems as low convergent speed and local optimal solution in simple genetic algorithms, a new hybrid genetic algorithm is proposed. In this algorithm, a mutative scale chaos optimization strategy is operated on the population after a genetic operation. And according to the searching process, the searching space of the optimal variables is gradually diminished and the regulating coefficient of the secondary searching process is gradually changed which will lead to the quick evolution of the population. The algorithm has such advantages as fast search, precise results and convenient using etc. The simulation results show that the performance of the method is better than that of simple genetic algorithms.
Hybridization of Meta-heuristics for Optimizing Routing protocol in VANETs
Directory of Open Access Journals (Sweden)
R.R Sedamkar
2016-02-01
Full Text Available The goal of VANET is to establish a vehicular communication system which is reliable and fast which caters to road safety and road safety. In VANET where network fragmentation is frequent with no central control, routing becomes a challenging task. Planning an optimal routing plan for tuning parameter configuration of routing protocol for setting up VANET is very crucial. This is done by defining an optimization problem where hybridization of meta-heuristics is defined. The paper contributes the idea of combining meta-heuristic algorithm to enhance the performance of individual search method for optimization problem
Institute of Scientific and Technical Information of China (English)
Shengli Song; Li Kong; Yong Gan; Rijian Su
2008-01-01
An effective hybrid particle swarm cooperative optimization (HPSCO) algorithm combining simulated annealing method and simplex method is proposed. The main idea is to divide particle swarm into several sub-groups and achieve optimization through cooperativeness of different sub-groups among the groups. The proposed algorithm is tested by benchmark functions and applied to material balance computation (MBC) in alumina production. Results show that HPSCO, with both a better stability and a steady convergence, has faster convergence speed and higher global convergence ability than the single method and the improved particle swarm optimization method. Most importantly, results demonstrate that HPSCO is more feasible and efficient than other algorithms in MBC.
Robust design and optimization for autonomous PV-wind hybrid power systems
Institute of Scientific and Technical Information of China (English)
Jun-hai SHI; Zhi-dan ZHONG; Xin-jian ZHU; Guang-yi CAO
2008-01-01
This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated as a constraint multi-objective optimization problem, which is solved by a multi-objective genetic algorithm, NSGA-Ⅱ. Monte Carlo Simulation (MCS) method, combined with Latin Hypercube Sampling (LHS), is applied to evaluate the stochastic system performance. The potential of the proposed method has been demonstrated by a conceptual system design. A comparative study between the proposed robust method and the deterministic method presented in literature has been conducted. The results indicate that the proposed method can find a large mount of Pareto optimal system configurations with better compromising performance than the deterministic method. The trade-off information may be derived by a systematical comparison of these configurations. The proposed robust design method should be useful for hybrid power systems that require both optimality and robustness.
SVC Planning in Large–scale Power Systems via a Hybrid Optimization Method
DEFF Research Database (Denmark)
Yang, Guang ya; Majumder, Rajat; Xu, Zhao
2009-01-01
The research on allocation of FACTS devices has attracted quite a lot interests from various aspects. In this paper, a hybrid model is proposed to optimise the number, location as well as the parameter settings of static Var compensator (SVC) deployed in large–scale power systems. The model utili...... a candidate solution pool. Then in the second stage, the candidates are presented to a linear planning model to investigate the system optimal loadability, hence the optimal solution for SVC planning can be achieved. The method is presented to IEEE 300–bus system....... utilises the result of vulnerability assessment for determining the candidate locations. A hybrid optimisation method including two stages is proposed to find out the optimal solution of SVC in large– scale planning problem. In the first stage, a conventional genetic algorithm (GA) is exploited to generate...
Directory of Open Access Journals (Sweden)
A. Belloufi*
2013-01-01
Full Text Available The determination of optimal cutting parameters is one of the most important elements in any process planning ofmetal parts. In this paper, a new hybrid genetic algorithm by using sequential quadratic programming is used for theoptimization of cutting conditions. It is used for the resolution of a multipass turning optimization case by minimizingthe production cost under a set of machining constraints. The genetic algorithm (GA is the main optimizer of thisalgorithm whereas SQP Is used to fine tune the results obtained from the GA. Furthermore, the convergencecharacteristics and robustness of the proposed method have been explored through comparisons with resultsreported in literature. The obtained results indicate that the proposed hybrid genetic algorithm by using a sequentialquadratic programming is effective compared to other techniques carried out by different researchers.
Performance optimization of EDFA-Raman hybrid optical amplifier using genetic algorithm
Singh, Simranjit; Kaler, R. S.
2015-05-01
For the first time, a novel net gain analytical model of EDFA-Raman hybrid optical amplifier (HOA) is designed and optimized the various parameters using genetic algorithm. Our method has shown to be robust in the simultaneous analysis of multiple parameters, such as Raman length, EDFA length and its pump powers, to obtained highest possible gain. The optimized HOA is further investigated and characterized on system level in the scenario of 100×10 Gbps dense wavelength division multiplexed (DWDM) system with 25 GHz interval. With an optimized HOA, a flat gain of >18 dB is obtained from frequency region 187 to 189.5 THz with a gain variation of less than 1.35 dB without using any gain flattened technique. The obtained noise figure is also the lowest value (<2 dB/channel) ever reported for proposed hybrid optical amplifier at reduced channel spacing with acceptable bit error rate.
A hybrid method for optimization of the adaptive Goldstein filter
Jiang, Mi; Ding, Xiaoli; Tian, Xin; Malhotra, Rakesh; Kong, Weixue
2014-12-01
The Goldstein filter is a well-known filter for interferometric filtering in the frequency domain. The main parameter of this filter, alpha, is set as a power of the filtering function. Depending on it, considered areas are strongly or weakly filtered. Several variants have been developed to adaptively determine alpha using different indicators such as the coherence, and phase standard deviation. The common objective of these methods is to prevent areas with low noise from being over filtered while simultaneously allowing stronger filtering over areas with high noise. However, the estimators of these indicators are biased in the real world and the optimal model to accurately determine the functional relationship between the indicators and alpha is also not clear. As a result, the filter always under- or over-filters and is rarely correct. The study presented in this paper aims to achieve accurate alpha estimation by correcting the biased estimator using homogeneous pixel selection and bootstrapping algorithms, and by developing an optimal nonlinear model to determine alpha. In addition, an iteration is also merged into the filtering procedure to suppress the high noise over incoherent areas. The experimental results from synthetic and real data show that the new filter works well under a variety of conditions and offers better and more reliable performance when compared to existing approaches.
Hybrid optimization of dynamic deployment for networked fire control system
Institute of Scientific and Technical Information of China (English)
Chen Chen; Jie Chen; Bin Xin
2013-01-01
With applying the information technology to the military field, the advantages and importance of the networked combat are more and more obvious. In order to make ful use of limited battle-field resources and maximal y destroy enemy targets from arbitrary angle in a limited time, the research on firepower nodes dynamic deployment becomes a key problem of command and control. Con-sidering a variety of tactical indexes and actual constraints in air defense, a mathematical model is formulated to minimize the ene-my target penetration probability. Based on characteristics of the mathematical model and demands of the deployment problems, an assistance-based algorithm is put forward which combines the ar-tificial potential field (APF) method with a memetic algorithm. The APF method is employed to solve the constraint handling prob-lem and generate feasible solutions. The constrained optimization problem transforms into an optimization problem of APF para-meters adjustment, and the dimension of the problem is reduced greatly. The dynamic deployment is accomplished by generation and refinement of feasible solutions. The simulation results show that the proposed algorithm is effective and feasible in dynamic situation.
Directory of Open Access Journals (Sweden)
Biwei Tang
2016-05-01
Full Text Available Global path planning is a challenging issue in the filed of mobile robotics due to its complexity and the nature of nondeterministic polynomial-time hard (NP-hard. Particle swarm optimization (PSO has gained increasing popularity in global path planning due to its simplicity and high convergence speed. However, since the basic PSO has difficulties balancing exploration and exploitation, and suffers from stagnation, its efficiency in solving global path planning may be restricted. Aiming at overcoming these drawbacks and solving the global path planning problem efficiently, this paper proposes a hybrid PSO algorithm that hybridizes PSO and differential evolution (DE algorithms. To dynamically adjust the exploration and exploitation abilities of the hybrid PSO, a novel PSO, the nonlinear time-varying PSO (NTVPSO, is proposed for updating the velocities and positions of particles in the hybrid PSO. In an attempt to avoid stagnation, a modified DE, the ranking-based self adaptive DE (RBSADE, is developed to evolve the personal best experience of particles in the hybrid PSO. The proposed algorithm is compared with four state-of-the-art evolutionary algorithms. Simulation results show that the proposed algorithm is highly competitive in terms of path optimality and can be considered as a vital alternative for solving global path planning.
Stand-alone hybrid wind-photovoltaic power generation systems optimal sizing
Crǎciunescu, Aurelian; Popescu, Claudia; Popescu, Mihai; Florea, Leonard Marin
2013-10-01
Wind and photovoltaic energy resources have attracted energy sectors to generate power on a large scale. A drawback, common to these options, is their unpredictable nature and dependence on day time and meteorological conditions. Fortunately, the problems caused by the variable nature of these resources can be partially overcome by integrating the two resources in proper combination, using the strengths of one source to overcome the weakness of the other. The hybrid systems that combine wind and solar generating units with battery backup can attenuate their individual fluctuations and can match with the power requirements of the beneficiaries. In order to efficiently and economically utilize the hybrid energy system, one optimum match design sizing method is necessary. In this way, literature offers a variety of methods for multi-objective optimal designing of hybrid wind/photovoltaic (WG/PV) generating systems, one of the last being genetic algorithms (GA) and particle swarm optimization (PSO). In this paper, mathematical models of hybrid WG/PV components and a short description of the last proposed multi-objective optimization algorithms are given.
Optimization of batteries for plug-in hybrid electric vehicles
English, Jeffrey Robb
This thesis presents a method to quickly determine the optimal battery for an electric vehicle given a set of vehicle characteristics and desired performance metrics. The model is based on four independent design variables: cell count, cell capacity, state-of-charge window, and battery chemistry. Performance is measured in seven categories: cost, all-electric range, maximum speed, acceleration, battery lifetime, lifetime greenhouse gas emissions, and charging time. The performance of each battery is weighted according to a user-defined objective function to determine its overall fitness. The model is informed by a series of battery tests performed on scaled-down battery samples. Seven battery chemistries were tested for capacity at different discharge rates, maximum output power at different charge levels, and performance in a real-world automotive duty cycle. The results of these tests enable a prediction of the performance of the battery in an automobile. Testing was performed at both room temperature and low temperature to investigate the effects of battery temperature on operation. The testing highlighted differences in behavior between lithium, nickel, and lead based batteries. Battery performance decreased with temperature across all samples with the largest effect on nickel-based chemistries. Output power also decreased with lead acid batteries being the least affected by temperature. Lithium-ion batteries were found to be highly efficient (>95%) under a vehicular duty cycle; nickel and lead batteries have greater losses. Low temperatures hindered battery performance and resulted in accelerated failure in several samples. Lead acid, lead tin, and lithium nickel alloy batteries were unable to complete the low temperature testing regime without losing significant capacity and power capability. This is a concern for their applicability in electric vehicles intended for cold climates which have to maintain battery temperature during long periods of inactivity
Hybrid particle swarm optimization for multiobjective resource allocation
Institute of Scientific and Technical Information of China (English)
Yi Yang; Li Xiaoxing; Gu Chunqin
2008-01-01
Resource allocation (RA) is the problem of allocating resources among various artifacts or business units to meet one or more expected goals,such as maximizing the profits,minimizing the costs,or achieving the best qualities.A complex multiobjective RA is addressed,and a multiobjective mathematical model is used to find solutions efficiently.Then,an improved particle swarm algorithm (mO_PSO) is proposed combined with a new particle diversity controller policies and dissipation operation.Meanwhile,a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented.The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.
Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control.
Deshpande, Sunil; Nandola, Naresh N; Rivera, Daniel E; Younger, Jarred W
2014-12-01
The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.
DEFF Research Database (Denmark)
Herbert-Acero, José F.; Martínez-Lauranchet, Jaime; Probst, Oliver
2014-01-01
This work presents a novel framework for the aerodynamic design and optimization of blades for small horizontal axiswind turbines (WT). The framework is based on a state-of-the-art blade element momentum model, which is complemented with the XFOIL 6.96 software in order to provide an estimate...
Adaptive hybrid optimization strategy for calibration and parameter estimation of physical models
Vesselinov, Velimir V
2011-01-01
A new adaptive hybrid optimization strategy, entitled squads, is proposed for complex inverse analysis of computationally intensive physical models. The new strategy is designed to be computationally efficient and robust in identification of the global optimum (e.g. maximum or minimum value of an objective function). It integrates a global Adaptive Particle Swarm Optimization (APSO) strategy with a local Levenberg-Marquardt (LM) optimization strategy using adaptive rules based on runtime performance. The global strategy optimizes the location of a set of solutions (particles) in the parameter space. The LM strategy is applied only to a subset of the particles at different stages of the optimization based on the adaptive rules. After the LM adjustment of the subset of particle positions, the updated particles are returned to the APSO strategy. The advantages of coupling APSO and LM in the manner implemented in squads is demonstrated by comparisons of squads performance against Levenberg-Marquardt (LM), Particl...
Vasilyev, Oleg V.; Gazzola, Mattia; Koumoutsakos, Petros
2009-11-01
In this talk we discuss preliminary results for the use of hybrid wavelet collocation - Brinkman penalization approach for shape and topology optimization of fluid flows. Adaptive wavelet collocation method tackles the problem of efficiently resolving a fluid flow on a dynamically adaptive computational grid in complex geometries (where grid resolution varies both in space and time time), while Brinkman volume penalization allows easy variation of flow geometry without using body-fitted meshes by simply changing the shape of the penalization region. The use of Brinkman volume penalization approach allow seamless transition from shape to topology optimization by combining it with level set approach and increasing the size of the optimization space. The approach is demonstrated for shape optimization of a variety of fluid flows by optimizing single cost function (time averaged Drag coefficient) using covariance matrix adaptation (CMA) evolutionary algorithm.
A hybrid GA-TS algorithm for open vehicle routing optimization of coal mines material
Energy Technology Data Exchange (ETDEWEB)
Yu, S.W.; Ding, C.; Zhu, K.J. [China University of Geoscience, Wuhan (China)
2011-08-15
In the open vehicle routing problem (OVRP), the objective is to minimize the number of vehicles and the total distance (or time) traveled. This study primarily focuses on solving an open vehicle routing problem (OVRP) by applying a novel hybrid genetic algorithm and the Tabu search (GA-TS), which combines the GA's parallel computing and global optimization with TS's Tabu search skill and fast local search. Firstly, the proposed algorithm uses natural number coding according to the customer demands and the captivity of the vehicle for globe optimization. Secondly, individuals of population do TS local search with a certain degree of probability, namely, do the local routing optimization of all customer sites belong to one vehicle. The mechanism not only improves the ability of global optimization, but also ensures the speed of operation. The algorithm was used in Zhengzhou Coal Mine and Power Supply Co., Ltd.'s transport vehicle routing optimization.
Directory of Open Access Journals (Sweden)
Li Mao
2016-01-01
Full Text Available Artificial bee colony (ABC algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.
Synthesis/design optimization of SOFC-PEM hybrid system under uncertainty
Institute of Scientific and Technical Information of China (English)
Lingjun Tan; Chen Yang; Nana Zhou
2015-01-01
Solid oxide fuel cell–proton exchange membrane (SOFC–PEM) hybrid system is being foreseen as a valuable alternative for power generation. As this hybrid system is a conceptual design, many uncertainties involving input values should be considered at the early stage of process optimization. We present in this paper a general-ized framework of multi-objective optimization under uncertainty for the synthesis/design optimization of the SOFC–PEM hybrid system. The framework is based on geometric, economic and electrochemical models and focuses on evaluating the effect of uncertainty in operating parameters on three conflicting objectives:electricity efficiency, SOFC current density and capital cost of system. The multi-objective optimization provides solutions in the form of a Pareto surface, with a range of possible synthesis/design solutions and a logical procedure for searching the global optimum solution for decision maker. Comparing the stochastic and deterministic Pareto surfaces of different objectives, we conclude that the objectives are considerably influenced by uncertainties because the two trade-off surfaces are different.
DEFF Research Database (Denmark)
Wang, Yong; Cai, Zixing; Zhou, Yuren
2009-01-01
A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...
Optical design and optimization of parabolic dish solar concentrator with a cavity hybrid receiver
Blázquez, R.; Carballo, J.; Silva, M.
2016-05-01
One of the main goals of the BIOSTIRLING-4SKA project, funded by the European Commission, is the development of a hybrid Dish-Stirling system based on a hybrid solar-gas receiver, which has been designed by the Swedish company Cleanergy. A ray tracing study, which is part of the design of this parabolic dish system, is presented in this paper. The study pursues the optimization of the concentrator and receiver cavity geometry according to the requirements of flux distribution on the receiver walls set by the designer of the hybrid receiver. The ray-tracing analysis has been performed with the open source software Tonatiuh, a ray-tracing tool specifically oriented to the modeling of solar concentrators.
Optimization of levulinic acid from lignocellulosic biomass using a new hybrid catalyst.
Ya'aini, Nazlina; Amin, Nor Aishah Saidina; Asmadi, Mohd
2012-07-01
Conversion of glucose, empty fruit bunch (efb) and kenaf to levulinic acid over a new hybrid catalyst has been investigated in this study. The characterization and catalytic performance results revealed that the physico-chemical properties of the new hybrid catalyst comprised of chromium chloride and HY zeolite increased the levulinic acid production from glucose compared to the parent catalysts. Optimization of the glucose conversion process using two level full factorial designs (2(3)) with two center points reported 55.2% of levulinic acid yield at 145.2 °C, 146.7 min and 12.0% of reaction temperature, reaction time and catalyst loading, respectively. Subsequently, the potential of efb and kenaf for producing levulinic acid at the optimum conditions was established after 53.2% and 66.1% of efficiencies were reported. The observation suggests that the hybrid catalyst has a potential to be used in biomass conversion to levulinic acid.
Shimohigashi, Yoshinobu; Araki, Fujio; Maruyama, Masato; Nakaguchi, Yuji; Nakato, Kengo; Nagasue, Nozomu; Kai, Yudai
2015-01-01
Our purpose in this study was to evaluate the performance of four-dimensional computed tomography (4D-CBCT) and to optimize the acquisition parameters. We evaluated the relationship between the acquisition parameters of 4D-CBCT and the accuracy of the target motion trajectory using a dynamic thorax phantom. The target motion was created three dimensionally using target sizes of 2 and 3 cm, respiratory cycles of 4 and 8 s, and amplitudes of 1 and 2 cm. The 4D-CBCT data were acquired under two detector configurations: "small mode" and "medium mode". The projection data acquired with scan times ranging from 1 to 4 min were sorted into 2, 5, 10, and 15 phase bins. The accuracy of the measured target motion trajectories was evaluated by means of the root mean square error (RMSE) from the setup values. For the respiratory cycle of 4 s, the measured trajectories were within 2 mm of the setup values for all acquisition times and target sizes. Similarly, the errors for the respiratory cycle of 8 s were <4 mm. When we used 10 or more phase bins, the measured trajectory errors were within 2 mm of the setup values. The trajectory errors for the two detector configurations showed similar trends. The acquisition times for achieving an RMSE of 1 mm for target sizes of 2 and 3 cm were 2 and 1 min, respectively, for respiratory cycles of 4 s. The results obtained in this study enable optimization of the acquisition parameters for target size, respiratory cycle, and desired measurement accuracy.
Energy Technology Data Exchange (ETDEWEB)
Jennings, W.; Green, J.
2001-01-01
The purpose of this research was to determine the optimal configuration of home power systems relevant to different regions in the United States. The hypothesis was that, regardless of region, the optimal system would be a hybrid incorporating wind technology, versus a photovoltaic hybrid system without the use of wind technology. The method used in this research was HOMER, the Hybrid Optimization Model for Electric Renewables. HOMER is a computer program that optimizes electrical configurations under user-defined circumstances. According to HOMER, the optimal system for the four regions studied (Kansas, Massachusetts, Oregon, and Arizona) was a hybrid incorporating wind technology. The cost differences between these regions, however, were dependent upon regional renewable resources. Future studies will be necessary, as it is difficult to estimate meteorological impacts for other regions.
Energy Technology Data Exchange (ETDEWEB)
Jennings, W.; Green, J.
2001-01-01
The purpose of this research was to determine the optimal configuration of home power systems relevant to different regions in the United States. The hypothesis was that, regardless of region, the optimal system would be a hybrid incorporating wind technology, versus a photovoltaic hybrid system without the use of wind technology. The method used in this research was HOMER, the Hybrid Optimization Model for Electric Renewables. HOMER is a computer program that optimizes electrical configurations under user-defined circumstances. According to HOMER, the optimal system for the four regions studied (Kansas, Massachusetts, Oregon, and Arizona) was a hybrid incorporating wind technology. The cost differences between these regions, however, were dependent upon regional renewable resources. Future studies will be necessary, as it is difficult to estimate meteorological impacts for other regions.
Hooke–Jeeves Method-used Local Search in a Hybrid Global Optimization Algorithm
Directory of Open Access Journals (Sweden)
V. D. Sulimov
2014-01-01
Full Text Available Modern methods for optimization investigation of complex systems are based on development and updating the mathematical models of systems because of solving the appropriate inverse problems. Input data desirable for solution are obtained from the analysis of experimentally defined consecutive characteristics for a system or a process. Causal characteristics are the sought ones to which equation coefficients of mathematical models of object, limit conditions, etc. belong. The optimization approach is one of the main ones to solve the inverse problems. In the main case it is necessary to find a global extremum of not everywhere differentiable criterion function. Global optimization methods are widely used in problems of identification and computation diagnosis system as well as in optimal control, computing to-mography, image restoration, teaching the neuron networks, other intelligence technologies. Increasingly complicated systems of optimization observed during last decades lead to more complicated mathematical models, thereby making solution of appropriate extreme problems significantly more difficult. A great deal of practical applications may have the problem con-ditions, which can restrict modeling. As a consequence, in inverse problems the criterion functions can be not everywhere differentiable and noisy. Available noise means that calculat-ing the derivatives is difficult and unreliable. It results in using the optimization methods without calculating the derivatives.An efficiency of deterministic algorithms of global optimization is significantly restrict-ed by their dependence on the extreme problem dimension. When the number of variables is large they use the stochastic global optimization algorithms. As stochastic algorithms yield too expensive solutions, so this drawback restricts their applications. Developing hybrid algo-rithms that combine a stochastic algorithm for scanning the variable space with deterministic local search
Optimal air-supply mode of hybrid system with radiant cooling and dedicated outdoor air
Institute of Scientific and Technical Information of China (English)
丁研; 田喆; 朱能
2015-01-01
The hybrid system with radiant cooling and dedicated outdoor air not only possesses high energy efficiency, but also creates a healthy and comfortable indoor environment. Indoor air quality will be improved by the dedicated outdoor air system (DOAS) and indoor thermal comfort can be enhanced by the radiant cooling system (RCS). The optimal air-supply mode of the hybrid system and the corresponding design approach were investigated. A full-scale experimental chamber with various air outlets and the ceiling radiant cooling panels (CRCP) was designed and established. The performances of different air-supply modes along with CRCPs were analyzed by multi-index evaluations. Preliminary investigations were also conducted on the humidity stratification and the control effect of different airflow modes to prevent condensation on CRCP. The overhead supply air is recommended as the best combination mode for the hybrid system after comprehensive comparison of the experiment results. The optimal proportion of CRCP accounting for the total cooling capacities in accord with specific cooling loads is found, which may provide valuable reference for the design and operation of the hybrid system.
Directory of Open Access Journals (Sweden)
Chun-Liang Lu
2015-10-01
Full Text Available The optimized hybrid artificial intelligence model is a potential tool to deal with construction engineering and management problems. Support vector machine (SVM has achieved excellent performance in a wide variety of applications. Nevertheless, how to effectively reduce the training complexity for SVM is still a serious challenge. In this paper, a novel order-independent approach for instance selection, called the dynamic condensed nearest neighbor (DCNN rule, is proposed to adaptively construct prototypes in the training dataset and to reduce the redundant or noisy instances in a classification process for the SVM. Furthermore, a hybrid model based on the genetic algorithm (GA is proposed to simultaneously optimize the prototype construction and the SVM kernel parameters setting to enhance the classification accuracy. Several UCI benchmark datasets are considered to compare the proposed hybrid GA-DCNN-SVM approach with the previously published GA-based method. The experimental results illustrate that the proposed hybrid model outperforms the existing method and effectively improves the classification performance for the SVM.
Lee, JongHyup; Pak, Dohyun
2016-08-29
For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections.
Directory of Open Access Journals (Sweden)
JongHyup Lee
2016-08-01
Full Text Available For practical deployment of wireless sensor networks (WSN, WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections.
Institute of Scientific and Technical Information of China (English)
蔡绍洪; 龙文; 焦建军
2015-01-01
A novel hybrid algorithm named ABC-BBO, which integrates artificial bee colony (ABC) algorithm with biogeography-based optimization (BBO) algorithm, is proposed to solve constrained mechanical design problems. ABC-BBO combined the exploration of ABC algorithm with the exploitation of BBO algorithm effectively, and hence it can generate the promising candidate individuals. The proposed hybrid algorithm speeds up the convergence and improves the algorithm’s performance. Several benchmark test functions and mechanical design problems are applied to verifying the effects of these improvements and it is demonstrated that the performance of this proposed ABC-BBO is superior to or at least highly competitive with other population-based optimization approaches.
一种求解最优控制问题的混合WNN-PSO算法%Hybrid WNN-PSO Algorithm for Optimal Control Problems
Institute of Scientific and Technical Information of China (English)
李树荣; 雷阳; 张强; 张晓东
2013-01-01
To solve optimal control problems with numerical methods, a hybrid wavelet neural network -particle swarm optimization (WNN-PSO) algorithm was developed. The first step of WNN-PSO was to parameterize the optimal control trajectory based on the non-linear approximation capability of the wavelet neural network. Then the optimal control problem was transformed into a non-linear programming problem where the decision variables are the parameters of the wavelet neural network. Lastly, the parameters of the network were optimized by the particle swarm optimization (PSO) algorithm and the global optimal solution of the NLP was obtained. Simulation study on a Bang-Bang optimal control problem and a benchmark chemical process optimal control problem shows the feasibility and effectiveness of the proposed method.%针对最优控制问题的数值求解,提出了一种混合小波神经网络粒子群(WNN-PSO)算法,算法首先利用小波神经网络的非线性逼近能力参数化最优控制轨迹,将最优控制问题转化为非线性规划(NLP)问题,其决策变量为小波神经网络的参数,然后采用粒子群(PSO)算法优化小波神经网络参数,获得NLP问题的全局最优解.针对Bang-Bang最优控制问题和一个经典的化工过程最优控制问题进行仿真研究,验证了所提出算法的可行性和有效性.
A new hybrid genetic algorithm for optimizing the single and multivariate objective functions
Energy Technology Data Exchange (ETDEWEB)
Tumuluru, Jaya Shankar [Idaho National Laboratory; McCulloch, Richard Chet James [Idaho National Laboratory
2015-07-01
In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the most improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.
Energy Technology Data Exchange (ETDEWEB)
Robar, James L., E-mail: james.robar@cdha.nshealth.ca [Department of Radiation Oncology, Dalhousie University, Halifax (Canada); Department of Physics and Atmospheric Science, Dalhousie University, Halifax (Canada); Thomas, Christopher [Department of Radiation Oncology, Dalhousie University, Halifax (Canada)
2012-01-01
This investigation focuses on possible dosimetric and efficiency advantages of HybridArc-a novel treatment planning approach combining optimized dynamic arcs with intensity-modulated radiation therapy (IMRT) beams. Application of this technique to two disparate sites, complex cranial tumors, and prostate was examined. HybridArc plans were compared with either dynamic conformal arc (DCA) or IMRT plans to determine whether HybridArc offers a synergy through combination of these 2 techniques. Plans were compared with regard to target volume dose conformity, target volume dose homogeneity, sparing of proximal organs at risk, normal tissue sparing, and monitor unit (MU) efficiency. For cranial cases, HybridArc produced significantly improved dose conformity compared with both DCA and IMRT but did not improve sparing of the brainstem or optic chiasm. For prostate cases, conformity was improved compared with DCA but not IMRT. Compared with IMRT, the dose homogeneity in the planning target volume was improved, and the maximum doses received by the bladder and rectum were reduced. Both arc-based techniques distribute peripheral dose over larger volumes of normal tissue compared with IMRT, whereas HybridArc involved slightly greater volumes of normal tissues compared with DCA. Compared with IMRT, cranial cases required 38% more MUs, whereas for prostate cases, MUs were reduced by 7%. For cranial cases, HybridArc improves dose conformity to the target. For prostate cases, dose conformity and homogeneity are improved compared with DCA and IMRT, respectively. Compared with IMRT, whether required MUs increase or decrease with HybridArc was site-dependent.
Optimal Planning and Operation of Hybrid Energy System Supplemented by Storage Devices
DEFF Research Database (Denmark)
Javadi, Mohammad Sadegh; Anvari-Moghaddam, Amjad; Guerrero, Josep M.
2017-01-01
This paper presents a two-stage model for optimal planning and operation of a distribution network. Optimal siting and sizing of renewable energy sources (RES) as well as electrical energy storage (EES) systems are considered in the proposed hybrid energy system. In this context, the planning...... problem is considered as a master problem, while there are different sub-problems associated with the short-term operational problem. To properly handle the uncertainties of forecasted load as well as renewable power generations, fair stochastic models are involved in the sub-problems based on historical...
Optimization of wind-marine hybrid power system configuration based on genetic algorithm
Shi, Hongda; Li, Linna; Zhao, Chenyu
2017-08-01
Multi-energy power systems can use energy generated from various sources to improve power generation reliability. This paper presents a cost-power generation model of a wind-tide-wave energy hybrid power system for use on a remote island, where the configuration is optimized using a genetic algorithm. A mixed integer programming model is used and a novel object function, including cost and power generation, is proposed to solve the boundary problem caused by existence of two goals. Using this model, the final optimized result is found to have a good fit with local resources.
Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem.
Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing
2015-01-01
Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA.
Directory of Open Access Journals (Sweden)
Tobias Nüesch
2014-02-01
Full Text Available This paper presents a novel method to solve the energy management problem for hybrid electric vehicles (HEVs with engine start and gearshift costs. The method is based on a combination of deterministic dynamic programming (DP and convex optimization. As demonstrated in a case study, the method yields globally optimal results while returning the solution in much less time than the conventional DP method. In addition, the proposed method handles state constraints, which allows for the application to scenarios where the battery state of charge (SOC reaches its boundaries.
Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem
Directory of Open Access Journals (Sweden)
Yi-xiang Yue
2015-01-01
Full Text Available Vehicle Routing Problem (VRP is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA based on Fractal Space Filling Curves (SFC method and Genetic Algorithm (GA is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon’s benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA.
Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem
Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing
2015-01-01
Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA. PMID:26167171
Directory of Open Access Journals (Sweden)
Ambarish Panda
2016-09-01
Full Text Available A new evolutionary hybrid algorithm (HA has been proposed in this work for environmental optimal power flow (EOPF problem. The EOPF problem has been formulated in a nonlinear constrained multi objective optimization framework. Considering the intermittency of available wind power a cost model of the wind and thermal generation system is developed. Suitably formed objective function considering the operational cost, cost of emission, real power loss and cost of installation of FACTS devices for maintaining a stable voltage in the system has been optimized with HA and compared with particle swarm optimization algorithm (PSOA to prove its effectiveness. All the simulations are carried out in MATLAB/SIMULINK environment taking IEEE30 bus as the test system.
Optimal Energy Management for a Complex Hybrid Electric Vehicle:Tolerating Power-loss of Motor
Institute of Scientific and Technical Information of China (English)
ZHANG Pei-zhi; YIN Cheng-liang; ZHANG Yong; WU Zhi-wei
2009-01-01
The energy management may perform well under normal conditions, but may lead to poor behavior under abnormal situations. To tackle this problem, an optimal control strategy called rule-based equivalent fuel consumption minimization strategy (RECMS) is developed for a new complex hybrid electric vehicle (CHEV).It optimizes the energy efficiency and drive performance to cater for normal and power-loss operations of the tractive motor. Firstly, the strategy formulates a novel objective function based on the equivalent fuel concept.By accounting for the actual fuel cost, the equivalent fuel cost for the electric machines and virtual fuel cost for the drivability, the cost function is obtained. Furthermore, some penalty factors are presented to optimize the performance target. Finally, experiments for a practical CHEV are performed to validate a simulation model.Then simulations are carried out for both rule-based and RECMS. The results show that the optimal energy management is working well.