Exploring the complexity of quantum control optimization trajectories.

Science.gov (United States)

Nanduri, Arun; Shir, Ofer M; Donovan, Ashley; Ho, Tak-San; Rabitz, Herschel

2015-01-07

The control of quantum system dynamics is generally performed by seeking a suitable applied field. The physical objective as a functional of the field forms the quantum control landscape, whose topology, under certain conditions, has been shown to contain no critical point suboptimal traps, thereby enabling effective searches for fields that give the global maximum of the objective. This paper addresses the structure of the landscape as a complement to topological critical point features. Recent work showed that landscape structure is highly favorable for optimization of state-to-state transition probabilities, in that gradient-based control trajectories to the global maximum value are nearly straight paths. The landscape structure is codified in the metric R ≥ 1.0, defined as the ratio of the length of the control trajectory to the Euclidean distance between the initial and optimal controls. A value of R = 1 would indicate an exactly straight trajectory to the optimal observable value. This paper extends the state-to-state transition probability results to the quantum ensemble and unitary transformation control landscapes. Again, nearly straight trajectories predominate, and we demonstrate that R can take values approaching 1.0 with high precision. However, the interplay of optimization trajectories with critical saddle submanifolds is found to influence landscape structure. A fundamental relationship necessary for perfectly straight gradient-based control trajectories is derived, wherein the gradient on the quantum control landscape must be an eigenfunction of the Hessian. This relation is an indicator of landscape structure and may provide a means to identify physical conditions when control trajectories can achieve perfect linearity. The collective favorable landscape topology and structure provide a foundation to understand why optimal quantum control can be readily achieved.

Mars Hybrid Propulsion System Trajectory Analysis. Part II; Cargo Missions

Science.gov (United States)

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 Trajectories Generation in Robotic Fiber Placement Systems

Science.gov (United States)

Gao, Jiuchun; Pashkevich, Anatol; Caro, Stéphane

2017-06-01

The paper proposes a methodology for optimal trajectories generation in robotic fiber placement systems. A strategy to tune the parameters of the optimization algorithm at hand is also introduced. The presented technique transforms the original continuous problem into a discrete one where the time-optimal motions are generated by using dynamic programming. The developed strategy for the optimization algorithm tuning allows essentially reducing the computing time and obtaining trajectories satisfying industrial constraints. Feasibilities and advantages of the proposed methodology are confirmed by an application example.

Optimal heliocentric trajectories for solar sail with minimum area

Science.gov (United States)

Petukhov, Vyacheslav G.

2018-05-01

The fixed-time heliocentric trajectory optimization problem is considered for planar solar sail with minimum area. Necessary optimality conditions are derived, a numerical method for solving the problem is developed, and numerical examples of optimal trajectories to Mars, Venus and Mercury are presented. The dependences of the minimum area of the solar sail from the date of departure from the Earth, the time of flight and the departing hyperbolic excess of velocity are analyzed. In particular, for the rendezvous problem (approaching a target planet with zero relative velocity) with zero departing hyperbolic excess of velocity for a flight duration of 1200 days it was found that the minimum area-to-mass ratio should be about 12 m2/kg for trajectory to Venus, 23.5 m2/kg for the trajectory to Mercury and 25 m2/kg for trajectory to Mars.

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

Optimal Hankel Norm Model Reduction by Truncation of Trajectories

NARCIS (Netherlands)

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

Optimal Control of Hybrid Systems in Air Traffic Applications

Science.gov (United States)

Kamgarpour, Maryam

Growing concerns over the scalability of air traffic operations, air transportation fuel emissions and prices, as well as the advent of communication and sensing technologies motivate improvements to the air traffic management system. To address such improvements, in this thesis a hybrid dynamical model as an abstraction of the air traffic system is considered. Wind and hazardous weather impacts are included using a stochastic model. This thesis focuses on the design of algorithms for verification and control of hybrid and stochastic dynamical systems and the application of these algorithms to air traffic management problems. In the deterministic setting, a numerically efficient algorithm for optimal control of hybrid systems is proposed based on extensions of classical optimal control techniques. This algorithm is applied to optimize the trajectory of an Airbus 320 aircraft in the presence of wind and storms. In the stochastic setting, the verification problem of reaching a target set while avoiding obstacles (reach-avoid) is formulated as a two-player game to account for external agents' influence on system dynamics. The solution approach is applied to air traffic conflict prediction in the presence of stochastic wind. Due to the uncertainty in forecasts of the hazardous weather, and hence the unsafe regions of airspace for aircraft flight, the reach-avoid framework is extended to account for stochastic target and safe sets. This methodology is used to maximize the probability of the safety of aircraft paths through hazardous weather. Finally, the problem of modeling and optimization of arrival air traffic and runway configuration in dense airspace subject to stochastic weather data is addressed. This problem is formulated as a hybrid optimal control problem and is solved with a hierarchical approach that decouples safety and performance. As illustrated with this problem, the large scale of air traffic operations motivates future work on the efficient

Design and Analysis of Optimal Ascent Trajectories for Stratospheric Airships

Science.gov (United States)

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

Optimal control and optimal trajectories of regional macroeconomic dynamics based on the Pontryagin maximum principle

Science.gov (United States)

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.

Mars Hybrid Propulsion System Trajectory Analysis. Part I; Crew Missions

Science.gov (United States)

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.

A Method of Trajectory Design for Manned Asteroids Exploration

Science.gov (United States)

Gan, Q. B.; Zhang, Y.; Zhu, Z. F.; Han, W. H.; Dong, X.

2014-11-01

A trajectory optimization method of the nuclear propulsion manned asteroids exploration is presented. In the case of launching between 2035 and 2065, based on the Lambert transfer orbit, the phases of departure from and return to the Earth are searched at first. Then the optimal flight trajectory in the feasible regions is selected by pruning the flight sequences. Setting the nuclear propulsion flight plan as propel-coast-propel, and taking the minimal mass of aircraft departure as the index, the nuclear propulsion flight trajectory is separately optimized using a hybrid method. With the initial value of the optimized local parameters of each three phases, the global parameters are jointedly optimized. At last, the minimal departure mass trajectory design result is given.

Enabling Parametric Optimal Ascent Trajectory Modeling During Early Phases of Design

Science.gov (United States)

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

Parameterization of Fuel-Optimal Synchronous Approach Trajectories to Tumbling Targets

Directory of Open Access Journals (Sweden)

David Charles Sternberg

2018-04-01

Full Text Available Docking with potentially tumbling Targets is a common element of many mission architectures, including on-orbit servicing and active debris removal. This paper studies synchronized docking trajectories as a way to ensure the Chaser satellite remains on the docking axis of the tumbling Target, thereby reducing collision risks and enabling persistent onboard sensing of the docking location. Chaser satellites have limited computational power available to them and the time allowed for the determination of a fuel optimal trajectory may be limited. Consequently, parameterized trajectories that approximate the fuel optimal trajectory while following synchronous approaches may be used to provide a computationally efficient means of determining near optimal trajectories to a tumbling Target. This paper presents a method of balancing the computation cost with the added fuel expenditure required for parameterization, including the selection of a parameterization scheme, the number of parameters in the parameterization, and a means of incorporating the dynamics of a tumbling satellite into the parameterization process. Comparisons of the parameterized trajectories are made with the fuel optimal trajectory, which is computed through the numerical propagation of Euler’s equations. Additionally, various tumble types are considered to demonstrate the efficacy of the presented computation scheme. With this parameterized trajectory determination method, Chaser satellites may perform terminal approach and docking maneuvers with both fuel and computational efficiency.

Trajectory Optimization for Differential Flat Systems

OpenAIRE

Kahina Louadj; Benjamas Panomruttanarug; Alexandre Carlos Brandao Ramos; Felix Mora-Camino

2016-01-01

International audience; The purpose of this communication is to investigate the applicability of Variational Calculus to the optimization of the operation of differentially flat systems. After introducingcharacteristic properties of differentially flat systems, the applicability of variational calculus to the optimization of flat output trajectories is displayed. Two illustrative examples are also presented.

Task driven optimal leg trajectories in insect-scale legged microrobots

Science.gov (United States)

Doshi, Neel; Goldberg, Benjamin; Jayaram, Kaushik; Wood, Robert

Origami inspired layered manufacturing techniques and 3D-printing have enabled the development of highly articulated legged robots at the insect-scale, including the 1.43g Harvard Ambulatory MicroRobot (HAMR). Research on these platforms has expanded its focus from manufacturing aspects to include design optimization and control for application-driven tasks. Consequently, the choice of gait selection, body morphology, leg trajectory, foot design, etc. have become areas of active research. HAMR has two controlled degrees-of-freedom per leg, making it an ideal candidate for exploring leg trajectory. We will discuss our work towards optimizing HAMR's leg trajectories for two different tasks: climbing using electroadhesives and level ground running (5-10 BL/s). These tasks demonstrate the ability of single platform to adapt to vastly different locomotive scenarios: quasi-static climbing with controlled ground contact, and dynamic running with un-controlled ground contact. We will utilize trajectory optimization methods informed by existing models and experimental studies to determine leg trajectories for each task. We also plan to discuss how task specifications and choice of objective function have contributed to the shape of these optimal leg trajectories.

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.

An Expert System-Driven Method for Parametric Trajectory Optimization During Conceptual Design

Science.gov (United States)

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

Velocity trajectory optimization in Hybrid Electric trucks

NARCIS (Netherlands)

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

A hybrid scheme for real-time prediction of bus trajectories

NARCIS (Netherlands)

Fadaei, Masoud; Cats, O.; Bhaskar, Ashish

2016-01-01

The uncertainty associated with public transport services can be partially counteracted by developing real-time models to predict downstream service conditions. In this study, a hybrid approach for predicting bus trajectories by integrating multiple predictors is proposed. The prediction model

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.

Minimum deltaV Burn Planning for the International Space Station Using a Hybrid Optimization Technique, Level 1

Science.gov (United States)

Brown, Aaron J.

2015-01-01

The International Space Station's (ISS) trajectory is coordinated and executed by the Trajectory Operations and Planning (TOPO) group at NASA's Johnson Space Center. TOPO group personnel routinely generate look-ahead trajectories for the ISS that incorporate translation burns needed to maintain its orbit over the next three to twelve months. The burns are modeled as in-plane, horizontal burns, and must meet operational trajectory constraints imposed by both NASA and the Russian Space Agency. In generating these trajectories, TOPO personnel must determine the number of burns to model, each burn's Time of Ignition (TIG), and magnitude (i.e. deltaV) that meet these constraints. The current process for targeting these burns is manually intensive, and does not take advantage of more modern techniques that can reduce the workload needed to find feasible burn solutions, i.e. solutions that simply meet the constraints, or provide optimal burn solutions that minimize the total DeltaV while simultaneously meeting the constraints. A two-level, hybrid optimization technique is proposed to find both feasible and globally optimal burn solutions for ISS trajectory planning. For optimal solutions, the technique breaks the optimization problem into two distinct sub-problems, one for choosing the optimal number of burns and each burn's optimal TIG, and the other for computing the minimum total deltaV burn solution that satisfies the trajectory constraints. Each of the two aforementioned levels uses a different optimization algorithm to solve one of the sub-problems, giving rise to a hybrid technique. Level 2, or the outer level, uses a genetic algorithm to select the number of burns and each burn's TIG. Level 1, or the inner level, uses the burn TIGs from Level 2 in a sequential quadratic programming (SQP) algorithm to compute a minimum total deltaV burn solution subject to the trajectory constraints. The total deltaV from Level 1 is then used as a fitness function by the genetic

Computational Approaches to Simulation and Optimization of Global Aircraft Trajectories

Science.gov (United States)

Ng, Hok Kwan; Sridhar, Banavar

2016-01-01

This study examines three possible approaches to improving the speed in generating wind-optimal routes for air traffic at the national or global level. They are: (a) using the resources of a supercomputer, (b) running the computations on multiple commercially available computers and (c) implementing those same algorithms into NASAs Future ATM Concepts Evaluation Tool (FACET) and compares those to a standard implementation run on a single CPU. Wind-optimal aircraft trajectories are computed using global air traffic schedules. The run time and wait time on the supercomputer for trajectory optimization using various numbers of CPUs ranging from 80 to 10,240 units are compared with the total computational time for running the same computation on a single desktop computer and on multiple commercially available computers for potential computational enhancement through parallel processing on the computer clusters. This study also re-implements the trajectory optimization algorithm for further reduction of computational time through algorithm modifications and integrates that with FACET to facilitate the use of the new features which calculate time-optimal routes between worldwide airport pairs in a wind field for use with existing FACET applications. The implementations of trajectory optimization algorithms use MATLAB, Python, and Java programming languages. The performance evaluations are done by comparing their computational efficiencies and based on the potential application of optimized trajectories. The paper shows that in the absence of special privileges on a supercomputer, a cluster of commercially available computers provides a feasible approach for national and global air traffic system studies.

Optimal control of hybrid vehicles

CERN Document Server

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...

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

A direct method for trajectory optimization of rigid bodies through contact

OpenAIRE

Posa, Michael Antonio; Cantu, Cecilia; Tedrake, Russell Louis

2013-01-01

Direct methods for trajectory optimization are widely used for planning locally optimal trajectories of robotic systems. Many critical tasks, such as locomotion and manipulation, often involve impacting the ground or objects in the environment. Most state-of-the-art techniques treat the discontinuous dynamics that result from impacts as discrete modes and restrict the search for a complete path to a specified sequence through these modes. Here we present a novel method for trajectory planning...

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.

Mirror hybrid reactor optimization studies

International Nuclear Information System (INIS)

Bender, D.J.

1976-01-01

A system model of the mirror hybrid reactor has been developed. The major components of the model include (1) the reactor description, (2) a capital cost analysis, (3) various fuel management schemes, and (4) an economic analysis that includes the hybrid plus its associated fission burner reactors. The results presented describe the optimization of the mirror hybrid reactor, the objective being to minimize the cost of electricity from the hybrid fission-burner reactor complex. We have examined hybrid reactors with two types of blankets, one containing natural uranium, the other thorium. The major difference between the two optimized reactors is that the uranium hybrid is a significant net electrical power producer, whereas the thorium hybrid just about breaks even on electrical power. Our projected costs for fissile fuel production are approximately 50 $/g for 239 Pu and approximately 125 $/g for 233 U

Optimizing interplanetary trajectories with deep space maneuvers

Science.gov (United States)

Navagh, John

1993-09-01

Analysis of interplanetary trajectories is a crucial area for both manned and unmanned missions of the Space Exploration Initiative. A deep space maneuver (DSM) can improve a trajectory in much the same way as a planetary swingby. However, instead of using a gravitational field to alter the trajectory, the on-board propulsion system of the spacecraft is used when the vehicle is not near a planet. The purpose is to develop an algorithm to determine where and when to use deep space maneuvers to reduce the cost of a trajectory. The approach taken to solve this problem uses primer vector theory in combination with a non-linear optimizing program to minimize Delta(V). A set of necessary conditions on the primer vector is shown to indicate whether a deep space maneuver will be beneficial. Deep space maneuvers are applied to a round trip mission to Mars to determine their effect on the launch opportunities. Other studies which were performed include cycler trajectories and Mars mission abort scenarios. It was found that the software developed was able to locate quickly DSM's which lower the total Delta(V) on these trajectories.

Optimization of Vehicular Trajectories under Gaussian Noise Disturbances

Directory of Open Access Journals (Sweden)

Joan Garcia-Haro

2012-12-01

Full Text Available Nowadays, research on Vehicular Technology aims at automating every single mechanical element of vehicles, in order to increase passengers’ safety, reduce human driving intervention and provide entertainment services on board. Automatic trajectory tracing for vehicles under especially risky circumstances is a field of research that is currently gaining enormous attention. In this paper, we show some results on how to develop useful policies to execute maneuvers by a vehicle at high speeds with the mathematical optimization of some already established mobility conditions of the car. We also study how the presence of Gaussian noise on measurement sensors while maneuvering can disturb motion and affect the final trajectories. Different performance criteria for the optimization of such maneuvers are presented, and an analysis is shown on how path deviations can be minimized by using trajectory smoothing techniques like the Kalman Filter. We finalize the paper with a discussion on how communications can be used to implement these schemes.

Automated trajectory planning for multiple-flyby interplanetary missions

Science.gov (United States)

Englander, Jacob

Many space mission planning problems may be formulated as hybrid optimal control problems (HOCP), i.e. problems that include both real-valued variables and categorical variables. In interplanetary trajectory design problems the categorical variables will typically specify the sequence of planets at which to perform flybys, and the real-valued variables will represent the launch date, ight times between planets, magnitudes and directions of thrust, flyby altitudes, etc. The contribution of this work is a framework for the autonomous optimization of multiple-flyby interplanetary trajectories. The trajectory design problem is converted into a HOCP with two nested loops: an "outer-loop" that finds the sequence of flybys and an "inner-loop" that optimizes the trajectory for each candidate yby sequence. The problem of choosing a sequence of flybys is posed as an integer programming problem and solved using a genetic algorithm (GA). This is an especially difficult problem to solve because GAs normally operate on a fixed-length set of decision variables. Since in interplanetary trajectory design the number of flyby maneuvers is not known a priori, it was necessary to devise a method of parameterizing the problem such that the GA can evolve a variable-length sequence of flybys. A novel "null gene" transcription was developed to meet this need. Then, for each candidate sequence of flybys, a trajectory must be found that visits each of the flyby targets and arrives at the final destination while optimizing some cost metric, such as minimizing ▵v or maximizing the final mass of the spacecraft. Three different classes of trajectory are described in this work, each of which requireda different physical model and optimization method. The choice of a trajectory model and optimization method is especially challenging because of the nature of the hybrid optimal control problem. Because the trajectory optimization problem is generated in real time by the outer-loop, the inner

Augmenting Parametric Optimal Ascent Trajectory Modeling with Graph Theory

Science.gov (United States)

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 Trajectory Tracking Control for a Wheeled Mobile Robot Using Fractional Order PID Controller

Directory of Open Access Journals (Sweden)

Ameer L. Saleh

2018-02-01

Full Text Available This paper present an optimal Fractional Order PID (FOPID controller based on Particle Swarm Optimization (PSO for controlling the trajectory tracking of Wheeled Mobile Robot(WMR.The issue of trajectory tracking with given a desired reference velocity is minimized to get the distance and deviation angle equal to zero, to realize the objective of trajectory tracking a two FOPID controllers are used for velocity control and azimuth control to implement the trajectory tracking control. A path planning and path tracking methodologies are used to give different desired tracking trajectories.  PSO algorithm is using to find the optimal parameters of FOPID controllers. The kinematic and dynamic models of wheeled mobile robot for desired trajectory tracking with PSO algorithm are simulated in Simulink-Matlab. Simulation results show that the optimal FOPID controllers are more effective and has better dynamic performance than the conventional methods.

Application of Modern Fortran to Spacecraft Trajectory Design and Optimization

Science.gov (United States)

Williams, Jacob; Falck, Robert D.; Beekman, Izaak B.

2018-01-01

In this paper, applications of the modern Fortran programming language to the field of spacecraft trajectory optimization and design are examined. Modern object-oriented Fortran has many advantages for scientific programming, although many legacy Fortran aerospace codes have not been upgraded to use the newer standards (or have been rewritten in other languages perceived to be more modern). NASA's Copernicus spacecraft trajectory optimization program, originally a combination of Fortran 77 and Fortran 95, has attempted to keep up with modern standards and makes significant use of the new language features. Various algorithms and methods are presented from trajectory tools such as Copernicus, as well as modern Fortran open source libraries and other projects.

Propositional Optimal Trajectory Programming for Improving Stability ...

African Journals Online (AJOL)

Propositional Optimal Trajectory Programming for Improving Stability of Hermite Definite Control System. ... PROMOTING ACCESS TO AFRICAN RESEARCH. AFRICAN JOURNALS ONLINE (AJOL) ... Knowledge of systems operation subjected to heat diffusion constraints is required of systems analysts. In an instance that ...

Hybrid cryptosystem RSA - CRT optimization and VMPC

Science.gov (United States)

Rahmadani, R.; Mawengkang, H.; Sutarman

2018-03-01

Hybrid cryptosystem combines symmetric algorithms and asymmetric algorithms. This combination utilizes speeds on encryption/decryption processes of symmetric algorithms and asymmetric algorithms to secure symmetric keys. In this paper we propose hybrid cryptosystem that combine symmetric algorithms VMPC and asymmetric algorithms RSA - CRT optimization. RSA - CRT optimization speeds up the decryption process by obtaining plaintext with dp and p key only, so there is no need to perform CRT processes. The VMPC algorithm is more efficient in software implementation and reduces known weaknesses in RC4 key generation. The results show hybrid cryptosystem RSA - CRT optimization and VMPC is faster than hybrid cryptosystem RSA - VMPC and hybrid cryptosystem RSA - CRT - VMPC. Keyword : Cryptography, RSA, RSA - CRT, VMPC, Hybrid Cryptosystem.

Implementation of optimal trajectory control of series resonant converter

Science.gov (United States)

Oruganti, Ramesh; Yang, James J.; Lee, Fred C.

1987-01-01

Due to the presence of a high-frequency LC tank circuit, the dynamics of a resonant converter are unpredictable. There is often a large surge of tank energy during transients. Using state-plane analysis technique, an optimal trajectory control utilizing the desired solution trajectory as the control law was previously proposed for the series resonant converters. The method predicts the fastest response possible with minimum energy surge in the resonant tank. The principle of the control and its experimental implementation are described here. The dynamics of the converter are shown to be close to time-optimal.

A Robot Trajectory Optimization Approach for Thermal Barrier Coatings Used for Free-Form Components

Science.gov (United States)

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.

Developing a simulation framework for safe and optimal trajectories considering drivers’ driving style

DEFF Research Database (Denmark)

Gruber, Thierry; Larue, Grégoire S.; Rakotonirainy, Andry

2017-01-01

drivers with the optimal trajectory considering the motorist's driving style in real time. Travel duration and safety are the main parameters used to find the optimal trajectory. A simulation framework to determine the optimal trajectory was developed in which the ego car travels in a highway environment......Advanced driving assistance systems (ADAS) have huge potential for improving road safety and travel times. However, their take-up in the market is very slow; and these systems should consider driver's preferences to increase adoption rates. The aim of this study is to develop a model providing...

Optimal control of stretching process of flexible solar arrays on spacecraft based on a hybrid optimization strategy

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Qijia Yao

2017-07-01

Full Text Available The optimal control of multibody spacecraft during the stretching process of solar arrays is investigated, and a hybrid optimization strategy based on Gauss pseudospectral method (GPM and direct shooting method (DSM is presented. First, the elastic deformation of flexible solar arrays was described approximately by the assumed mode method, and a dynamic model was established by the second Lagrangian equation. Then, the nonholonomic motion planning problem is transformed into a nonlinear programming problem by using GPM. By giving fewer LG points, initial values of the state variables and control variables were obtained. A serial optimization framework was adopted to obtain the approximate optimal solution from a feasible solution. Finally, the control variables were discretized at LG points, and the precise optimal control inputs were obtained by DSM. The optimal trajectory of the system can be obtained through numerical integration. Through numerical simulation, the stretching process of solar arrays is stable with no detours, and the control inputs match the various constraints of actual conditions. The results indicate that the method is effective with good robustness. Keywords: Motion planning, Multibody spacecraft, Optimal control, Gauss pseudospectral method, Direct shooting method

SeGRAm - A practical and versatile tool for spacecraft trajectory optimization

Science.gov (United States)

Rishikof, Brian H.; Mccormick, Bernell R.; Pritchard, Robert E.; Sponaugle, Steven J.

1991-01-01

An implementation of the Sequential Gradient/Restoration Algorithm, SeGRAm, is presented along with selected examples. This spacecraft trajectory optimization and simulation program uses variational calculus to solve problems of spacecraft flying under the influence of one or more gravitational bodies. It produces a series of feasible solutions to problems involving a wide range of vehicles, environments and optimization functions, until an optimal solution is found. The examples included highlight the various capabilities of the program and emphasize in particular its versatility over a wide spectrum of applications from ascent to interplanetary trajectories.

Comparison of kinematic and dynamic leg trajectory optimization techniques for biped robot locomotion

Science.gov (United States)

Khusainov, R.; Klimchik, A.; Magid, E.

2017-01-01

The paper presents comparison analysis of two approaches in defining leg trajectories for biped locomotion. The first one operates only with kinematic limitations of leg joints and finds the maximum possible locomotion speed for given limits. The second approach defines leg trajectories from the dynamic stability point of view and utilizes ZMP criteria. We show that two methods give different trajectories and demonstrate that trajectories based on pure dynamic optimization cannot be realized due to joint limits. Kinematic optimization provides unstable solution which can be balanced by upper body movement.

Optimal bounds and extremal trajectories for time averages in dynamical systems

Science.gov (United States)

Tobasco, Ian; Goluskin, David; Doering, Charles

2017-11-01

For systems governed by differential equations it is natural to seek extremal solution trajectories, maximizing or minimizing the long-time average of a given quantity of interest. A priori bounds on optima can be proved by constructing auxiliary functions satisfying certain point-wise inequalities, the verification of which does not require solving the underlying equations. We prove that for any bounded autonomous ODE, the problems of finding extremal trajectories on the one hand and optimal auxiliary functions on the other are strongly dual in the sense of convex duality. As a result, auxiliary functions provide arbitrarily sharp bounds on optimal time averages. Furthermore, nearly optimal auxiliary functions provide volumes in phase space where maximal and nearly maximal trajectories must lie. For polynomial systems, such functions can be constructed by semidefinite programming. We illustrate these ideas using the Lorenz system, producing explicit volumes in phase space where extremal trajectories are guaranteed to reside. Supported by NSF Award DMS-1515161, Van Loo Postdoctoral Fellowships, and the John Simon Guggenheim Foundation.

Reliability-based trajectory optimization using nonintrusive polynomial chaos for Mars entry mission

Science.gov (United States)

Huang, Yuechen; Li, Haiyang

2018-06-01

This paper presents the reliability-based sequential optimization (RBSO) method to settle the trajectory optimization problem with parametric uncertainties in entry dynamics for Mars entry mission. First, the deterministic entry trajectory optimization model is reviewed, and then the reliability-based optimization model is formulated. In addition, the modified sequential optimization method, in which the nonintrusive polynomial chaos expansion (PCE) method and the most probable point (MPP) searching method are employed, is proposed to solve the reliability-based optimization problem efficiently. The nonintrusive PCE method contributes to the transformation between the stochastic optimization (SO) and the deterministic optimization (DO) and to the approximation of trajectory solution efficiently. The MPP method, which is used for assessing the reliability of constraints satisfaction only up to the necessary level, is employed to further improve the computational efficiency. The cycle including SO, reliability assessment and constraints update is repeated in the RBSO until the reliability requirements of constraints satisfaction are satisfied. Finally, the RBSO is compared with the traditional DO and the traditional sequential optimization based on Monte Carlo (MC) simulation in a specific Mars entry mission to demonstrate the effectiveness and the efficiency of the proposed method.

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

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Narinder Singh

2018-03-01

Full Text Available The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO and Whale Optimizer Algorithm (WOA. We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO algorithm: (i firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

Optimal Point-to-Point Trajectory Tracking of Redundant Manipulators using Generalized Pattern Search

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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.

Optimal bounds and extremal trajectories for time averages in nonlinear dynamical systems

Science.gov (United States)

Tobasco, Ian; Goluskin, David; Doering, Charles R.

2018-02-01

For any quantity of interest in a system governed by ordinary differential equations, it is natural to seek the largest (or smallest) long-time average among solution trajectories, as well as the extremal trajectories themselves. Upper bounds on time averages can be proved a priori using auxiliary functions, the optimal choice of which is a convex optimization problem. We prove that the problems of finding maximal trajectories and minimal auxiliary functions are strongly dual. Thus, auxiliary functions provide arbitrarily sharp upper bounds on time averages. Moreover, any nearly minimal auxiliary function provides phase space volumes in which all nearly maximal trajectories are guaranteed to lie. For polynomial equations, auxiliary functions can be constructed by semidefinite programming, which we illustrate using the Lorenz system.

Original Framework for Optimizing Hybrid Energy Supply

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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.

Trajectory optimization for lunar rover performing vertical takeoff vertical landing maneuvers in the presence of terrain

Science.gov (United States)

Ma, Lin; Wang, Kexin; Xu, Zuhua; Shao, Zhijiang; Song, Zhengyu; Biegler, Lorenz T.

2018-05-01

This study presents a trajectory optimization framework for lunar rover performing vertical takeoff vertical landing (VTVL) maneuvers in the presence of terrain using variable-thrust propulsion. First, a VTVL trajectory optimization problem with three-dimensional kinematics and dynamics model, boundary conditions, and path constraints is formulated. Then, a finite-element approach transcribes the formulated trajectory optimization problem into a nonlinear programming (NLP) problem solved by a highly efficient NLP solver. A homotopy-based backtracking strategy is applied to enhance the convergence in solving the formulated VTVL trajectory optimization problem. The optimal thrust solution typically has a "bang-bang" profile considering that bounds are imposed on the magnitude of engine thrust. An adaptive mesh refinement strategy based on a constant Hamiltonian profile is designed to address the difficulty in locating the breakpoints in the thrust profile. Four scenarios are simulated. Simulation results indicate that the proposed trajectory optimization framework has sufficient adaptability to handle VTVL missions efficiently.

Optimal trajectory planning of free-floating space manipulator using differential evolution algorithm

Science.gov (United States)

Wang, Mingming; Luo, Jianjun; Fang, Jing; Yuan, Jianping

2018-03-01

The existence of the path dependent dynamic singularities limits the volume of available workspace of free-floating space robot and induces enormous joint velocities when such singularities are met. In order to overcome this demerit, this paper presents an optimal joint trajectory planning method using forward kinematics equations of free-floating space robot, while joint motion laws are delineated with application of the concept of reaction null-space. Bézier curve, in conjunction with the null-space column vectors, are applied to describe the joint trajectories. Considering the forward kinematics equations of the free-floating space robot, the trajectory planning issue is consequently transferred to an optimization issue while the control points to construct the Bézier curve are the design variables. A constrained differential evolution (DE) scheme with premature handling strategy is implemented to find the optimal solution of the design variables while specific objectives and imposed constraints are satisfied. Differ from traditional methods, we synthesize null-space and specialized curve to provide a novel viewpoint for trajectory planning of free-floating space robot. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) kinematically redundant manipulator mounted on a free-floating spacecraft and demonstrate the feasibility and effectiveness of the proposed method.

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.

Hybrid systems, optimal control and hybrid vehicles theory, methods and applications

CERN Document Server

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...

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.

Real-time trajectory optimization on parallel processors

Science.gov (United States)

Psiaki, Mark L.

1993-01-01

A parallel algorithm has been developed for rapidly solving trajectory optimization problems. The goal of the work has been to develop an algorithm that is suitable to do real-time, on-line optimal guidance through repeated solution of a trajectory optimization problem. The algorithm has been developed on an INTEL iPSC/860 message passing parallel processor. It uses a zero-order-hold discretization of a continuous-time problem and solves the resulting nonlinear programming problem using a custom-designed augmented Lagrangian nonlinear programming algorithm. The algorithm achieves parallelism of function, derivative, and search direction calculations through the principle of domain decomposition applied along the time axis. It has been encoded and tested on 3 example problems, the Goddard problem, the acceleration-limited, planar minimum-time to the origin problem, and a National Aerospace Plane minimum-fuel ascent guidance problem. Execution times as fast as 118 sec of wall clock time have been achieved for a 128-stage Goddard problem solved on 32 processors. A 32-stage minimum-time problem has been solved in 151 sec on 32 processors. A 32-stage National Aerospace Plane problem required 2 hours when solved on 32 processors. A speed-up factor of 7.2 has been achieved by using 32-nodes instead of 1-node to solve a 64-stage Goddard problem.

End-to-End Trajectory for Conjunction Class Mars Missions Using Hybrid Solar-Electric/Chemical Transportation System

Science.gov (United States)

Chai, Patrick R.; Merrill, Raymond G.; Qu, Min

2016-01-01

NASA's Human Spaceflight Architecture Team is developing a reusable hybrid transportation architecture in which both chemical and solar-electric propulsion systems are used to deliver crew and cargo to exploration destinations. By combining chemical and solar-electric propulsion into a single spacecraft and applying each where it is most effective, the hybrid architecture enables a series of Mars trajectories that are more fuel efficient than an all chemical propulsion architecture without significant increases to trip time. The architecture calls for the aggregation of exploration assets in cislunar space prior to departure for Mars and utilizes high energy lunar-distant high Earth orbits for the final staging prior to departure. This paper presents the detailed analysis of various cislunar operations for the EMC Hybrid architecture as well as the result of the higher fidelity end-to-end trajectory analysis to understand the implications of the design choices on the Mars exploration campaign.

Optimization of Low-Thrust Spiral Trajectories by Collocation

Science.gov (United States)

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.

Map-Based Power-Split Strategy Design with Predictive Performance Optimization for Parallel Hybrid Electric Vehicles

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.

Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neural networks for plug-in hybrid electric vehicles fuel economy

Science.gov (United States)

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.

Global Launcher Trajectory Optimization for Lunar Base Settlement

NARCIS (Netherlands)

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

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.

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.

Multi-Objective Trajectory Optimization of a Hypersonic Reconnaissance Vehicle with Temperature Constraints

Science.gov (United States)

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.

Design Optimization of Hybrid FRP/RC Bridge

Science.gov (United States)

Papapetrou, Vasileios S.; Tamijani, Ali Y.; Brown, Jeff; Kim, Daewon

2018-04-01

The hybrid bridge consists of a Reinforced Concrete (RC) slab supported by U-shaped Fiber Reinforced Polymer (FRP) girders. Previous studies on similar hybrid bridges constructed in the United States and Europe seem to substantiate these hybrid designs for lightweight, high strength, and durable highway bridge construction. In the current study, computational and optimization analyses were carried out to investigate six composite material systems consisting of E-glass and carbon fibers. Optimization constraints are determined by stress, deflection and manufacturing requirements. Finite Element Analysis (FEA) and optimization software were utilized, and a framework was developed to run the complete analyses in an automated fashion. Prior to that, FEA validation of previous studies on similar U-shaped FRP girders that were constructed in Poland and Texas is presented. A finer optimization analysis is performed for the case of the Texas hybrid bridge. The optimization outcome of the hybrid FRP/RC bridge shows the appropriate composite material selection and cross-section geometry that satisfies all the applicable Limit States (LS) and, at the same time, results in the lightest design. Critical limit states show that shear stress criteria determine the optimum design for bridge spans less than 15.24 m and deflection criteria controls for longer spans. Increased side wall thickness can reduce maximum observed shear stresses, but leads to a high weight penalty. A taller cross-section and a thicker girder base can efficiently lower the observed deflections and normal stresses. Finally, substantial weight savings can be achieved by the optimization framework if base and side-wall thickness are treated as independent variables.

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.

Optimal trajectory generation for mechanical arms. M.S. Thesis

Science.gov (United States)

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.

Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

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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.

Trajectory planning and optimal tracking for an industrial mobile robot

Science.gov (United States)

Hu, Huosheng; Brady, J. Michael; Probert, Penelope J.

1994-02-01

This paper introduces a unified approach to trajectory planning and tracking for an industrial mobile robot subject to non-holonomic constraints. We show (1) how a smooth trajectory is generated that takes into account the constraints from the dynamic environment and the robot kinematics; and (2) how a general predictive controller works to provide optimal tracking capability for nonlinear systems. The tracking performance of the proposed guidance system is analyzed by simulation.

Coordinated trajectory planning of dual-arm space robot using constrained particle swarm optimization

Science.gov (United States)

Wang, Mingming; Luo, Jianjun; Yuan, Jianping; Walter, Ulrich

2018-05-01

Application of the multi-arm space robot will be more effective than single arm especially when the target is tumbling. This paper investigates the application of particle swarm optimization (PSO) strategy to coordinated trajectory planning of the dual-arm space robot in free-floating mode. In order to overcome the dynamics singularities issue, the direct kinematics equations in conjunction with constrained PSO are employed for coordinated trajectory planning of dual-arm space robot. The joint trajectories are parametrized with Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for coordinated trajectory planning of two kinematically redundant manipulators mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.

Optimal trajectory planning and train scheduling for urban rail transit systems

CERN Document Server

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...

A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting

International Nuclear Information System (INIS)

Su, Zhongyue; Wang, Jianzhou; Lu, Haiyan; Zhao, Ge

2014-01-01

Highlights: • A new hybrid model is developed for wind speed forecasting. • The model is based on the Kalman filter and the ARIMA. • An intelligent optimization method is employed in the hybrid model. • The new hybrid model has good performance in western China. - Abstract: Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily

Method of interplanetary trajectory optimization for the spacecraft with low thrust and swing-bys

Science.gov (United States)

Konstantinov, M. S.; Thein, M.

2017-07-01

The method developed to avoid the complexity of solving the multipoint boundary value problem while optimizing interplanetary trajectories of the spacecraft with electric propulsion and a sequence of swing-bys is presented in the paper. This method is based on the use of the preliminary problem solutions for the impulsive trajectories. The preliminary problem analyzed at the first stage of the study is formulated so that the analysis and optimization of a particular flight path is considered as the unconstrained minimum in the space of the selectable parameters. The existing methods can effectively solve this problem and make it possible to identify rational flight paths (the sequence of swing-bys) to receive the initial approximation for the main characteristics of the flight path (dates, values of the hyperbolic excess velocity, etc.). These characteristics can be used to optimize the trajectory of the spacecraft with electric propulsion. The special feature of the work is the introduction of the second (intermediate) stage of the research. At this stage some characteristics of the analyzed flight path (e.g. dates of swing-bys) are fixed and the problem is formulated so that the trajectory of the spacecraft with electric propulsion is optimized on selected sites of the flight path. The end-to-end optimization is carried out at the third (final) stage of the research. The distinctive feature of this stage is the analysis of the full set of optimal conditions for the considered flight path. The analysis of the characteristics of the optimal flight trajectories to Jupiter with Earth, Venus and Mars swing-bys for the spacecraft with electric propulsion are presented. The paper shows that the spacecraft weighing more than 7150 kg can be delivered into the vicinity of Jupiter along the trajectory with two Earth swing-bys by use of the space transportation system based on the "Angara A5" rocket launcher, the chemical upper stage "KVTK" and the electric propulsion system

Trajectory optimization for dynamic couch rotation during volumetric modulated arc radiotherapy

Science.gov (United States)

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.

Optimal Collision Avoidance Trajectories for Unmanned/Remotely Piloted Aircraft

Science.gov (United States)

2014-12-26

collocation method to solve this problem and then analyzes these results for di↵erent collision avoidance scenarios. iv To my beautiful “ Proverbs 31” wife... le ( d e g ) Optimal Control JOCA Baseline 0 10 20 30 40 50 60 0.8 1 1.2 1.4 N z Control time (sec) N z Optimal Control JOCA Baseline (b...Optimal Control JOCA Baseline (a) Trajectory Deviation 0 10 20 30 40 50 60 70 −20 −10 0 10 20 µ Control time (sec) a n g le ( d e g

Optimal management with hybrid dynamics : The shallow lake problem

NARCIS (Netherlands)

Reddy, P.V.; Schumacher, Hans; Engwerda, Jacob; Camlibel, M.K.; Julius, A.A.; Pasumarthy, R.

2015-01-01

In this article we analyze an optimal management problem that arises in ecological economics using hybrid systems modeling. First, we introduce a discounted autonomous infinite horizon hybrid optimal control problem and develop few tools to analyze the necessary conditions for optimality. Next,

Optimal design of a hybridization scheme with a fuel cell using genetic optimization

Science.gov (United States)

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

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.

Aerodynamic Shape Optimization Using Hybridized Differential Evolution

Science.gov (United States)

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.

Optimal trajectory control of a CLCC resonant power converter

NARCIS (Netherlands)

Huisman, H.; Visser, de I.; Duarte, J.L.

2015-01-01

A CLCC resonant converter to be used in an isolated power supply is operated using optimal trajectory control (OTC). As a consequence, the converter's inner loop behavior is changed to that of a controlled current source. The controller is implemented in an FPGA. Simulation results and recorded

Development of a Multi-Event Trajectory Optimization Tool for Noise-Optimized Approach Route Design

NARCIS (Netherlands)

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 man in the loop trajectory optimization program (MILTOP)

Science.gov (United States)

Reinfields, J.

1974-01-01

An interactive trajectory optimization program is developed for use in initial fixing of launch configurations. The program is called MILTOP for Man-In-the-Loop-Trajectory Optimization-Program. The program is designed to facilitate quick look studies using man-machine decision combinations to reduce the time required to solve a given problem. MILTOP integrates the equations of motion of a point-mass in 3-Dimensions with drag as the only aerodynamic force present. Any point in time at which an integration step terminates, may be used as a decision-break-point, with complete user control over all variables and routines at this point. Automatic phases are provided for different modes of control: vertical rise, pitch-over, gravity turn, chi-freeze and control turn. Stage parameters are initialized from a separate routine so the user may fly as many stages as his problem demands. The MILTOP system uses both interactively on storage scope consoles, or in batch mode with numerical output on the live printer.

Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs

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Jiajun Liu

2017-10-01

Full Text Available Energy storage systems (ESS play an important role in the performance of mining vehicles. A hybrid ESS combining both batteries (BTs and supercapacitors (SCs is one of the most promising solutions. As a case study, this paper discusses the optimal hybrid ESS sizing and energy management strategy (EMS of 14-ton underground load-haul-dump vehicles (LHDs. Three novel contributions are added to the relevant literature. First, a multi-objective optimization is formulated regarding energy consumption and the total cost of a hybrid ESS, which are the key factors of LHDs, and a battery capacity degradation model is used. During the process, dynamic programming (DP-based EMS is employed to obtain the optimal energy consumption and hybrid ESS power profiles. Second, a 10-year life cycle cost model of a hybrid ESS for LHDs is established to calculate the total cost, including capital cost, operating cost, and replacement cost. According to the optimization results, three solutions chosen from the Pareto front are compared comprehensively, and the optimal one is selected. Finally, the optimal and battery-only options are compared quantitatively using the same objectives, and the hybrid ESS is found to be a more economical and efficient option.

Optimal Acceleration-Velocity-Bounded Trajectory Planning in Dynamic Crowd Simulation

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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.

Optimism and Pessimism as Predictors of Alcohol Use Trajectories in Adolescence

Science.gov (United States)

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 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.

Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory

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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.

New reference trajectory optimization algorithm for a flight management system inspired in beam search

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.

ATM4E: A concept for environmentally-optimized aircraft trajectories

NARCIS (Netherlands)

Matthes, S; Grewe, V.; Lee, D; Linke, F.; Shine, Keith; Stromatas, Stavros

2016-01-01

Trajectory optimisation is one option to reduce air traffic impact on environment. A multidimensional environmental assessment framework is needed to optimize impact on climate, local air quality and noise simultaneously. An interface between flight planning and environmental impact information can

Predictive cruise control in hybrid electric vehicles

NARCIS (Netherlands)

Keulen, T. van; Naus, M.J.G.; Jager, B. de; Molengraft, G.J.L. van de; Steinbuch, M.; Aneke, N.P.I.

2009-01-01

Deceleration rates have considerable influence on the fuel economy of hybrid electric vehicles. Given the vehicle characteristics and actual/measured operating conditions, as well as upcoming route information, optimal velocity trajectories can be constructed that maximize energy recovery. To

Global Optimization Based on the Hybridization of Harmony Search and Particle Swarm Optimization Methods

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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.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

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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.

Multi-Objective Trajectory Optimization of a Hypersonic Reconnaissance Vehicle with Temperature Constraints

Science.gov (United States)

2014-12-26

geocentric gravitational constant ν basis functions ω angular velocity of the Earth Φ fuel-air ratio φ longitude φ optimal control terminal cost (Mayer) xxvi...incorporate sensor parameters. The current methodologies are also numerically inefficient. A trajectory optimization approach , or a general optimal...control software approach , that is computationally ef- ficient and versatile, while based on a robust mathematical foundation, would provide significant

Trajectory optimization for lunar soft landing with complex constraints

Science.gov (United States)

Chu, Huiping; Ma, Lin; Wang, Kexin; Shao, Zhijiang; Song, Zhengyu

2017-11-01

A unified trajectory optimization framework with initialization strategies is proposed in this paper for lunar soft landing for various missions with specific requirements. Two main missions of interest are Apollo-like Landing from low lunar orbit and Vertical Takeoff Vertical Landing (a promising mobility method) on the lunar surface. The trajectory optimization is characterized by difficulties arising from discontinuous thrust, multi-phase connections, jump of attitude angle, and obstacles avoidance. Here R-function is applied to deal with the discontinuities of thrust, checkpoint constraints are introduced to connect multiple landing phases, attitude angular rate is designed to get rid of radical changes, and safeguards are imposed to avoid collision with obstacles. The resulting dynamic problems are generally with complex constraints. The unified framework based on Gauss Pseudospectral Method (GPM) and Nonlinear Programming (NLP) solver are designed to solve the problems efficiently. Advanced initialization strategies are developed to enhance both the convergence and computation efficiency. Numerical results demonstrate the adaptability of the framework for various landing missions, and the performance of successful solution of difficult dynamic problems.

An aircraft noise pollution model for trajectory optimization

Science.gov (United States)

Barkana, A.; Cook, G.

1976-01-01

A mathematical model describing the generation of aircraft noise is developed with the ultimate purpose of reducing noise (noise-optimizing landing trajectories) in terminal areas. While the model is for a specific aircraft (Boeing 737), the methodology would be applicable to a wide variety of aircraft. The model is used to obtain a footprint on the ground inside of which the noise level is at or above 70 dB.

A New Methodology for Solving Trajectory Planning and Dynamic Load-Carrying Capacity of a Robot Manipulator

Directory of Open Access Journals (Sweden)

Wanjin Guo

2016-01-01

Full Text Available A new methodology using a direct method for obtaining the best found trajectory planning and maximum dynamic load-carrying capacity (DLCC is presented for a 5-degree of freedom (DOF hybrid robot manipulator. A nonlinear constrained multiobjective optimization problem is formulated with four objective functions, namely, travel time, total energy involved in the motion, joint jerks, and joint acceleration. The vector of decision variables is defined by the sequence of the time-interval lengths associated with each two consecutive via-points on the desired trajectory of the 5-DOF robot generalized coordinates. Then this vector of decision variables is computed in order to minimize the cost function (which is the weighted sum of these four objective functions subject to constraints on joint positions, velocities, acceleration, jerks, forces/torques, and payload mass. Two separate approaches are proposed to deal with the trajectory planning problem and the maximum DLCC calculation for the 5-DOF robot manipulator using an evolutionary optimization technique. The adopted evolutionary algorithm is the elitist nondominated sorting genetic algorithm (NSGA-II. A numerical application is performed for obtaining best found solutions of trajectory planning and maximum DLCC calculation for the 5-DOF hybrid robot manipulator.

Optimization methods applied to hybrid vehicle design

Science.gov (United States)

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.

Trajectory Optimization of Spray Painting Robot for Complex Curved Surface Based on Exponential Mean Bézier Method

Directory of Open Access Journals (Sweden)

Wei Chen

2017-01-01

Full Text Available Automated tool trajectory planning for spray painting robots is still a challenging problem, especially for a large complex curved surface. This paper presents a new method of trajectory optimization for spray painting robot based on exponential mean Bézier method. The definition and the three theorems of exponential mean Bézier curves are discussed. Then a spatial painting path generation method based on exponential mean Bézier curves is developed. A new simple algorithm for trajectory optimization on complex curved surfaces is introduced. A golden section method is adopted to calculate the values. The experimental results illustrate that the exponential mean Bézier curves enhanced flexibility of the path planning, and the trajectory optimization algorithm achieved satisfactory performance. This method can also be extended to other applications.

Validation of Multibody Program to Optimize Simulated Trajectories II Parachute Simulation with Interacting Forces

Science.gov (United States)

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.

Trajectory Optimization for a Cruising Unmanned Aerial Vehicle Attacking a Target at Back Slope While Subjected to a Wind Gradient

Directory of Open Access Journals (Sweden)

Tieying Jiang

2015-01-01

Full Text Available The trajectory of a tubular launched cruising unmanned aerial vehicle is optimized using the modified direct collocation method for attacking a target at back slope under a wind gradient. A mathematical model of the cruising unmanned aerial vehicle is established based on its operational and motion features under a wind gradient to optimize the trajectory. The motion characteristics of  “altitude adjustment” and “suicide attack” are taken into full account under the combat circumstance of back slope time key targets. By introducing a discrete time function, the trajectory optimization is converted into a nonlinear programming problem and the SNPOT software is applied to solve for the optimal trajectory of the missile under different wind loads. The simulation results show that, for optimized trajectories, the average attack time decreased by up to 29.1% and the energy consumption is reduced by up to 25.9% under specified wind gradient conditions. A, ωdire, and Wmax have an influence on the flight trajectories of cruising unmanned aerial vehicle. This verifies that the application of modified direct collocation method is reasonable and feasible in an effort to achieve more efficient missile trajectories.

Optimal carbon emissions trajectories when damages depend on the rate or level of global warming

International Nuclear Information System (INIS)

Peck, S.C.; Teisberg, T.J.

1994-01-01

The authors extend earlier work with the Carbon Emissions Trajectory Assessment model (CETA) to consider a number of issues relating to the nature of optimal carbon emissions trajectories. They first explore model results when warming costs are associated with the rate of temperature rise, rather than with its level, as in earlier work. It is found that optimal trajectories are more strongly affected by the degree of non-linearity in the warming cost function than by whether the cost function is driven by the warming level or the warming rate. The authors briefly explore the implications of simple uncertainty and risk aversion for optimal emissions trajectories to be somewhat lower, but that the effect is not noticeable in the near term and not dramatic in the long term; the long term effect on the shadow price of carbon is more marked, however. Finally, they experiment with scaling up the warming cost functions until optimal policies are approximately the same as a policy of stabilising emissions at the 1990 level. Based on the results of this experiment, it is concluded that damages would have to be very high to justify anything like a stabilization policy; and even in this case, a policy allowing intertemporal variation in emissions would be better. 18 refs., 15 figs

On-line trajectory planning of time-jerk optimal for robotic arms

Directory of Open Access Journals (Sweden)

Nadir Bendali

2016-09-01

Full Text Available A method based on the computation of the time intervals of the knots for time-jerk optimal planning under kinematic constraints of robot manipulators in predefined 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 can meet the requirements of a short execution time and low arm vibration of the manipulator and the simulation provides good results.

Hybrid Firefly Variants Algorithm for Localization Optimization in WSN

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P. SrideviPonmalar

2017-01-01

Full Text Available Localization is one of the key issues in wireless sensor networks. Several algorithms and techniques have been introduced for localization. Localization is a procedural technique of estimating the sensor node location. In this paper, a novel three hybrid algorithms based on firefly is proposed for localization problem. Hybrid Genetic Algorithm-Firefly Localization Algorithm (GA-FFLA, Hybrid Differential Evolution-Firefly Localization Algorithm (DE-FFLA and Hybrid Particle Swarm Optimization -Firefly Localization Algorithm (PSO-FFLA are analyzed, designed and implemented to optimize the localization error. The localization algorithms are compared based on accuracy of estimation of location, time complexity and iterations required to achieve the accuracy. All the algorithms have hundred percent estimation accuracy but with variations in the number of firefliesr requirements, variation in time complexity and number of iteration requirements. Keywords: Localization; Genetic Algorithm; Differential Evolution; Particle Swarm Optimization

Adaptive hybrid optimal quantum control for imprecisely characterized systems.

Science.gov (United States)

Egger, D J; Wilhelm, F K

2014-06-20

Optimal quantum control theory carries a huge promise for quantum technology. Its experimental application, however, is often hindered by imprecise knowledge of the input variables, the quantum system's parameters. We show how to overcome this by adaptive hybrid optimal control, using a protocol named Ad-HOC. This protocol combines open- and closed-loop optimal control by first performing a gradient search towards a near-optimal control pulse and then an experimental fidelity estimation with a gradient-free method. For typical settings in solid-state quantum information processing, adaptive hybrid optimal control enhances gate fidelities by an order of magnitude, making optimal control theory applicable and useful.

Particle swarm optimization of ascent trajectories of multistage launch vehicles

Science.gov (United States)

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

Optimal energy management of HEVs with hybrid storage system

International Nuclear Information System (INIS)

Vinot, E.; Trigui, R.

2013-01-01

Highlights: • A battery and ultra-capacitor system for parallel hybrid vehicle is considered. • Optimal management using Pontryagin’s minimum principle is developed. • Battery stress limitation is taken into account by means of RMS current. • Rule based management approaching the optimal control is proposed. • Comparison between rule based and optimal management are proposed using Pareto front. - Abstract: Energy storage systems are a key point in the design and development of electric and hybrid vehicles. In order to reduce the battery size and its current stress, a hybrid storage system, where a battery is coupled with an electrical double-layer capacitor (EDLC) is considered in this paper. The energy management of such a configuration is not obvious and the optimal operation concerning the energy consumption and battery RMS current has to be identified. Most of the past work on the optimal energy management of HEVs only considered one additional power source. In this paper, the control of a hybrid vehicle with a hybrid storage system (HSS), where two additional power sources are used, is presented. Applying the Pontryagin’s minimum principle, an optimal energy management strategy is found and compared to a rule-based parameterized control strategy. Simulation results are shown and discussed. Applied on a small compact car, optimal and ruled-based methods show that gains of fuel consumption and/or a battery RMS current higher than 15% may be obtained. The paper also proves that a well tuned rule-based algorithm presents rather good performances when compared to the optimal strategy and remains relevant for different driving cycles. This rule-based algorithm may easily be implemented in a vehicle prototype or in an HIL test bench

A hybrid iterative scheme for optimal control problems governed by ...

African Journals Online (AJOL)

MRT

KEY WORDS: Optimal control problem; Fredholm integral equation; ... control problems governed by Fredholm integral and integro-differential equations is given in (Brunner and Yan, ..... The exact optimal trajectory and control functions are. 2.

Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

Science.gov (United States)

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.

Optimization of extended propulsion time nuclear-electric propulsion trajectories

Science.gov (United States)

Sauer, C. G., Jr.

1981-01-01

This paper presents the methodology used in optimizing extended propulsion time NEP missions considering realistic thruster lifetime constraints. These missions consist of a powered spiral escape from a 700-km circular orbit at the earth, followed by a powered heliocentric transfer with an optimized coast phase, and terminating in a spiral capture phase at the target planet. This analysis is most applicable to those missions with very high energy requirements such as outer planet orbiter missions or sample return missions where the total propulsion time could greatly exceed the expected lifetime of an individual thruster. This methodology has been applied to the investigation of NEP missions to the outer planets where examples are presented of both constrained and optimized trajectories.

Preliminary optimal configuration on free standing hybrid riser

Directory of Open Access Journals (Sweden)

Kyoung-Su Kim

2018-05-01

Full Text Available Free Standing Hybrid Riser (FSHR is comprised of vertical steel risers and Flexible Jumpers (FJ. They are jointly connected to a submerged Buoyancy Can (BC. There are several factors that have influence on the behavior of FSHR such as the span distance between an offshore platform and a foundation, BC up-lift force, BC submerged location and FJ length.An optimization method through a parametric study is presented. Firstly, descriptions for the overall arrangement and characteristics of FSHR are introduced. Secondly, a flowchart for optimization of FSHR is suggested. Following that, it is described how to select reasonable ranges for a parametric study and determine each of optimal configuration options. Lastly, numerical analysis based on this procedure is performed through a case study. In conclusion, the relation among those parameters is analyzed and non-dimensional parametric ranges on optimal arrangements are suggested. Additionally, strength analysis is performed with variation in the configuration. Keywords: Free standing hybrid riser, Hybrid riser system, Buoyancy can, Flexible jumper, Deepwater, Multi-body dynamics

Hybrid uncertainty-based design optimization and its application to hybrid rocket motors for manned lunar landing

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.

Hybrid uncertainty-based design optimization and its application to hybrid rocket motors for manned lunar landing

Institute of Scientific and Technical Information of China (English)

Zhu Hao; Tian Hui; Cai Guobiao

2017-01-01

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 uncer-tainty 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.

The Effects of Reducing Preparation Time on the Execution of Intentionally Curved Trajectories: Optimization and Geometrical Analysis

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Dovrat Kohen

2017-06-01

Full Text Available When subjects are intentionally preparing a curved trajectory, they are engaged in a time-consuming trajectory planning process that is separate from target selection. To investigate the construction of such a plan, we examined the effect of artificially shortening preparation time on the performance of intentionally curved trajectories using the Timed Response task that enforces initiation of movements prematurely. Fifteen subjects performed obstacle avoidance movements toward one of four targets that were presented 25 or 350 ms before the “go” signal, imposing short and long preparation time conditions with mean values of 170 ms and 493 ms, respectively. While trajectories with short preparation times showed target specificity at their onset, they were significantly more variable and showed larger angular deviations from the lines connecting their initial position and the target, compared to the trajectories with long preparation times. Importantly, the trajectories of the short preparation time movements still reached their end-point targets accurately, with comparable movement durations. We hypothesize that success in the short preparation time condition is a result of an online control mechanism that allows further refinement of the plan during its execution and study this control mechanism with a novel trajectory analysis approach using minimum jerk optimization and geometrical modeling approaches. Results show a later agreement of the short preparation time trajectories with the optimal minimum jerk trajectory, accompanied by a later initiation of a parabolic segment. Both observations are consistent with the existence of an online trajectory planning process.Our results suggest that when preparation time is not sufficiently long, subjects execute a more variable and less optimally prepared initial trajectory and exploit online control mechanisms to refine their actions on the fly.

The Effects of Reducing Preparation Time on the Execution of Intentionally Curved Trajectories: Optimization and Geometrical Analysis

Science.gov (United States)

Kohen, Dovrat; Karklinsky, Matan; Meirovitch, Yaron; Flash, Tamar; Shmuelof, Lior

2017-01-01

When subjects are intentionally preparing a curved trajectory, they are engaged in a time-consuming trajectory planning process that is separate from target selection. To investigate the construction of such a plan, we examined the effect of artificially shortening preparation time on the performance of intentionally curved trajectories using the Timed Response task that enforces initiation of movements prematurely. Fifteen subjects performed obstacle avoidance movements toward one of four targets that were presented 25 or 350 ms before the “go” signal, imposing short and long preparation time conditions with mean values of 170 ms and 493 ms, respectively. While trajectories with short preparation times showed target specificity at their onset, they were significantly more variable and showed larger angular deviations from the lines connecting their initial position and the target, compared to the trajectories with long preparation times. Importantly, the trajectories of the short preparation time movements still reached their end-point targets accurately, with comparable movement durations. We hypothesize that success in the short preparation time condition is a result of an online control mechanism that allows further refinement of the plan during its execution and study this control mechanism with a novel trajectory analysis approach using minimum jerk optimization and geometrical modeling approaches. Results show a later agreement of the short preparation time trajectories with the optimal minimum jerk trajectory, accompanied by a later initiation of a parabolic segment. Both observations are consistent with the existence of an online trajectory planning process.Our results suggest that when preparation time is not sufficiently long, subjects execute a more variable and less optimally prepared initial trajectory and exploit online control mechanisms to refine their actions on the fly. PMID:28706478

Numerical study and ex vivo assessment of HIFU treatment time reduction through optimization of focal point trajectory

Science.gov (United States)

Grisey, A.; Yon, S.; Pechoux, T.; Letort, V.; Lafitte, P.

2017-03-01

Treatment time reduction is a key issue to expand the use of high intensity focused ultrasound (HIFU) surgery, especially for benign pathologies. This study aims at quantitatively assessing the potential reduction of the treatment time arising from moving the focal point during long pulses. In this context, the optimization of the focal point trajectory is crucial to achieve a uniform thermal dose repartition and avoid boiling. At first, a numerical optimization algorithm was used to generate efficient trajectories. Thermal conduction was simulated in 3D with a finite difference code and damages to the tissue were modeled using the thermal dose formula. Given an initial trajectory, the thermal dose field was first computed, then, making use of Pontryagin's maximum principle, the trajectory was iteratively refined. Several initial trajectories were tested. Then, an ex vivo study was conducted in order to validate the efficicency of the resulting optimized strategies. Single pulses were performed at 3MHz on fresh veal liver samples with an Echopulse and the size of each unitary lesion was assessed by cutting each sample along three orthogonal planes and measuring the dimension of the whitened area based on photographs. We propose a promising approach to significantly shorten HIFU treatment time: the numerical optimization algorithm was shown to provide a reliable insight on trajectories that can improve treatment strategies. The model must now be improved in order to take in vivo conditions into account and extensively validated.

Kinematic Optimization of Robot Trajectories for Thermal Spray Coating Application

Science.gov (United States)

Deng, Sihao; Liang, Hong; Cai, Zhenhua; Liao, Hanlin; Montavon, Ghislain

2014-12-01

Industrial robots are widely used in the field of thermal spray nowadays. Due to their characteristics of high-accuracy and programmable flexibility, spraying on complex geometrical workpieces can be realized in the equipped spray room. However, in some cases, the robots cannot guarantee the process parameters defined by the robot movement, such as the scanning trajectory, spray angle, relative speed between the torch and the substrate, etc., which have distinct influences on heat and mass transfer during the generation of any thermally sprayed coatings. In this study, an investigation on the robot kinematics was proposed to find the rules of motion in a common case. The results showed that the motion behavior of each axis of robot permits to identify the motion problems in the trajectory. This approach allows to optimize the robot trajectory generation in a limited working envelop. It also minimizes the influence of robot performance to achieve a more constant relative scanning speed which is represented as a key parameter in thermal spraying.

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...... on optimizing a very time consuming operation in GPAW, the finite-different stencil operation, and different hybrid programming approaches are evaluated. The work succeeds in demonstrating a hybrid programming model which is clearly beneficial compared to the original flat programming model. In total...... an improvement of 1.94 compared to the original implementation is obtained. The results we demonstrate here are reasonably general and may be applied to other finite difference codes....

Terminal Control Area Aircraft Scheduling and Trajectory Optimization Approaches

Directory of Open Access Journals (Sweden)

Samà Marcella

2017-01-01

Full Text Available Aviation authorities are seeking optimization methods to better use the available infrastructure and better manage aircraft movements. This paper deals with the realtime scheduling of take-off and landing aircraft at a busy terminal control area and with the optimization of aircraft trajectories during the landing procedures. The first problem aims to reduce the propagation of delays, while the second problem aims to either minimize the travel time or reduce the fuel consumption. Both problems are particularly complex, since the first one is NP-hard while the second one is nonlinear and a combined solution needs to be computed in a short-time during operations. This paper proposes a framework for the lexicographic optimization of the two problems. Computational experiments are performed for the Milano Malpensa airport and show the existing gaps between the performance indicators of the two problems when different lexicographic optimization approaches are considered.

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

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Narinder Singh

2017-01-01

Full Text Available A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO and Grey Wolf Optimizer (GWO. The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer to produce both variants’ strength. Some unimodal, multimodal, and fixed-dimension multimodal test functions are used to check the solution quality and performance of HPSOGWO variant. The numerical and statistical solutions show that the hybrid variant outperforms significantly the PSO and GWO variants in terms of solution quality, solution stability, convergence speed, and ability to find the global optimum.

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.

Energy Optimal Trajectories in Human Arm Motion Aiming for Assistive Robots

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Lelai Zhou

2017-01-01

Full Text Available The energy expenditure in human arm has been of great interests for seeking optimal human arm trajectories. This paper presents a new way for calculating metabolic energy consumption of human arm motions. The purpose is to reveal the relationship between the energy consumption and the trajectory of arm motion, and further, the acceleration and arm orientation contributions. Human arm motion in horizontal plane is investigated by virtue of Qualisys motion capture system. The motion data is post-processed by a biomechanical model to obtain the metabolic expenditure. Results on the arm motion kinematics, dynamics and metabolic energy consumption, are included.

Optimal energy management for a flywheel-based hybrid vehicle

NARCIS (Netherlands)

Berkel, van K.; Hofman, T.; Vroemen, B.G.; Steinbuch, M.

2011-01-01

This paper presents the modeling and design of an optimal Energy Management Strategy (EMS) for a flywheel-based hybrid vehicle, that does not use any electrical motor/generator, or a battery, for its hybrid functionalities. The hybrid drive train consists of only low-cost components, such as a

A new hybrid optimization algorithm CRO-DE for optimal coordination of overcurrent relays in complex power systems

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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.

Trajectory Tracking of a Tri-Rotor Aerial Vehicle Using an MRAC-Based Robust Hybrid Control Algorithm

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Zain Anwar Ali

2017-01-01

Full Text Available In this paper, a novel Model Reference Adaptive Control (MRAC-based hybrid control algorithm is presented for the trajectory tracking of a tri-rotor Unmanned Aerial Vehicle (UAV. The mathematical model of the tri-rotor is based on the Newton–Euler formula, whereas the MRAC-based hybrid controller consists of Fuzzy Proportional Integral Derivative (F-PID and Fuzzy Proportional Derivative (F-PD controllers. MRAC is used as the main controller for the dynamics, while the parameters of the adaptive controller are fine-tuned by the F-PD controller for the altitude control subsystem and the F-PID controller for the attitude control subsystem of the UAV. The stability of the system is ensured and proven by Lyapunov stability analysis. The proposed control algorithm is tested and verified using computer simulations for the trajectory tracking of the desired path as an input. The effectiveness of our proposed algorithm is compared with F-PID and the Fuzzy Logic Controller (FLC. Our proposed controller exhibits much less steady state error, quick error convergence in the presence of disturbance or noise, and model uncertainties.

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®.

Optimal trajectories for flexible-link manipulator slewing using recursive quadratic programming: Experimental verification

International Nuclear Information System (INIS)

Parker, G.G.; Eisler, G.R.; Feddema, J.T.

1994-01-01

Procedures for trajectory planning and control of flexible link robots are becoming increasingly important to satisfy performance requirements of hazardous waste removal efforts. It has been shown that utilizing link flexibility in designing open loop joint commands can result in improved performance as opposed to damping vibration throughout a trajectory. The efficient use of link compliance is exploited in this work. Specifically, experimental verification of minimum time, straight line tracking using a two-link planar flexible robot is presented. A numerical optimization process, using an experimentally verified modal model, is used for obtaining minimum time joint torque and angle histories. The optimal joint states are used as commands to the proportional-derivative servo actuated joints. These commands are precompensated for the nonnegligible joint servo actuator dynamics. Using the precompensated joint commands, the optimal joint angles are tracked with such fidelity that the tip tracking error is less than 2.5 cm

Implementation of an optimal control energy management strategy in a hybrid truck

NARCIS (Netherlands)

Mullem, D. van; Keulen, T. van; Kessels, J.T.B.A.; Jager, B. de; Steinbuch, M.

2010-01-01

Energy Management Strategies for hybrid powertrains control the power split, between the engine and electric motor, of a hybrid vehicle, with fuel consumption or emission minimization as objective. Optimal control theory can be applied to rewrite the optimization problem to an optimization

Adaptive Mesh Iteration Method for Trajectory Optimization Based on Hermite-Pseudospectral Direct Transcription

Directory of Open Access Journals (Sweden)

Humin Lei

2017-01-01

Full Text Available An adaptive mesh iteration method based on Hermite-Pseudospectral is described for trajectory optimization. The method uses the Legendre-Gauss-Lobatto points as interpolation points; then the state equations are approximated by Hermite interpolating polynomials. The method allows for changes in both number of mesh points and the number of mesh intervals and produces significantly smaller mesh sizes with a higher accuracy tolerance solution. The derived relative error estimate is then used to trade the number of mesh points with the number of mesh intervals. The adaptive mesh iteration method is applied successfully to the examples of trajectory optimization of Maneuverable Reentry Research Vehicle, and the simulation experiment results show that the adaptive mesh iteration method has many advantages.

Hybrid metaheuristic optimization algorithm for strategic planning of {4D} aircraft trajectories at the continent scale

OpenAIRE

Chaimatanan , Supatcha; Delahaye , Daniel; Mongeau , Marcel

2014-01-01

International audience; Global air-traffic demand is continuously increasing. To handle such a tremendous traffic volume while maintaining at least the same level of safety, a more efficient strategic trajectory planning is necessary. In this work, we present a strategic trajectory planning methodology which aims to minimize interaction between aircraft at the European-continent scale. In addition, we propose a preliminary study that takes into account uncertainties of aircraft positions in t...

Modeling and optimization of batteryless hybrid PV (photovoltaic)/Diesel systems for off-grid applications

International Nuclear Information System (INIS)

Tsuanyo, David; Azoumah, Yao; Aussel, Didier; Neveu, Pierre

2015-01-01

This paper presents a new model and optimization procedure for off-grid hybrid PV (photovoltaic)/Diesel systems operating without battery storage. The proposed technico-economic model takes into account the variability of both the solar irradiation and the electrical loads. It allows optimizing the design and the operation of the hybrid systems by searching their lowest LCOE (Levelized Cost of Electricity). Two cases have been investigated: identical Diesel generators and Diesel generators with different sizes, and both are compared to conventional standalone Diesel generator systems. For the same load profile, the optimization results show that the LCOE of the optimized batteryless hybrid solar PV/Diesel (0.289 €/kWh for the hybrid system with identical Diesel generators and 0.284 €/kWh for the hybrid system with different sizes of Diesel generators) is lower than the LCOE obtained with standalone Diesel generators (0.32 €/kWh for the both cases). The obtained results are then confirmed by HOMER (Hybrid Optimization Model for Electric Renewables) software. - Highlights: • A technico-economic model for optimal design and operation management of batteryless hybrid systems is developed. • The model allows optimizing design and operation of hybrid systems by ensuring their lowest LCOE. • The model was validated by HOMER. • Batteryless hybrid system are suitable for off-grid applications

Optimization of geothermal well trajectory in order to minimize borehole failure

Science.gov (United States)

Dahrabou, A.; Valley, B.; Ladner, F.; Guinot, F.; Meier, P.

2017-12-01

In projects based on Enhanced Geothermal System (EGS) principle, deep boreholes are drilled to low permeability rock masses. As part of the completion operations, the permeability of existing fractures in the rock mass is enhanced by injecting large volumes of water. These stimulation treatments aim at achieving enough water circulation for heat extraction at commercial rates which makes the stimulation operations critical to the project success. The accurate placement of the stimulation treatments requires well completion with effective zonal isolation, and wellbore stability is a prerequisite to all zonal isolation techniques, be it packer sealing or cement placement. In this project, a workflow allowing a fast decision-making process for selecting an optimal well trajectory for EGS projects is developed. In fact, the well is first drilled vertically then based on logging data which are costly (100 KCHF/day), the direction in which the strongly deviated borehole section will be drilled needs to be determined in order to optimize borehole stability and to intersect the highest number of fractures that are oriented favorably for stimulation. The workflow applies to crystalline rock and includes an uncertainty and risk assessment framework. An initial sensitivity study was performed to identify the most influential parameters on borehole stability. The main challenge in these analyses is that the strength and stress profiles are unknown independently. Calibration of a geomechanical model on the observed borehole failure has been performed using data from the Basel Geothermal well BS-1. In a first approximation, a purely elastic-static analytical solution in combination with a purely cohesive failure criterion were used as it provides the most consistent prediction across failure indicators. A systematic analysis of the uncertainty on all parameters was performed to assess the reliability of the optimal trajectory selection. To each drilling scenario, failure

TH-EF-BRB-02: Feasibility of Optimization for Dynamic Trajectory Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Fix, MK; Frei, D; Volken, W; Terribilini, D; Aebersold, DM; Manser, P [Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Berne (Switzerland)

2016-06-15

Purpose: Over the last years, volumetric modulated arc therapy (VMAT) has been widely introduced into clinical routine using a coplanar delivery technique. However, VMAT might be improved by including dynamic couch and collimator rotations, leading to dynamic trajectory radiotherapy (DTRT). In this work the feasibility and the potential benefit of DTRT was investigated. Methods: A general framework for the optimization was developed using the Eclipse Scripting Research Application Programming Interface (ESRAPI). Based on contoured target and organs at risk (OARs), the structures are extracted using the ESRAPI. Sampling potential beam directions, regularly distributed on a sphere using a Fibanocci-lattice, the fractional volume-overlap of each OAR and the target is determined and used to establish dynamic gantry-couch movements. Then, for each gantry-couch track the most suitable collimator angle is determined for each control point by optimizing the area between the MLC leaves and the target contour. The resulting dynamic trajectories are used as input to perform the optimization using a research version of the VMAT optimization algorithm and the ESRAPI. The feasibility of this procedure was tested for a clinically motivated head and neck case. Resulting dose distributions for the VMAT plan and for the dynamic trajectory treatment plan were compared based on DVH-parameters. Results: While the DVH for the target is virtually preserved, improvements in maximum dose for the DTRT plan were achieved for all OARs except for the inner-ear, where maximum dose remains the same. The major improvements in maximum dose were 6.5% of the prescribed dose (66 Gy) for the parotid and 5.5% for the myelon and the eye. Conclusion: The result of this work suggests that DTRT has a great potential to reduce dose to OARs with similar target coverage when compared to conventional VMAT treatment plans. This work was supported by Varian Medical Systems. This work was supported by Varian

Real-time aircraft continuous descent trajectory optimization with ATC time constraints using direct collocation methods.

OpenAIRE

Verhoeven, Ronald; Dalmau Codina, Ramon; Prats Menéndez, Xavier; de Gelder, Nico

2014-01-01

1 Abstract In this paper an initial implementation of a real - time aircraft trajectory optimization algorithm is presented . The aircraft trajectory for descent and approach is computed for minimum use of thrust and speed brake in support of a “green” continuous descent and approach flight operation, while complying with ATC time constraints for maintaining runway throughput and co...

Thermal resistance analysis and optimization of photovoltaic-thermoelectric hybrid system

International Nuclear Information System (INIS)

Yin, Ershuai; Li, Qiang; Xuan, Yimin

2017-01-01

Highlights: • A detailed thermal resistance analysis of the PV-TE hybrid system is proposed. • c-Si PV and p-Si PV cells are proved to be inapplicable for the PV-TE hybrid system. • Some criteria for selecting coupling devices and optimal design are obtained. • A detailed process of designing the practical PV-TE hybrid system is provided. - Abstract: The thermal resistance theory is introduced into the theoretical model of the photovoltaic-thermoelectric (PV-TE) hybrid system. A detailed thermal resistance analysis is proposed to optimize the design of the coupled system in terms of optimal total conversion efficiency. Systems using four types of photovoltaic cells are investigated, including monocrystalline silicon photovoltaic cell, polycrystalline silicon photovoltaic cell, amorphous silicon photovoltaic cell and polymer photovoltaic cell. Three cooling methods, including natural cooling, forced air cooling and water cooling, are compared, which demonstrates a significant superiority of water cooling for the concentrating photovoltaic-thermoelectric hybrid system. Influences of the optical concentrating ratio and velocity of water are studied together and the optimal values are revealed. The impacts of the thermal resistances of the contact surface, TE generator and the upper heat loss thermal resistance on the property of the coupled system are investigated, respectively. The results indicate that amorphous silicon PV cell and polymer PV cell are more appropriate for the concentrating hybrid system. Enlarging the thermal resistance of the thermoelectric generator can significantly increase the performance of the coupled system using amorphous silicon PV cell or polymer PV cell.

A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design.

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.

Multi-objective trajectory optimization of Space Manoeuvre Vehicle using adaptive differential evolution and modified game theory

Science.gov (United States)

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.

Hybrid computer optimization of systems with random parameters

Science.gov (United States)

White, R. C., Jr.

1972-01-01

A hybrid computer Monte Carlo technique for the simulation and optimization of systems with random parameters is presented. The method is applied to the simultaneous optimization of the means and variances of two parameters in the radar-homing missile problem treated by McGhee and Levine.

Power and mass optimization of the hybrid solar panel and thermoelectric generators

International Nuclear Information System (INIS)

Kwan, Trevor Hocksun; Wu, Xiaofeng

2016-01-01

Highlights: • The dynamics of the hybrid PV/TEG system operating in outer space is studied. • A generalized thermodynamic model of the hybrid PV/TEG system is given. • This model is then simplified to consider the outer space scenario. • The design of the hybrid PV/TEG system is optimized using the NSGA-II algorithm. • The optimized hybrid system is more efficient than its monolithic counterparts. - Abstract: The thermoelectric generator (TEG) has been widely considered as an electrical power source in many ground applications because of its clean and noiseless characteristics. Moreover, the hybrid photovoltaic cell and TEG (PV/TEG) system has also received wide attention due to its improved power conversion efficiency over its monolithic counterparts. This paper presents a study of the dynamics and the operation of the hybrid PV/TEG system in an outer space environment where a unified thermodynamic model of this system is presented. Moreover, the multi-objective NSGA-II genetic algorithm is utilized to optimize the design of the TEG both in terms of optimal output power and in terms of mass. Specifically, the design of the single stage and the two stage variant of the aforementioned TEG are considered. Simulation results indicate that the optimized PV/TEG system does indeed achieve better efficiencies than that of the monolithic counterparts. Furthermore, it is shown that the single stage TEG is more beneficial than the two stage TEG in terms of achieving optimal performance.

Hybrid Design Optimization of High Voltage Pulse Transformers for Klystron Modulators

CERN Document Server

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.

Genetic algorithm based optimization on modeling and design of hybrid renewable energy systems

International Nuclear Information System (INIS)

Ismail, M.S.; Moghavvemi, M.; Mahlia, T.M.I.

2014-01-01

Highlights: • Solar data was analyzed in the location under consideration. • A program was developed to simulate operation of the PV hybrid system. • Genetic algorithm was used to optimize the sizes of the hybrid system components. • The costs of the pollutant emissions were considered in the optimization. • It is cost effective to power houses in remote areas with such hybrid systems. - Abstract: A sizing optimization of a hybrid system consisting of photovoltaic (PV) panels, a backup source (microturbine or diesel), and a battery system minimizes the cost of energy production (COE), and a complete design of this optimized system supplying a small community with power in the Palestinian Territories is presented in this paper. A scenario that depends on a standalone PV, and another one that depends on a backup source alone were analyzed in this study. The optimization was achieved via the usage of genetic algorithm. The objective function minimizes the COE while covering the load demand with a specified value for the loss of load probability (LLP). The global warming emissions costs have been taken into account in this optimization analysis. Solar radiation data is firstly analyzed, and the tilt angle of the PV panels is then optimized. It was discovered that powering a small rural community using this hybrid system is cost-effective and extremely beneficial when compared to extending the utility grid to supply these remote areas, or just using conventional sources for this purpose. This hybrid system decreases both operating costs and the emission of pollutants. The hybrid system that realized these optimization purposes is the one constructed from a combination of these sources

Hierarchical models and iterative optimization of hybrid systems

Energy Technology Data Exchange (ETDEWEB)

Rasina, Irina V. [Ailamazyan Program Systems Institute, Russian Academy of Sciences, Peter One str. 4a, Pereslavl-Zalessky, 152021 (Russian Federation); Baturina, Olga V. [Trapeznikov Control Sciences Institute, Russian Academy of Sciences, Profsoyuznaya str. 65, 117997, Moscow (Russian Federation); Nasatueva, Soelma N. [Buryat State University, Smolina str.24a, Ulan-Ude, 670000 (Russian Federation)

2016-06-08

A class of hybrid control systems on the base of two-level discrete-continuous model is considered. The concept of this model was proposed and developed in preceding works as a concretization of the general multi-step system with related optimality conditions. A new iterative optimization procedure for such systems is developed on the base of localization of the global optimality conditions via contraction the control set.

Optimizations of spin-exchange relaxation-free magnetometer based on potassium and rubidium hybrid optical pumping

International Nuclear Information System (INIS)

Fang, Jiancheng; Wang, Tao; Li, Yang; Zhang, Hong; Zou, Sheng

2014-01-01

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

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

Operation management of daily economic dispatch using novel hybrid particle swarm optimization and gravitational search algorithm with hybrid mutation strategy

Science.gov (United States)

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.

SFC Optimization for Aero Engine Based on Hybrid GA-SQP Method

Science.gov (United States)

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.

Feasibility Study and Optimization of An Hybrid System (Eolian ...

African Journals Online (AJOL)

Feasibility Study and Optimization of An Hybrid System (Eolian- Photovoltaic - Diesel) With Provision of Electric Energy Completely Independent. ... reducing emissions of greenhouse gas (CO2 rate = 16086 kg / year for a system using only the generator diesel and is 599 kg / year for the stand alone hybrid system studied).

Parameter Identification of Static Friction Based on An Optimal Exciting Trajectory

Science.gov (United States)

Tu, X.; Zhao, P.; Zhou, Y. F.

2017-12-01

In this paper, we focus on how to improve the identification efficiency of friction parameters in a robot joint. First, the static friction model that has only linear dependencies with respect to their parameters is adopted so that the servomotor dynamics can be linearized. In this case, the traditional exciting trajectory based on Fourier series is modified by replacing the constant term with quintic polynomial to ensure the boundary continuity of speed and acceleration. Then, the Fourier-related parameters are optimized by genetic algorithm(GA) in which the condition number of regression matrix is set as the fitness function. At last, compared with the constant-velocity tracking experiment, the friction parameters from the exciting trajectory experiment has the similar result with the advantage of time reduction.

Optimal Energy Management in Hybrid Electric Trucks Using Route Information Optimisation de la gestion de l’énergie dans des véhicules poids lourds électriques hybrides utilisant le guidage d’itinéraire

Directory of Open Access Journals (Sweden)

van Keulen T.

2009-08-01

Full Text Available To benchmark a hybrid vehicle’s Energy Management Strategy (EMS usually a given, often certified, velocity trajectory is exploited. In this paper it is reasoned that it is also beneficial to optimize the velocity trajectory. Especially optimizing the vehicle braking trajectories, through maximization of energy recuperation, results in considerable fuel savings on the same traveled distance. Given future route (target velocities as function of traveled distance/location, traffic, and possibly weather information, together with the vehicle’s road load parameters, the future power request trajectory can be estimated. Dynamic Programming (DP techniques can then be used to predict the optimal power split trajectory for the upcoming route, such that a desired state-of-charge at the end of the route is reached. The DP solution is re-calculated at a certain rate in order to adapt to changing conditions, e.g., traffic conditions, and used in a lower level real-time EMS to guarantee both battery state-of-charge as well as minimal fuel consumption. Pour évaluer la Stratégie de Gestion de l’Énergie (SGE d’un véhicule hybride, on exploite généralement un cycle de conduite donné, souvent certifié. Dans cet article, l’optimisation de l’itinéraire apparaît aussi comme nécessaire. L’optimisation, en particulier, des conditions de freinage du véhicule, par la maximisation de la récupération d’énergie, permet des économies considérables de combustible sur une même distance parcourue. Pour un itinéraire donné (vitesses cibles en fonction de la distance parcourue et de la position, compte tenu des conditions de circulation, des éventuelles données météorologiques et des paramètres de perte du véhicule, on peut estimer les besoins en puissance nécessaire pour le parcourir. Des techniques de Programmation Dynamique (PD peuvent alors être employées pour prévoir la répartition de puissance optimale pour un parcours donn

Optimizing Likelihood Models for Particle Trajectory Segmentation in Multi-State Systems.

Science.gov (United States)

Young, Dylan Christopher; Scrimgeour, Jan

2018-06-19

Particle tracking offers significant insight into the molecular mechanics that govern the behav- ior of living cells. The analysis of molecular trajectories that transition between different motive states, such as diffusive, driven and tethered modes, is of considerable importance, with even single trajectories containing significant amounts of information about a molecule's environment and its interactions with cellular structures. Hidden Markov models (HMM) have been widely adopted to perform the segmentation of such complex tracks. In this paper, we show that extensive analysis of hidden Markov model outputs using data derived from multi-state Brownian dynamics simulations can be used both for the optimization of the likelihood models used to describe the states of the system and for characterization of the technique's failure mechanisms. This analysis was made pos- sible by the implementation of parallelized adaptive direct search algorithm on a Nvidia graphics processing unit. This approach provides critical information for the visualization of HMM failure and successful design of particle tracking experiments where trajectories contain multiple mobile states. © 2018 IOP Publishing Ltd.

Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

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.

A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

Directory of Open Access Journals (Sweden)

Qingyang Zhang

2015-02-01

Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

Optimization of hybrid model on hajj travel

Science.gov (United States)

Cahyandari, R.; Ariany, R. L.; Sukono

2018-03-01

Hajj travel insurance is an insurance product offered by the insurance company in preparing funds to perform the pilgrimage. This insurance product helps would-be pilgrims to set aside a fund of saving hajj with regularly, but also provides funds of profit sharing (mudharabah) and insurance protection. Scheme of insurance product fund management is largely using the hybrid model, which is the fund from would-be pilgrims will be divided into three account management, that is personal account, tabarru’, and ujrah. Scheme of hybrid model on hajj travel insurance was already discussed at the earlier paper with titled “The Hybrid Model Algorithm on Sharia Insurance”, taking the example case of Mitra Mabrur Plus product from Bumiputera company. On these advanced paper, will be made the previous optimization model design, with partition of benefit the tabarru’ account. Benefits such as compensation for 40 critical illness which initially only for participants of insurance only, on optimization is intended for participants of the insurance and his heir, also to benefit the hospital bills. Meanwhile, the benefits of death benefit is given if the participant is fixed die.

Simultaneous Optimization of Topology and Component Sizes for Double Planetary Gear Hybrid Powertrains

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.

Commercial Aircraft Trajectory Planning based on Multiphase Mixed-Integer Optimal Control

OpenAIRE

Soler Arnedo, Manuel Fernando

2017-01-01

The main goal of this dissertation is to develop optimal control techniques for aircraft trajectory planning looking at reduction of fuel consumption, emissions and overfly charges in flight plans. The calculation of a flight plan involves the consideration of multiple factors. They can be classified as either continuous or discrete, and include nonlinear aircraft performance, atmospheric conditions, wind conditions, airspace structure, amount of departure fuel, and operational...

Gravity-Assist Trajectories to the Ice Giants: An Automated Method to Catalog Mass-or Time-Optimal Solutions

Science.gov (United States)

Hughes, Kyle M.; Knittel, Jeremy M.; Englander, Jacob A.

2017-01-01

This work presents an automated method of calculating mass (or time) optimal gravity-assist trajectories without a priori knowledge of the flyby-body combination. Since gravity assists are particularly crucial for reaching the outer Solar System, we use the Ice Giants, Uranus and Neptune, as example destinations for this work. Catalogs are also provided that list the most attractive trajectories found over launch dates ranging from 2024 to 2038. The tool developed to implement this method, called the Python EMTG Automated Trade Study Application (PEATSA), iteratively runs the Evolutionary Mission Trajectory Generator (EMTG), a NASA Goddard Space Flight Center in-house trajectory optimization tool. EMTG finds gravity-assist trajectories with impulsive maneuvers using a multiple-shooting structure along with stochastic methods (such as monotonic basin hopping) and may be run with or without an initial guess provided. PEATSA runs instances of EMTG in parallel over a grid of launch dates. After each set of runs completes, the best results within a neighborhood of launch dates are used to seed all other cases in that neighborhood---allowing the solutions across the range of launch dates to improve over each iteration. The results here are compared against trajectories found using a grid-search technique, and PEATSA is found to outperform the grid-search results for most launch years considered.

Computing energy-optimal trajectories for an autonomous underwater vehicle using direct shooting

Directory of Open Access Journals (Sweden)

Inge Spangelo

1992-07-01

Full Text Available Energy-optimal trajectories for an autonomous underwater vehicle can be computed using a numerical solution of the optimal control problem. The vehicle is modeled with the six dimensional nonlinear and coupled equations of motion, controlled with DC-motors in all degrees of freedom. The actuators are modeled and controlled with velocity loops. The dissipated energy is expressed in terms of the control variables as a nonquadratic function. Direct shooting methods, including control vector parameterization (CVP arc used in this study. Numerical calculations are performed and good results are achieved.

Hybrid vehicle energy management: singular optimal control

NARCIS (Netherlands)

Delprat, S.; Hofman, T.; Paganelli, S.

2017-01-01

Hybrid vehicle energymanagement is often studied in simulation as an optimal control problem. Under strict convexity assumptions, a solution can be developed using Pontryagin’s minimum principle. In practice, however, many engineers do not formally check these assumptions resulting in the possible

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.

A Novel Hybrid Firefly Algorithm for Global Optimization.

Directory of Open Access Journals (Sweden)

Lina Zhang

Full Text Available Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA, is proposed by combining the advantages of both the firefly algorithm (FA and differential evolution (DE. FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA, differential evolution (DE and particle swarm optimization (PSO in the sense of avoiding local minima and increasing the convergence rate.

Optimal control of cooperative multi-vehicle systems; Optimalsteuerung kooperierender Mehrfahrzeugsysteme

Energy Technology Data Exchange (ETDEWEB)

Reinl, Christian; Stryk, Oskar von [Technische Univ. Darmstadt (Germany). FB Informatik; Glocker, Markus [Trimble Terrasat GmbH, Hoehenkirchen (Germany)

2009-07-01

Nonlinear hybrid dynamical systems for modeling optimal cooperative control enable a tight and formal coupling of discrete and continuous state dynamics, i.e. of dynamic role and task assignment with switching dynamics of motions. In the resulting mixed-integer multi-phase optimal control problems constraints on the discrete and continuous state and control variables can be considered, e.g., formation or communication requirements. Two numerical methods are investigated: a decomposition approach using branch-and-bound and direct collocation methods as well as an approximation by large-scale, mixed-integer linear problems. Both methods are applied to example problems: the optimal simultaneous waypoint sequencing and trajectory planning of a team of aerial vehicles and the optimization of role distribution and trajectories in robot soccer. (orig.)

Assembly Line Productivity Assessment by Comparing Optimization-Simulation Algorithms of Trajectory Planning for Industrial Robots

Directory of Open Access Journals (Sweden)

Francisco Rubio

2015-01-01

Full Text Available In this paper an analysis of productivity will be carried out from the resolution of the problem of trajectory planning of industrial robots. The analysis entails economic considerations, thus overcoming some limitations of the existing literature. Two methodologies based on optimization-simulation procedures are compared to calculate the time needed to perform an industrial robot task. The simulation methodology relies on the use of robotics and automation software called GRASP. The optimization methodology developed in this work is based on the kinematics and the dynamics of industrial robots. It allows us to pose a multiobjective optimization problem to assess the trade-offs between the economic variables by means of the Pareto fronts. The comparison is carried out for different examples and from a multidisciplinary point of view, thus, to determine the impact of using each method. Results have shown the opportunity costs of non using the methodology with optimized time trajectories. Furthermore, it allows companies to stay competitive because of the quick adaptation to rapidly changing markets.

Optimal UAS Assignments and Trajectories for Persistent Surveillance and Data Collection from a Wireless Sensor Network

Science.gov (United States)

2015-12-24

Optimal UAS Assignments and Trajectories for Persistent Surveillance and Data Collection from a Wireless Sensor Network DISSERTATION Nidal M. Jodeh...ASSIGNMENTS AND TRAJECTORIES FOR PERSISTENT SURVEILLANCE AND DATA COLLECTION FROM A WIRELESS SENSOR NETWORK DISSERTATION Presented to the Faculty...COLLECTION FROM A WIRELESS SENSOR NETWORK Nidal M. Jodeh, B.S., M.A.S., M.S. Lieutenant Colonel, USAF Committee Membership: Richard G. Cobb, PhD Chairman

Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes

Directory of Open Access Journals (Sweden)

Jaya Shankar Tumuluru

2016-11-01

Full Text Available 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.

Optimal Energy Control Strategy Design for a Hybrid Electric Vehicle

Directory of Open Access Journals (Sweden)

Yuan Zou

2013-01-01

Full Text Available A heavy-duty parallel hybrid electric truck is modeled, and its optimal energy control is studied in this paper. The fundamental architecture of the parallel hybrid electric truck is modeled feed-forwardly, together with necessary dynamic features of subsystem or components. Dynamic programming (DP technique is adopted to find the optimal control strategy including the gear-shifting sequence and the power split between the engine and the motor subject to a battery SOC-sustaining constraint. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement on the fuel economy can be achieved in the heavy-duty vehicle cycle from the natural driving statistics.

High-performance simulation-based algorithms for an alpine ski racer’s trajectory optimization in heterogeneous computer systems

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

Space engineering modeling and optimization with case studies

CERN Document Server

Pintér, János

2016-01-01

This book presents a selection of advanced case studies that cover a substantial range of issues and real-world challenges and applications in space engineering. Vital mathematical modeling, optimization methodologies and numerical solution aspects of each application case study are presented in detail, with discussions of a range of advanced model development and solution techniques and tools. Space engineering challenges are discussed in the following contexts: •Advanced Space Vehicle Design •Computation of Optimal Low Thrust Transfers •Indirect Optimization of Spacecraft Trajectories •Resource-Constrained Scheduling, •Packing Problems in Space •Design of Complex Interplanetary Trajectories •Satellite Constellation Image Acquisition •Re-entry Test Vehicle Configuration Selection •Collision Risk Assessment on Perturbed Orbits •Optimal Robust Design of Hybrid Rocket Engines •Nonlinear Regression Analysis in Space Engineering< •Regression-Based Sensitivity Analysis and Robust Design ...

A new adaptive hybrid electromagnetic damper: modelling, optimization, and experiment

International Nuclear Information System (INIS)

Asadi, Ehsan; Ribeiro, Roberto; Behrad Khamesee, Mir; Khajepour, Amir

2015-01-01

This paper presents the development of a new electromagnetic hybrid damper which provides regenerative adaptive damping force for various applications. Recently, the introduction of electromagnetic technologies to the damping systems has provided researchers with new opportunities for the realization of adaptive semi-active damping systems with the added benefit of energy recovery. In this research, a hybrid electromagnetic damper is proposed. The hybrid damper is configured to operate with viscous and electromagnetic subsystems. The viscous medium provides a bias and fail-safe damping force while the electromagnetic component adds adaptability and the capacity for regeneration to the hybrid design. The electromagnetic component is modeled and analyzed using analytical (lumped equivalent magnetic circuit) and electromagnetic finite element method (FEM) (COMSOL ® software package) approaches. By implementing both modeling approaches, an optimization for the geometric aspects of the electromagnetic subsystem is obtained. Based on the proposed electromagnetic hybrid damping concept and the preliminary optimization solution, a prototype is designed and fabricated. A good agreement is observed between the experimental and FEM results for the magnetic field distribution and electromagnetic damping forces. These results validate the accuracy of the modeling approach and the preliminary optimization solution. An analytical model is also presented for viscous damping force, and is compared with experimental results The results show that the damper is able to produce damping coefficients of 1300 and 0–238 N s m −1 through the viscous and electromagnetic components, respectively. (paper)

Optimization of hybrid system (wind-solar energy) for pumping water ...

African Journals Online (AJOL)

This paper presents an optimization method for a hybrid (wind-solar) autonomous system designed for pumping water. This method is based on mathematical models demonstrated for the analysis and control of the performance of the various components of the hybrid system. These models provide an estimate of ...

Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

Science.gov (United States)

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.

Hybrid Differential Dynamic Programming with Stochastic Search

Science.gov (United States)

Aziz, Jonathan; Parker, Jeffrey; Englander, Jacob

2016-01-01

Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASAs Dawn mission. The Dawn trajectory was designed with the DDP-based Static Dynamic Optimal Control algorithm used in the Mystic software. Another recently developed method, Hybrid Differential Dynamic Programming (HDDP) is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.

Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm

International Nuclear Information System (INIS)

Wang, Jianzhou; Jiang, He; Wu, Yujie; Dong, Yao

2015-01-01

Due to energy crisis and environmental problems, it is very urgent to find alternative energy sources nowadays. Solar energy, as one of the great potential clean energies, has widely attracted the attention of researchers. In this paper, an optimized hybrid method by CS (Cuckoo Search) on the basis of the OP-ELM (Optimally Pruned Extreme Learning Machine), called CS-OP-ELM, is developed to forecast clear sky and real sky global horizontal radiation. First, MRSR (Multiresponse Sparse Regression) and LOO-CV (leave-one-out cross-validation) can be applied to rank neurons and prune the possibly meaningless neurons of the FFNN (Feed Forward Neural Network), respectively. Then, Direct strategy and Direct-Recursive strategy based on OP-ELM are introduced to build a hybrid model. Furthermore, CS (Cuckoo Search) optimized algorithm is employed to determine the proper weight coefficients. In order to verify the effectiveness of the developed method, hourly solar radiation data from six sites of the United States has been collected, and methods like ARMA (Autoregression moving average), BP (Back Propagation) neural network and OP-ELM can be compared with CS-OP-ELM. Experimental results show the optimized hybrid method CS-OP-ELM has the best forecasting performance. - Highlights: • An optimized hybrid method called CS-OP-ELM is proposed to forecast solar radiation. • CS-OP-ELM adopts multiple variables dataset as input variables. • Direct and Direct-Recursive strategy are introduced to build a hybrid model. • CS (Cuckoo Search) algorithm is used to determine the optimal weight coefficients. • The proposed method has the best performance compared with other methods

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.

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.

Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models.

Science.gov (United States)

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.

An adaptive multi-spline refinement algorithm in simulation based sailboat trajectory optimization using onboard multi-core computer systems

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.

Solar-Diesel Hybrid Power System Optimization and Experimental Validation

Science.gov (United States)

Jacobus, Headley Stewart

As of 2008 1.46 billion people, or 22 percent of the World's population, were without electricity. Many of these people live in remote areas where decentralized generation is the only method of electrification. Most mini-grids are powered by diesel generators, but new hybrid power systems are becoming a reliable method to incorporate renewable energy while also reducing total system cost. This thesis quantifies the measurable Operational Costs for an experimental hybrid power system in Sierra Leone. Two software programs, Hybrid2 and HOMER, are used during the system design and subsequent analysis. Experimental data from the installed system is used to validate the two programs and to quantify the savings created by each component within the hybrid system. This thesis bridges the gap between design optimization studies that frequently lack subsequent validation and experimental hybrid system performance studies.

Decentralized flight trajectory planning of multiple aircraft

OpenAIRE

Yokoyama, Nobuhiro; 横山　信宏

2008-01-01

Conventional decentralized algorithms for optimal trajectory planning tend to require prohibitive computational time as the number of aircraft increases. To overcome this drawback, this paper proposes a novel decentralized trajectory planning algorithm adopting a constraints decoupling approach for parallel optimization. The constraints decoupling approach is formulated as the path constraints of the real-time trajectory optimization problem based on nonlinear programming. Due to the parallel...

Swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement

Science.gov (United States)

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

Optimization on Trajectory of Stanford Manipulator based on Genetic Algorithm

Directory of Open Access Journals (Sweden)

Han Xi

2017-01-01

Full Text Available The optimization of robot manipulator’s trajectory has become a hot topic in academic and industrial fields. In this paper, a method for minimizing the moving distance of robot manipulators is presented. The Stanford Manipulator is used as the research object and the inverse kinematics model is established with Denavit-Hartenberg method. Base on the initial posture matrix, the inverse kinematics model is used to find the initial state of each joint. In accordance with the given beginning moment, cubic polynomial interpolation is applied to each joint variable and the positive kinematic model is used to calculate the moving distance of end effector. Genetic algorithm is used to optimize the sequential order of each joint and the time difference between different starting time of joints. Numerical applications involving a Stanford manipulator are presented.

Parameter optimization via cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power system

International Nuclear Information System (INIS)

Berrazouane, S.; Mohammedi, K.

2014-01-01

Highlights: • Optimized fuzzy logic controller (FLC) for operating a standalone hybrid power system based on cuckoo search algorithm. • Comparison between optimized fuzzy logic controller based on cuckoo search and swarm intelligent. • Loss of power supply probability and levelized energy cost are introduced. - Abstract: This paper presents the development of an optimized fuzzy logic controller (FLC) for operating a standalone hybrid power system based on cuckoo search algorithm. The FLC inputs are batteries state of charge (SOC) and net power flow, FLC outputs are the power rate of batteries, photovoltaic and diesel generator. Data for weekly solar irradiation, ambient temperature and load profile are used to tune the proposed controller by using cuckoo search algorithm. The optimized FLC is able to minimize loss of power supply probability (LPSP), excess energy (EE) and levelized energy cost (LEC). Moreover, the results of CS optimization are better than of particle swarm optimization PSO for fuzzy system controller

Hybrid chaotic ant swarm optimization

International Nuclear Information System (INIS)

Li Yuying; Wen Qiaoyan; Li Lixiang; Peng Haipeng

2009-01-01

Chaotic ant swarm optimization (CASO) is a powerful chaos search algorithm that is used to find the global optimum solution in search space. However, the CASO algorithm has some disadvantages, such as lower solution precision and longer computational time, when solving complex optimization problems. To resolve these problems, an improved CASO, called hybrid chaotic swarm optimization (HCASO), is proposed in this paper. The new algorithm introduces preselection operator and discrete recombination operator into the CASO; meanwhile it replaces the best position found by own and its neighbors' ants with the best position found by preselection operator and discrete recombination operator in evolution equation. Through testing five benchmark functions with large dimensionality, the experimental results show the new method enhances the solution accuracy and stability greatly, as well as reduces the computational time and computer memory significantly when compared to the CASO. In addition, we observe the results can become better with swarm size increasing from the sensitivity study to swarm size. And we gain some relations between problem dimensions and swam size according to scalability study.

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.

Mass Optimization of Battery/Supercapacitors Hybrid Systems Based on a Linear Programming Approach

Science.gov (United States)

Fleury, Benoit; Labbe, Julien

2014-08-01

The objective of this paper is to show that, on a specific launcher-type mission profile, a 40% gain of mass is expected using a battery/supercapacitors active hybridization instead of a single battery solution. This result is based on the use of a linear programming optimization approach to perform the mass optimization of the hybrid power supply solution.

Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts

Science.gov (United States)

2013-12-01

with 214 turbines [22]. In July 2011, the DoD declared that a complete study of 217 wind farm projects proposed in 35 states and Puerto Rico found...14. SUBJECT TERMS Hybrid electric grid , Microgrid , Hybrid renewable energy system , energy management center, optimization, Day...electric grid. In the case of a hybrid electric grid (HEG), or hybrid renewable energy system (HRES) where the microgrid can be connected to the commercial

Programs To Optimize Spacecraft And Aircraft Trajectories

Science.gov (United States)

Brauer, G. L.; Petersen, F. M.; Cornick, D.E.; Stevenson, R.; Olson, D. W.

1994-01-01

POST/6D POST is set of two computer programs providing ability to target and optimize trajectories of powered or unpowered spacecraft or aircraft operating at or near rotating planet. POST treats point-mass, three-degree-of-freedom case. 6D POST treats more-general rigid-body, six-degree-of-freedom (with point masses) case. Used to solve variety of performance, guidance, and flight-control problems for atmospheric and orbital vehicles. Applications include computation of performance or capability of vehicle in ascent, or orbit, and during entry into atmosphere, simulation and analysis of guidance and flight-control systems, dispersion-type analyses and analyses of loads, general-purpose six-degree-of-freedom simulation of controlled and uncontrolled vehicles, and validation of performance in six degrees of freedom. Written in FORTRAN 77 and C language. Two machine versions available: one for SUN-series computers running SunOS(TM) (LAR-14871) and one for Silicon Graphics IRIS computers running IRIX(TM) operating system (LAR-14869).

Optimizing the specificity of nucleic acid hybridization.

Science.gov (United States)

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.

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.

A methodology for optimal sizing of autonomous hybrid PV/wind system

International Nuclear Information System (INIS)

Diaf, S.; Diaf, D.; Belhamel, M.; Haddadi, M.; Louche, A.

2007-01-01

The present paper presents a methodology to perform the optimal sizing of an autonomous hybrid PV/wind system. The methodology aims at finding the configuration, among a set of systems components, which meets the desired system reliability requirements, with the lowest value of levelized cost of energy. Modelling a hybrid PV/wind system is considered as the first step in the optimal sizing procedure. In this paper, more accurate mathematical models for characterizing PV module, wind generator and battery are proposed. The second step consists to optimize the sizing of a system according to the loss of power supply probability (LPSP) and the levelized cost of energy (LCE) concepts. Considering various types and capacities of system devices, the configurations, which can meet the desired system reliability, are obtained by changing the type and size of the devices systems. The configuration with the lowest LCE gives the optimal choice. Applying this method to an assumed PV/wind hybrid system to be installed at Corsica Island, the simulation results show that the optimal configuration, which meet the desired system reliability requirements (LPSP=0) with the lowest LCE, is obtained for a system comprising a 125 W photovoltaic module, one wind generator (600 W) and storage batteries (using 253 Ah). On the other hand, the device system choice plays an important role in cost reduction as well as in energy production

Study on hybrid multi-objective optimization algorithm for inverse treatment planning of radiation therapy

International Nuclear Information System (INIS)

Li Guoli; Song Gang; Wu Yican

2007-01-01

Inverse treatment planning for radiation therapy is a multi-objective optimization process. The hybrid multi-objective optimization algorithm is studied by combining the simulated annealing(SA) and genetic algorithm(GA). Test functions are used to analyze the efficiency of algorithms. The hybrid multi-objective optimization SA algorithm, which displacement is based on the evolutionary strategy of GA: crossover and mutation, is implemented in inverse planning of external beam radiation therapy by using two kinds of objective functions, namely the average dose distribution based and the hybrid dose-volume constraints based objective functions. The test calculations demonstrate that excellent converge speed can be achieved. (authors)

PRESS: A Novel Framework of Trajectory Compression in Road Networks

OpenAIRE

Song, Renchu; Sun, Weiwei; Zheng, Baihua; Zheng, Yu

2014-01-01

Location data becomes more and more important. In this paper, we focus on the trajectory data, and propose a new framework, namely PRESS (Paralleled Road-Network-Based Trajectory Compression), to effectively compress trajectory data under road network constraints. Different from existing work, PRESS proposes a novel representation for trajectories to separate the spatial representation of a trajectory from the temporal representation, and proposes a Hybrid Spatial Compression (HSC) algorithm ...

Optimal sizing study of hybrid wind/PV/diesel power generation unit

Energy Technology Data Exchange (ETDEWEB)

Belfkira, Rachid; Zhang, Lu; Barakat, Georges [Groupe de Recherche en Electrotechnique et Automatique du Havre, University of Le Havre, 25 rue Philippe Lebon, BP 1123, 76063 Le Havre (France)

2011-01-15

In this paper, a methodology of sizing optimization of a stand-alone hybrid wind/PV/diesel energy system is presented. This approach makes use of a deterministic algorithm to suggest, among a list of commercially available system devices, the optimal number and type of units ensuring that the total cost of the system is minimized while guaranteeing the availability of the energy. The collection of 6 months of data of wind speed, solar radiation and ambient temperature recorded for every hour of the day were used. The mathematical modeling of the main elements of the hybrid wind/PV/diesel system is exposed showing the more relevant sizing variables. A deterministic algorithm is used to minimize the total cost of the system while guaranteeing the satisfaction of the load demand. A comparison between the total cost of the hybrid wind/PV/diesel energy system with batteries and the hybrid wind/PV/diesel energy system without batteries is presented. The reached results demonstrate the practical utility of the used sizing methodology and show the influence of the battery storage on the total cost of the hybrid system. (author)

Trajectory optimization for A S.S.T.O. using in-flight LOX collection

Science.gov (United States)

Saint-Mard, M.; Hendrick, P.

A key point for a space mission (launch of a satellite, earth observation,…) is the optimization of the vehicle trajectory in order to burn the smallest quantity of propelant and then maximize the payload. This is true for evay space vehicle, but especially it is a crucial point for a Single-Stage-To-Orbit (SSTO) where the choice of a bad trajectory can result in an unrealizable vehicle due to the large airbreathing part of the flight In this study, we discuss the trajectory optimization for a Vertical Take-Off and Horizontal Landing (VTOHL) SSTO using supersonic in-flight atmospheric oxygen collection during a cruise phase (constant speed & constant altitude). This collected oxygen is stored in the LOX tanks and reused in the final rocket phase. This SSTO bas a Blended Body aerodynamic configuration as the one chosen by Lockheed Martin for its new space launcher (VentureStar and X-33). This SSTO uses rocket engines from take-off to Mach 1.7 and also for the exoatmospheric flight phase (that means for an altitude higher than 30km and a Mach number evolution from 6.8 to about 20). Between these two rocket phases, the SSTO is propelled by a subsonic ramjet. To perform this study, we use 2 computer programs (running on a home Computer): the first one allows to estimate the SSTO performances (TOGW, dry weight, hydrogen and oxygen consumptions) for a fixed payload mass and the second one permits the evaluation of the payload mass for a fixed TOGW.

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.

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...

A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

Science.gov (United States)

Tong, Cao; Gong, Haili

2018-03-01

This paper aims to reduce the computational cost of reliability analysis. A new hybrid algorithm is proposed based on PSO-optimized Kriging model and adaptive importance sampling method. Firstly, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of Kriging model. A typical function is fitted to validate improvement by comparing results of PSO-optimized Kriging model with those of the original Kriging model. Secondly, a hybrid algorithm for reliability analysis combined optimized Kriging model and adaptive importance sampling is proposed. Two cases from literatures are given to validate the efficiency and correctness. The proposed method is proved to be more efficient due to its application of small number of sample points according to comparison results.

Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles

NARCIS (Netherlands)

Silvas, E.; Hofman, T.; Murgovski, N.; Etman, L.F.P.; Steinbuch, M.

2017-01-01

The optimal design of a hybrid electric vehicle (HEV) can be formulated as a multiobjective optimization problem that spreads over multiple levels (technology, topology, size, and control). In the last decade, studies have shown that by integrating these optimization levels, fuel benefits are

Parametric Approach to Trajectory Tracking Control of Robot Manipulators

Directory of Open Access Journals (Sweden)

Shijie Zhang

2013-01-01

Full Text Available The mathematic description of the trajectory of robot manipulators with the optimal trajectory tracking problem is formulated as an optimal control problem, and a parametric approach is proposed for the optimal trajectory tracking control problem. The optimal control problem is first solved as an open loop optimal control problem by using a time scaling transform and the control parameterization method. Then, by virtue of the relationship between the optimal open loop control and the optimal closed loop control along the optimal trajectory, a practical method is presented to calculate an approximate optimal feedback gain matrix, without having to solve an optimal control problem involving the complex Riccati-like matrix differential equation coupled with the original system dynamics. Simulation results of 2-link robot manipulator are presented to show the effectiveness of the proposed method.

An integrated optimization approach for a hybrid energy system in electric vehicles

International Nuclear Information System (INIS)

Hung, Yi-Hsuan; Wu, Chien-Hsun

2012-01-01

Highlights: ► Second-order control-oriented dynamics for a battery/supercapacitor EV is modeled. ► Multiple for-loop programming and global searchwith constraints are main design principles of integrated optimization algorithm (IOA). ► Optimal hybridization is derived based on maximizing energy storage capacity. ► Optimal energy management in three EV operation modes is searched based on minimizing total consumed power. ► Simulation results prove that 6+% of total energy is saved by the IOA method. -- Abstract: This paper develops a simple but innovative integrated optimization approach (IOA) for deriving the best solutions of component sizing and control strategies of a hybrid energy system (HES) which consists of a lithium battery and a supercapacitor module. To implement IOA, a multiple for-loop structure with a preset cost function is needed to globally calculate the best hybridization and energy management of the HES. For system hybridization, the optimal size ratio is evaluated by maximizing the HES energy stored capacity at various costs. For energy management, the optimal power distribution combined with a three-mode rule-based strategy is searched to minimize the total consumed energy. Combining above two for-loop structures and giving a time-dependent test scenario, the IOA is derived by minimizing the accumulated HES power. Simulation results show that 6% of the total HES energy can be saved in the IOA case compared with the original system in two driving cycles: ECE and UDDS, and two vehicle weights, respectively. It proves that the IOA effectively derives the maximum energy storage capacity and the minimum energy consumption of the HES at the same time. Experimental verification will be carried out in the near future.

Review of optimization strategies for system-level design in hybrid electric vehicles

NARCIS (Netherlands)

Silvas, E.; Hofman, T.; Murgovski, N.; Etman, P.; Steinbuch, M.

2017-01-01

The optimal design of a hybrid electric vehicle can be formulated as a multi-objective optimization problem that spreads over multiple levels (technology, topology, size and control). In the last decade, studies have shown that, by integrating these optimization levels fuel benefits are obtained,

Microwave imaging for conducting scatterers by hybrid particle swarm optimization with simulated annealing

International Nuclear Information System (INIS)

Mhamdi, B.; Grayaa, K.; Aguili, T.

2011-01-01

In this paper, a microwave imaging technique for reconstructing the shape of two-dimensional perfectly conducting scatterers by means of a stochastic optimization approach is investigated. Based on the boundary condition and the measured scattered field derived by transverse magnetic illuminations, a set of nonlinear integral equations is obtained and the imaging problem is reformulated in to an optimization problem. A hybrid approximation algorithm, called PSO-SA, is developed in this work to solve the scattering inverse problem. In the hybrid algorithm, particle swarm optimization (PSO) combines global search and local search for finding the optimal results assignment with reasonable time and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of SA. Reconstruction results are compared with exact shapes of some conducting cylinders; and good agreements with the original shapes are observed.

Dynamic trajectory-based couch motion for improvement of radiation therapy trajectories in cranial SRT

Energy Technology Data Exchange (ETDEWEB)

MacDonald, R. Lee [Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia B3H 4R2 (Canada); Thomas, Christopher G., E-mail: Chris.Thomas@cdha.nshealth.ca [Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia B3H 4R2 (Canada); Department of Medical Physics, Nova Scotia Cancer Centre, Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia B3H 1V7 (Canada); Department of Radiation Oncology, Dalhousie University, Halifax, Nova Scotia B3H 4R2 (Canada); Department of Radiology, Dalhousie University, Halifax, Nova Scotia B3H 4R2 (Canada)

2015-05-15

Purpose: To investigate potential improvement in external beam stereotactic radiation therapy plan quality for cranial cases using an optimized dynamic gantry and patient support couch motion trajectory, which could minimize exposure to sensitive healthy tissue. Methods: Anonymized patient anatomy and treatment plans of cranial cancer patients were used to quantify the geometric overlap between planning target volumes and organs-at-risk (OARs) based on their two-dimensional projection from source to a plane at isocenter as a function of gantry and couch angle. Published dose constraints were then used as weighting factors for the OARs to generate a map of couch-gantry coordinate space, indicating degree of overlap at each point in space. A couch-gantry collision space was generated by direct measurement on a linear accelerator and couch using an anthropomorphic solid-water phantom. A dynamic, fully customizable algorithm was written to generate a navigable ideal trajectory for the patient specific couch-gantry space. The advanced algorithm can be used to balance the implementation of absolute minimum values of overlap with the clinical practicality of large-scale couch motion and delivery time. Optimized cranial cancer treatment trajectories were compared to conventional treatment trajectories. Results: Comparison of optimized treatment trajectories with conventional treatment trajectories indicated an average decrease in mean dose to the OARs of 19% and an average decrease in maximum dose to the OARs of 12%. Degradation was seen for homogeneity index (6.14% ± 0.67%–5.48% ± 0.76%) and conformation number (0.82 ± 0.02–0.79 ± 0.02), but neither was statistically significant. Removal of OAR constraints from volumetric modulated arc therapy optimization reveals that reduction in dose to OARs is almost exclusively due to the optimized trajectory and not the OAR constraints. Conclusions: The authors’ study indicated that simultaneous couch and gantry motion

Dynamic trajectory-based couch motion for improvement of radiation therapy trajectories in cranial SRT

International Nuclear Information System (INIS)

MacDonald, R. Lee; Thomas, Christopher G.

2015-01-01

Purpose: To investigate potential improvement in external beam stereotactic radiation therapy plan quality for cranial cases using an optimized dynamic gantry and patient support couch motion trajectory, which could minimize exposure to sensitive healthy tissue. Methods: Anonymized patient anatomy and treatment plans of cranial cancer patients were used to quantify the geometric overlap between planning target volumes and organs-at-risk (OARs) based on their two-dimensional projection from source to a plane at isocenter as a function of gantry and couch angle. Published dose constraints were then used as weighting factors for the OARs to generate a map of couch-gantry coordinate space, indicating degree of overlap at each point in space. A couch-gantry collision space was generated by direct measurement on a linear accelerator and couch using an anthropomorphic solid-water phantom. A dynamic, fully customizable algorithm was written to generate a navigable ideal trajectory for the patient specific couch-gantry space. The advanced algorithm can be used to balance the implementation of absolute minimum values of overlap with the clinical practicality of large-scale couch motion and delivery time. Optimized cranial cancer treatment trajectories were compared to conventional treatment trajectories. Results: Comparison of optimized treatment trajectories with conventional treatment trajectories indicated an average decrease in mean dose to the OARs of 19% and an average decrease in maximum dose to the OARs of 12%. Degradation was seen for homogeneity index (6.14% ± 0.67%–5.48% ± 0.76%) and conformation number (0.82 ± 0.02–0.79 ± 0.02), but neither was statistically significant. Removal of OAR constraints from volumetric modulated arc therapy optimization reveals that reduction in dose to OARs is almost exclusively due to the optimized trajectory and not the OAR constraints. Conclusions: The authors’ study indicated that simultaneous couch and gantry motion

Optimal design of damping layers in SMA/GFRP laminated hybrid composites

Science.gov (United States)

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.

Conflict Resolution for Wind-Optimal Aircraft Trajectories in North Atlantic Oceanic Airspace with Wind Uncertainties

Science.gov (United States)

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.

Design Optimization of a Hybrid Electric Vehicle Powertrain

Science.gov (United States)

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.

Solving Optimal Control Problem of Monodomain Model Using Hybrid Conjugate Gradient Methods

Directory of Open Access Journals (Sweden)

Kin Wei Ng

2012-01-01

Full Text Available We present the numerical solutions for the PDE-constrained optimization problem arising in cardiac electrophysiology, that is, the optimal control problem of monodomain model. The optimal control problem of monodomain model is a nonlinear optimization problem that is constrained by the monodomain model. The monodomain model consists of a parabolic partial differential equation coupled to a system of nonlinear ordinary differential equations, which has been widely used for simulating cardiac electrical activity. Our control objective is to dampen the excitation wavefront using optimal applied extracellular current. Two hybrid conjugate gradient methods are employed for computing the optimal applied extracellular current, namely, the Hestenes-Stiefel-Dai-Yuan (HS-DY method and the Liu-Storey-Conjugate-Descent (LS-CD method. Our experiment results show that the excitation wavefronts are successfully dampened out when these methods are used. Our experiment results also show that the hybrid conjugate gradient methods are superior to the classical conjugate gradient methods when Armijo line search is used.

Optimal Sizing and Control Strategy Design for Heavy Hybrid Electric Truck

Directory of Open Access Journals (Sweden)

Yuan Zou

2012-01-01

Full Text Available Due to the complexity of the hybrid powertrain, the control is highly involved to improve the collaborations of the different components. For the specific powertrain, the components' sizing just gives the possibility to propel the vehicle and the control will realize the function of the propulsion. Definitely the components' sizing also gives the constraints to the control design, which cause a close coupling between the sizing and control strategy design. This paper presents a parametric study focused on sizing of the powertrain components and optimization of the power split between the engine and electric motor for minimizing the fuel consumption. A framework is put forward to accomplish the optimal sizing and control design for a heavy parallel pre-AMT hybrid truck under the natural driving schedule. The iterative plant-controller combined optimization methodology is adopted to optimize the key parameters of the plant and control strategy simultaneously. A scalable powertrain model based on a bilevel optimization framework is built. Dynamic programming is applied to find the optimal control in the inner loop with a prescribed cycle. The parameters are optimized in the outer loop. The results are analysed and the optimal sizing and control strategy are achieved simultaneously.

A hybrid algorithm for instant optimization of beam weights in anatomy-based intensity modulated radiotherapy: a performance evaluation study

International Nuclear Information System (INIS)

Vaitheeswaran, Ranganathan; Sathiya Narayanan, V.K.; Bhangle, Janhavi R.; Nirhali, Amit; Kumar, Namita; Basu, Sumit; Maiya, Vikram

2011-01-01

The study aims to introduce a hybrid optimization algorithm for anatomy-based intensity modulated radiotherapy (AB-IMRT). Our proposal is that by integrating an exact optimization algorithm with a heuristic optimization algorithm, the advantages of both the algorithms can be combined, which will lead to an efficient global optimizer solving the problem at a very fast rate. Our hybrid approach combines Gaussian elimination algorithm (exact optimizer) with fast simulated annealing algorithm (a heuristic global optimizer) for the optimization of beam weights in AB-IMRT. The algorithm has been implemented using MATLAB software. The optimization efficiency of the hybrid algorithm is clarified by (i) analysis of the numerical characteristics of the algorithm and (ii) analysis of the clinical capabilities of the algorithm. The numerical and clinical characteristics of the hybrid algorithm are compared with Gaussian elimination method (GEM) and fast simulated annealing (FSA). The numerical characteristics include convergence, consistency, number of iterations and overall optimization speed, which were analyzed for the respective cases of 8 patients. The clinical capabilities of the hybrid algorithm are demonstrated in cases of (a) prostate and (b) brain. The analyses reveal that (i) the convergence speed of the hybrid algorithm is approximately three times higher than that of FSA algorithm (ii) the convergence (percentage reduction in the cost function) in hybrid algorithm is about 20% improved as compared to that in GEM algorithm (iii) the hybrid algorithm is capable of producing relatively better treatment plans in terms of Conformity Index (CI) (∼ 2% - 5% improvement) and Homogeneity Index (HI) (∼ 4% - 10% improvement) as compared to GEM and FSA algorithms (iv) the sparing of organs at risk in hybrid algorithm-based plans is better than that in GEM-based plans and comparable to that in FSA-based plans; and (v) the beam weights resulting from the hybrid algorithm are

A simple model based magnet sorting algorithm for planar hybrid undulators

International Nuclear Information System (INIS)

Rakowsky, G.

2010-01-01

Various magnet sorting strategies have been used to optimize undulator performance, ranging from intuitive pairing of high- and low-strength magnets, to full 3D FEM simulation with 3-axis Helmholtz coil magnet data. In the extreme, swapping magnets in a full field model to minimize trajectory wander and rms phase error can be time consuming. This paper presents a simpler approach, extending the field error signature concept to obtain trajectory displacement, kick angle and phase error signatures for each component of magnetization error from a Radia model of a short hybrid-PM undulator. We demonstrate that steering errors and phase errors are essentially decoupled and scalable from measured X, Y and Z components of magnetization. Then, for any given sequence of magnets, rms trajectory and phase errors are obtained from simple cumulative sums of the scaled displacements and phase errors. The cost function (a weighted sum of these errors) is then minimized by swapping magnets, using one's favorite optimization algorithm. This approach was applied recently at NSLS to a short in-vacuum undulator, which required no subsequent trajectory or phase shimming. Trajectory and phase signatures are also obtained for some mechanical errors, to guide 'virtual shimming' and specifying mechanical tolerances. Some simple inhomogeneities are modeled to assess their error contributions.

A Time-Regularized, Multiple Gravity-Assist Low-Thrust, Bounded-Impulse Model for Trajectory Optimization

Science.gov (United States)

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.

Predictive control strategies for energy saving of hybrid electric vehicles based on traffic light information

Directory of Open Access Journals (Sweden)

Kaijiang YU

2015-10-01

Full Text Available As the conventional control method for hybrid electric vehicle doesn’t consider the effect of known traffic light information on the vehicle energy management, this paper proposes a model predictive control intelligent optimization strategies based on traffic light information for hybrid electric vehicles. By building the simplified model of the hybrid electric vehicle and adopting the continuation/generalized minimum residual method, the model prediction problem is solved. The simulation is conducted by using MATLAB/Simulink platform. The simulation results show the effectiveness of the proposed model of the traffic light information, and that the proposed model predictive control method can improve fuel economy and the real-time control performance significantly. The research conclusions show that the proposed control strategy can achieve optimal control of the vehicle trajectory, significantly improving fuel economy of the vehicle, and meet the system requirements for the real-time optimal control.

An hybrid methodology for RL-based behavior coordination in a target following mission with an AUV

OpenAIRE

Carreras Pérez, Marc; Yuh, Junku; Batlle i Grabulosa, Joan

2001-01-01

Proposes a behavior-based scheme for high-level control of autonomous underwater vehicles (AUVs). Two main characteristics can be highlighted in the control scheme. Behavior coordination is done through a hybrid methodology, which takes in advantages of the robustness and modularity in competitive approaches, as well as optimized trajectories

Test Beam Results of Geometry Optimized Hybrid Pixel Detectors

CERN Document Server

Becks, K H; Grah, C; Mättig, P; Rohe, T

2006-01-01

The Multi-Chip-Module-Deposited (MCM-D) technique has been used to build hybrid pixel detector assemblies. This paper summarises the results of an analysis of data obtained in a test beam campaign at CERN. Here, single chip hybrids made of ATLAS pixel prototype read-out electronics and special sensor tiles were used. They were prepared by the Fraunhofer Institut fuer Zuverlaessigkeit und Mikrointegration, IZM, Berlin, Germany. The sensors feature an optimized sensor geometry called equal sized bricked. This design enhances the spatial resolution for double hits in the long direction of the sensor cells.

A Piecewise Acceleration-Optimal and Smooth-Jerk Trajectory Planning Method for Robot Manipulator along a Predefined Path

Directory of Open Access Journals (Sweden)

Yuan Chen

2011-09-01

Full Text Available This paper proposes a piecewise acceleration-optimal and smooth-jerk trajectory planning method of robot manipulator. The optimal objective function is given by the weighted sum of two terms having opposite effects: the maximal acceleration and the minimal jerk. Some computing techniques are proposed to determine the optimal solution. These techniques take both the time intervals between two interpolation points and the control points of B-spline function as optimal variables, redefine the kinematic constraints as the constraints of optimal variables, and reformulate the objective function in matrix form. The feasibility of the optimal method is illustrated by simulation and experimental results with pan mechanism for cooking robot.

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

Science.gov (United States)

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

Directory of Open Access Journals (Sweden)

Jun-qing Li

2014-01-01

Full Text Available A hybrid algorithm which combines particle swarm optimization (PSO and iterated local search (ILS is proposed for solving the hybrid flowshop scheduling (HFS problem with preventive maintenance (PM activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron’s benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.

Hybrid particle swarm optimization for hybrid flowshop scheduling problem with maintenance activities.

Science.gov (United States)

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.

Hybrid vehicle optimal control : Linear interpolation and singular control

NARCIS (Netherlands)

Delprat, S.; Hofman, T.

2015-01-01

Hybrid vehicle energy management can be formulated as an optimal control problem. Considering that the fuel consumption is often computed using linear interpolation over lookup table data, a rigorous analysis of the necessary conditions provided by the Pontryagin Minimum Principle is conducted. For

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.

A Hybrid Metaheuristic-Based Approach for the Aerodynamic Optimization of Small Hybrid Wind Turbine Rotors

DEFF Research Database (Denmark)

Herbert-Acero, José F.; Martínez-Lauranchet, Jaime; Probst, Oliver

2014-01-01

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...

On the Likely Utility of Hybrid Weights Optimized for Variances in Hybrid Error Covariance Models

Science.gov (United States)

Satterfield, E.; Hodyss, D.; Kuhl, D.; Bishop, C. H.

2017-12-01

Because of imperfections in ensemble data assimilation schemes, one cannot assume that the ensemble covariance is equal to the true error covariance of a forecast. Previous work demonstrated how information about the distribution of true error variances given an ensemble sample variance can be revealed from an archive of (observation-minus-forecast, ensemble-variance) data pairs. Here, we derive a simple and intuitively compelling formula to obtain the mean of this distribution of true error variances given an ensemble sample variance from (observation-minus-forecast, ensemble-variance) data pairs produced by a single run of a data assimilation system. This formula takes the form of a Hybrid weighted average of the climatological forecast error variance and the ensemble sample variance. Here, we test the extent to which these readily obtainable weights can be used to rapidly optimize the covariance weights used in Hybrid data assimilation systems that employ weighted averages of static covariance models and flow-dependent ensemble based covariance models. Univariate data assimilation and multi-variate cycling ensemble data assimilation are considered. In both cases, it is found that our computationally efficient formula gives Hybrid weights that closely approximate the optimal weights found through the simple but computationally expensive process of testing every plausible combination of weights.

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.

An Effective Hybrid Firefly Algorithm with Harmony Search for Global Numerical Optimization

Directory of Open Access Journals (Sweden)

Lihong Guo

2013-01-01

Full Text Available A hybrid metaheuristic approach by hybridizing harmony search (HS and firefly algorithm (FA, namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods.

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.

Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach

International Nuclear Information System (INIS)

Meo, Santolo; Zohoori, Alireza; Vahedi, Abolfazl

2016-01-01

Highlights: • A new optimal design of flux switching permanent magnet generator is developed. • A prototype is employed to validate numerical data used for optimization. • A novel hybrid multi-objective particle swarm optimization approach is proposed. • Optimization targets are weight, cost, voltage and its total harmonic distortion. • The hybrid approach preference is proved compared with other optimization methods. - Abstract: In this paper a new hybrid approach obtained combining a multi-objective particle swarm optimization and artificial neural network is proposed for the design optimization of a direct-drive permanent magnet flux switching generators for low power wind applications. The targets of the proposed multi-objective optimization are to reduce the costs and weight of the machine while maximizing the amplitude of the induced voltage as well as minimizing its total harmonic distortion. The permanent magnet width, the stator and rotor tooth width, the rotor teeth number and stator pole number of the machine define the search space for the optimization problem. Four supervised artificial neural networks are designed for modeling the complex relationships among the weight, the cost, the amplitude and the total harmonic distortion of the output voltage respect to the quantities of the search space. Finite element analysis is adopted to generate training dataset for the artificial neural networks. Finite element analysis based model is verified by experimental results with a 1.5 kW permanent magnet flux switching generator prototype suitable for renewable energy applications, having 6/19 stator poles/rotor teeth. Finally the effectiveness of the proposed hybrid procedure is compared with the results given by conventional multi-objective optimization algorithms. The obtained results show the soundness of the proposed multi objective optimization technique and its feasibility to be adopted as suitable methodology for optimal design of permanent

Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm

DEFF Research Database (Denmark)

Awasthi, Abhishek; Venkitusamy, Karthikeyan; Padmanaban, Sanjeevikumar

2017-01-01

India's ever increasing population has made it necessary to develop alternative modes of transportation with electric vehicles being the most preferred option. The major obstacle is the deteriorating impact on the utility distribution system brought about by improper setup of these charging...... stations. This paper deals with the optimal planning (siting and sizing) of charging station infrastructure in the city of Allahabad, India. This city is one of the upcoming smart cities, where electric vehicle transportation pilot project is going on under Government of India initiative. In this context......, 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...

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.

Optimal scheduling for distributed hybrid system with pumped hydro storage

International Nuclear Information System (INIS)

Kusakana, Kanzumba

2016-01-01

Highlights: • Pumped hydro storage is proposed for isolated hybrid PV–Wind–Diesel systems. • Optimal control is developed to dispatch power flow economically. • A case study is conducted using the model for an isolated load. • Effects of seasons on the system’s optimal scheduling are examined through simulation. - Abstract: Photovoltaic and wind power generations are currently seen as sustainable options of in rural electrification, particularly in standalone applications. However the variable character of solar and wind resources as well as the variable load demand prevent these generation systems from being totally reliable without suitable energy storage system. Several research works have been conducted on the use of photovoltaic and wind systems in rural electrification; however most of these works have not considered other ways of storing energy except for conventional battery storage systems. In this paper, an energy dispatch model that satisfies the load demand, taking into account the intermittent nature of the solar and wind energy sources and variations in demand, is presented for a hybrid system consisting of a photovoltaic unit, a wind unit, a pumped hydro storage system and a diesel generator. The main purpose of the developed model is to minimize the hybrid system’s operation cost while optimizing the system’s power flow considering the different component’s operational constraints. The simulations have been performed using “fmincon” implemented in Matlab. The model have been applied to two test examples; the simulation results are analyzed and compared to the case where the diesel generator is used alone to supply the given load demand. The results show that using the developed control model for the proposed hybrid system, fuel saving can be achieved compared to the case where the diesel is used alone to supply the same load patters.

Component sizing optimization of plug-in hybrid electric vehicles

International Nuclear Information System (INIS)

Wu, Xiaolan; Cao, Binggang; Li, Xueyan; Xu, Jun; Ren, Xiaolong

2011-01-01

Plug-in hybrid electric vehicles (PHEVs) are considered as one of the most promising means to improve the near-term sustainability of the transportation and stationary energy sectors. This paper describes a methodology for the optimization of PHEVs component sizing using parallel chaos optimization algorithm (PCOA). In this approach, the objective function is defined so as to minimize the drivetrain cost. In addition, the driving performance requirements are considered as constraints. Finally, the optimization process is performed over three different all electric range (AER) and two types of batteries. The results from computer simulation show the effectiveness of the approach and the reduction in drivetrian cost while ensuring the vehicle performance.

Optimization of an experimental hybrid microgrid operation: reliability and economic issues

OpenAIRE

Milo, Aitor; Gaztañaga, Haizea; Etxeberria Otadui, Ion; Bilbao, Endika; Rodríguez Cortés, Pedro

2009-01-01

In this paper a hybrid microgrid system, composed of RES (Renewable Energy System) and CHP (Combined Heat and Power) systems together with a battery based storage system is presented. The microgrid is accompanied by a centralized energy management system (CEMS) in order to optimize the microgrid operation both in grid-connected and in stand-alone modes. In grid-connected mode the optimization of the economic exploitation of the microgrid is privileged by applying optim...

Optimal design of energy storage systems for hybrid vehicle drivetrains

NARCIS (Netherlands)

Hofman, T.; Hoekstra, D.; Druten, van R.M.; Steinbuch, M.

2005-01-01

Current hybrid powertrain simulation packages arebased on discrete (existing) system components and predefinedsystem structures. Optimization of the performance of the hybridpowertrain is then based on finding the most efficient controlstrategy of the primary and secondary power source and

Developing traction control strategy for a plug-in hybrid electric vehicle using innovative optimization based approaches

Energy Technology Data Exchange (ETDEWEB)

Zhou, L.; Gu, J.; Dong, Z. [Victoria Univ., BC (Canada). Dept. of Mechanical Engineering

2010-07-01

This paper described a traction control system designed for hybrid vehicles with multiple power plants and drive axles. Model-based design tools were used to develop the traction control system and plug-in hybrid vehicle models. Optimization studies were conducted in a finite number of operating states in order to maximize the electrical and mechanical energy conversion efficiency of an extended range electric vehicle. Four global optimization algorithms were then evaluated in relation to their CPU times. The studied algorithms included a genetic algorithm (GA), a particle swarm optimization (PSO) algorithm, a hybrid adaptive metamodel optimization (HAM) and space elimination and unimodal region reduction (SEUMRE) algorithm. A comparative evaluation of the algorithms demonstrated that the PSO algorithm obtained optimal results, while the HAM algorithm used significantly less computational time. Results of the optimization studies were then implemented in a controller model. Results of the study showed that the energy efficiency of the vehicle improved using the developed controller model. 4 refs., 2 tabs., 8 figs.

Study on optimal configuration of the grid-connected wind-solar-battery hybrid power system

Science.gov (United States)

Ma, Gang; Xu, Guchao; Ju, Rong; Wu, Tiantian

2017-08-01

The capacity allocation of each energy unit in the grid-connected wind-solar-battery hybrid power system is a significant segment in system design. In this paper, taking power grid dispatching into account, the research priorities are as follows: (1) We establish the mathematic models of each energy unit in the hybrid power system. (2) Based on dispatching of the power grid, energy surplus rate, system energy volatility and total cost, we establish the evaluation system for the wind-solar-battery power system and use a number of different devices as the constraint condition. (3) Based on an improved Genetic algorithm, we put forward a multi-objective optimisation algorithm to solve the optimal configuration problem in the hybrid power system, so we can achieve the high efficiency and economy of the grid-connected hybrid power system. The simulation result shows that the grid-connected wind-solar-battery hybrid power system has a higher comprehensive performance; the method of optimal configuration in this paper is useful and reasonable.

Hybrid particle swarm optimization algorithm and its application in nuclear engineering

International Nuclear Information System (INIS)

Liu, C.Y.; Yan, C.Q.; Wang, J.J.

2014-01-01

Highlights: • We propose a hybrid particle swarm optimization algorithm (HPSO). • Modified Nelder–Mead simplex search method is applied in HPSO. • The algorithm has a high search precision and rapidly calculation speed. • HPSO can be used in the nuclear engineering optimization design problems. - Abstract: A hybrid particle swarm optimization algorithm with a feasibility-based rule for solving constrained optimization problems has been developed in this research. Firstly, the global optimal solution zone can be obtained through particle swarm optimization process, and then the refined search of the global optimal solution will be achieved through the modified Nelder–Mead simplex algorithm. Simulations based on two well-studied benchmark problems demonstrate the proposed algorithm will be an efficient alternative to solving constrained optimization problems. The vertical electrical heating pressurizer is one of the key components in reactor coolant system. The mathematical model of pressurizer has been established in steady state. The optimization design of pressurizer weight has been carried out through HPSO algorithm. The results show the pressurizer weight can be reduced by 16.92%. The thermal efficiencies of conventional PWR nuclear power plants are about 31–35% so far, which are much lower than fossil fueled plants based in a steam cycle as PWR. The thermal equilibrium mathematic model for nuclear power plant secondary loop has been established. An optimization case study has been conducted to improve the efficiency of the nuclear power plant with the proposed algorithm. The results show the thermal efficiency is improved by 0.5%

Non-binary Hybrid LDPC Codes: Structure, Decoding and Optimization

OpenAIRE

Sassatelli, Lucile; Declercq, David

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-bin...

Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.

Science.gov (United States)

Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui

2006-01-01

This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.

Firing Control Optimization of Impulse Thrusters for Trajectory Correction Projectiles

Directory of Open Access Journals (Sweden)

Min Gao

2015-01-01

Full Text Available This paper presents an optimum control scheme of firing time and firing phase angle by taking impact point deviation as optimum objective function which takes account of the difference of longitudinal and horizontal correction efficiency, firing delay, roll rate, flight stability, and so forth. Simulations indicate that this control scheme can assure lateral impulse thrusters are activated at time and phase angle when the correction efficiency is higher. Further simulations show that the impact point dispersion is mainly influenced by the total impulse deployed, and the impulse, number, and firing interval need to be optimized to reduce the impact point dispersion of rockets. Live firing experiments with two trajectory correction rockets indicate that the firing control scheme works effectively.

Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System

Directory of Open Access Journals (Sweden)

Farouk Odeim

2015-06-01

Full Text Available 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, with its slow dynamics, controls the state-of-charge of the battery. The parameters of the power management strategy are optimized by a genetic algorithm and Pareto front analysis in a framework of multi-objective optimization, taking into account the hydrogen consumption, the battery loading and the acceleration performance. The optimization results are validated on a test bench composed of a fuel cell system (1.2 kW, 26 V, lithium polymer battery (30 Ah, 37 V, and a supercapacitor (167 F, 48 V.

Trajectories of quality of life among Chinese patients diagnosed with nasopharynegeal cancer.

Directory of Open Access Journals (Sweden)

Wendy W T Lam

Full Text Available OBJECTIVE: This secondary longitudinal analysis describes distinct quality of life trajectories during eight months of radiation therapy (RT among patients with nasopharyngeal cancer (NPC and examines factors differentiating these trajectories. METHODS: 253 Chinese patients with NPC scheduled for RT were assessed at pre-treatment, and 4 months and 8 months later on QoL (Chinese version of the FACT-G, optimism, pain, eating function, and patient satisfaction. Latent growth mixture modelling identified different trajectories within each of four QoL domains: Physical, Emotional, Social/family, and Functional well-being. Multinomial logistic regression compared optimism, pain, eating function, and patient satisfaction by trajectories adjusted for demographic and medical characteristics. RESULTS: We identified three distinct trajectories for physical and emotional QoL domains, four trajectories for social/family, and two trajectories for functional domains. Within each domain most patients (physical (77%, emotional (85%, social/family (55% and functional (63% experienced relatively stable high levels of well-being over the 8-month period. Different Physical trajectory patterns were predicted by pain and optimism, whereas for Emotion-domain trajectories pain, optimism, eating enjoyment, patient satisfaction with information, and gender were predictive. Age, appetite, optimism, martial status, and household income predicted Social/family trajectories; household income, eating enjoyment, optimism, and patient satisfaction with information predicted Functional trajectories. CONCLUSION: Most patients with NPC showed high stable QoL during radiotherapy. Optimism predicted good QoL. Symptom impacts varied by QoL domain. Information satisfaction was protective in emotional and functional well-being, reflecting the importance in helping patients to establish a realistic expectation of treatment impacts.

Optimal sizing of grid-independent hybrid photovoltaic–battery power systems for household sector

International Nuclear Information System (INIS)

Bianchi, M.; Branchini, L.; Ferrari, C.; Melino, F.

2014-01-01

Highlights: • A feasibility study on a stand-alone solar–battery power generation system is carried out. • An in-house developed calculation code able to estimate photovoltaic panels behaviour is described. • The feasibility of replacing grid electricity with hybrid system is examined. • Guidelines for optimal photovoltaic design are given. • Guidelines for optimal storage sizing in terms of batteries number and capacity are given. - Abstract: The penetration of renewable sources into the grid, particularly wind and solar, have been increasing in recent years. As a consequence, there have been serious concerns over reliable and safety operation of power systems. One possible solution, to improve grid stability, is to integrate energy storage devices into power system network: storing energy produced in periods of low demand to later use, ensuring full exploitation of intermittent available sources. Focusing on stand-alone photovoltaic (PV) energy system, energy storage is needed with the purpose of ensuring continuous power flow, to minimize or, if anything, to neglect electrical grid supply. A comprehensive study on a hybrid stand-alone photovoltaic power system using two different energy storage technologies has been performed. The study examines the feasibility of replacing electricity provided by the grid with hybrid system to meet household demand. In particular, this paper presents first results for photovoltaic (PV)/battery (B) hybrid configuration. The main objective of this paper is focused on PV/B system, to recommend hybrid system optimal design in terms of PV module number, PV module tilt, number and capacity of batteries to minimize or, if possible, to neglect grid supply. This paper is the early stage of a theoretical and experimental study in which two different hybrid power system configurations will be evaluated and compared: (i) PV/B system and (ii) PV/B/fuel cell (FC) system. The aim of the overall study will be the definition of the

Parametric Optimal Design of a Parallel Schönflies-Motion Robot under Pick-And-Place Trajectory Constraints

DEFF Research Database (Denmark)

Wu, Guanglei; Bai, Shaoping; Hjørnet, Preben

2015-01-01

This paper deals with the parametric optimum design of a parallel Schoenflies-motion robot, named "Ragnar", designed for fast and flexible pick-and-place applications. The robot architecture admits a rectangular workspace, which can utilize the shop-floor space efficiently. In this work......, the parametric models of the transmission quality, elasto-statics and dynamics are established. By taking into consideration of design requirements and pick-and-place trajectory, a comprehensive multi-objective optimization problem is formulated to optimize both kinematic and dynamic performances. The Pareto......-front is obtained, which provides optimal solutions to the robot design. Robot prototyping work based on the optimal results is described....

Hybrid Metaheuristic Approach for Nonlocal Optimization of Molecular Systems.

Science.gov (United States)

Dresselhaus, Thomas; Yang, Jack; Kumbhar, Sadhana; Waller, Mark P

2013-04-09

Accurate modeling of molecular systems requires a good knowledge of the structure; therefore, conformation searching/optimization is a routine necessity in computational chemistry. Here we present a hybrid metaheuristic optimization (HMO) algorithm, which combines ant colony optimization (ACO) and particle swarm optimization (PSO) for the optimization of molecular systems. The HMO implementation meta-optimizes the parameters of the ACO algorithm on-the-fly by the coupled PSO algorithm. The ACO parameters were optimized on a set of small difluorinated polyenes where the parameters exhibited small variance as the size of the molecule increased. The HMO algorithm was validated by searching for the closed form of around 100 molecular balances. Compared to the gradient-based optimized molecular balance structures, the HMO algorithm was able to find low-energy conformations with a 87% success rate. Finally, the computational effort for generating low-energy conformation(s) for the phenylalanyl-glycyl-glycine tripeptide was approximately 60 CPU hours with the ACO algorithm, in comparison to 4 CPU years required for an exhaustive brute-force calculation.

Cloud computing-based energy optimization control framework for plug-in hybrid electric bus

International Nuclear Information System (INIS)

Yang, Chao; Li, Liang; You, Sixiong; Yan, Bingjie; Du, Xian

2017-01-01

Considering the complicated characteristics of traffic flow in city bus route and the nonlinear vehicle dynamics, optimal energy management integrated with clustering and recognition of driving conditions in plug-in hybrid electric bus is still a challenging problem. Motivated by this issue, this paper presents an innovative energy optimization control framework based on the cloud computing for plug-in hybrid electric bus. This framework, which includes offline part and online part, can realize the driving conditions clustering in offline part, and the energy management in online part. In offline part, utilizing the operating data transferred from a bus to the remote monitoring center, K-means algorithm is adopted to cluster the driving conditions, and then Markov probability transfer matrixes are generated to predict the possible operating demand of the bus driver. Next in online part, the current driving condition is real-time identified by a well-trained support vector machine, and Markov chains-based driving behaviors are accordingly selected. With the stochastic inputs, stochastic receding horizon control method is adopted to obtain the optimized energy management of hybrid powertrain. Simulations and hardware-in-loop test are carried out with the real-world city bus route, and the results show that the presented strategy could greatly improve the vehicle fuel economy, and as the traffic flow data feedback increases, the fuel consumption of every plug-in hybrid electric bus running in a specific bus route tends to be a stable minimum. - Highlights: • Cloud computing-based energy optimization control framework is proposed. • Driving cycles are clustered into 6 types by K-means algorithm. • Support vector machine is employed to realize the online recognition of driving condition. • Stochastic receding horizon control-based energy management strategy is designed for plug-in hybrid electric bus. • The proposed framework is verified by simulation and hard

Advanced hybrid and electric vehicles system optimization and vehicle integration

CERN Document Server

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.

Component sizing optimization of plug-in hybrid electric vehicles

Energy Technology Data Exchange (ETDEWEB)

Wu, Xiaolan; Cao, Binggang; Li, Xueyan; Xu, Jun; Ren, Xiaolong [School of Mechanical Engineering, Xi' an Jiaotong University, Xi' an, 710049 (China)

2011-03-15

Plug-in hybrid electric vehicles (PHEVs) are considered as one of the most promising means to improve the near-term sustainability of the transportation and stationary energy sectors. This paper describes a methodology for the optimization of PHEVs component sizing using parallel chaos optimization algorithm (PCOA). In this approach, the objective function is defined so as to minimize the drivetrain cost. In addition, the driving performance requirements are considered as constraints. Finally, the optimization process is performed over three different all electric range (AER) and two types of batteries. The results from computer simulation show the effectiveness of the approach and the reduction in drivetrian cost while ensuring the vehicle performance. (author)

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...... 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...... 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....

Optimal Design of a Novel Hybrid Electric Powertrain for Tracked Vehicles

Directory of Open Access Journals (Sweden)

Zhaobo Qin

2017-12-01

Full Text Available Tracked vehicles have been widely used in construction, agriculture, and the military. Major problems facing the industry, however, are high emissions and fuel consumption. Hybrid electric tracked vehicles have thus become increasingly popular because of their improved fuel economy and reduced emissions. While the series hybrid system has drawn the most attention and has been applied in most cases, the low efficiency caused by energy conversion losses and large propulsion motors has limited its development. A novel multi-mode powertrain with two output shafts controlling each side of the track independently is first proposed. The powertrain is a three-planetary-gear power-split system with one engine, three motors, and an ultracapacitor pack. Compared with the existing technologies, the proposed powertrain can realize skid steering without an extra steering mechanism, and significantly improve the overall efficiency. To demonstrate the advantages of the novel powertrain, a topology-control-size integrated optimization problem is solved based on drivability, fuel economy, and cost. Final simulation results show that the optimized design with downsized components can produce about a 30% improvement in drivability and a 15% improvement in fuel economy compared with the commonly used series hybrid benchmark. Moreover, the optimized design is verified to be much more economical taking cumulative cost into account, which is very attractive for potential industrial applications in the future.

Multi-Body Ski Jumper Model with Nonlinear Dynamic Inversion Muscle Control for Trajectory Optimization

Directory of Open Access Journals (Sweden)

Patrick Piprek

2018-02-01

Full Text Available This paper presents an approach to model a ski jumper as a multi-body system for an optimal control application. The modeling is based on the constrained Newton-Euler-Equations. Within this paper the complete multi-body modeling methodology as well as the musculoskeletal modeling is considered. For the musculoskeletal modeling and its incorporation in the optimization model, we choose a nonlinear dynamic inversion control approach. This approach uses the muscle models as nonlinear reference models and links them to the ski jumper movement by a control law. This strategy yields a linearized input-output behavior, which makes the optimal control problem easier to solve. The resulting model of the ski jumper can then be used for trajectory optimization whose results are compared to literature jumps. Ultimately, this enables the jumper to get a very detailed feedback of the flight. To achieve the maximal jump length, exact positioning of his body with respect to the air can be displayed.

Methods for control over learning individual trajectory

Science.gov (United States)

Mitsel, A. A.; Cherniaeva, N. V.

2015-09-01

The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects.

Energy Optimization for a Weak Hybrid Power System of an Automobile Exhaust Thermoelectric Generator

Science.gov (United States)

Fang, Wei; Quan, Shuhai; Xie, Changjun; Tang, Xinfeng; Ran, Bin; Jiao, Yatian

2017-11-01

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.

Improved hybrid optimization algorithm for 3D protein structure prediction.

Science.gov (United States)

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.

Optimal Solution for VLSI Physical Design Automation Using Hybrid Genetic Algorithm

Directory of Open Access Journals (Sweden)

I. Hameem Shanavas

2014-01-01

Full Text Available In Optimization of VLSI Physical Design, area minimization and interconnect length minimization is an important objective in physical design automation of very large scale integration chips. The objective of minimizing the area and interconnect length would scale down the size of integrated chips. To meet the above objective, it is necessary to find an optimal solution for physical design components like partitioning, floorplanning, placement, and routing. This work helps to perform the optimization of the benchmark circuits with the above said components of physical design using hierarchical approach of evolutionary algorithms. The goal of minimizing the delay in partitioning, minimizing the silicon area in floorplanning, minimizing the layout area in placement, minimizing the wirelength in routing has indefinite influence on other criteria like power, clock, speed, cost, and so forth. Hybrid evolutionary algorithm is applied on each of its phases to achieve the objective. Because evolutionary algorithm that includes one or many local search steps within its evolutionary cycles to obtain the minimization of area and interconnect length. This approach combines a hierarchical design like genetic algorithm and simulated annealing to attain the objective. This hybrid approach can quickly produce optimal solutions for the popular benchmarks.

Hybrid Quantum-Classical Approach to Quantum Optimal Control.

Science.gov (United States)

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.

Solving Bi-Objective Optimal Power Flow using Hybrid method of Biogeography-Based Optimization and Differential Evolution Algorithm: A case study of the Algerian Electrical Network

Directory of Open Access Journals (Sweden)

Ouafa Herbadji

2016-03-01

Full Text Available This paper proposes a new hybrid metaheuristique algorithm based on the hybridization of Biogeography-based optimization with the Differential Evolution for solving the optimal power flow problem with emission control. The biogeography-based optimization (BBO algorithm is strongly influenced by equilibrium theory of island biogeography, mainly through two steps: Migration and Mutation. Differential Evolution (DE is one of the best Evolutionary Algorithms for global optimization. The hybridization of these two methods is used to overcome traps of local optimal solutions and problems of time consumption. The objective of this paper is to minimize the total fuel cost of generation, total emission, total real power loss and also maintain an acceptable system performance in terms of limits on generator real power, bus voltages and power flow of transmission lines. In the present work, BBO/DE has been applied to solve the optimal power flow problems on IEEE 30-bus test system and the Algerian electrical network 114 bus. The results obtained from this method show better performances compared with DE, BBO and other well known metaheuristique and evolutionary optimization methods.

A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints

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.

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 -exploring random tree-based algorithms to fi nd smooth and optimal trajectories B MATEBESE1, MK BANDA2 AND S UTETE1 1CSIR Modelling and Digital Science, PO Box 395, Pretoria, South Africa, 0001 2Department of Applied Mathematics, Stellenbosch University... and complex environments. The RRT algorithm is the most popular and has the ability to find a 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...

Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches

Directory of Open Access Journals (Sweden)

Jui-Yu Wu

2013-01-01

Full Text Available Stochastic global optimization (SGO algorithms such as the particle swarm optimization (PSO approach have become popular for solving unconstrained global optimization (UGO problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO method and an artificial immune algorithm-based PSO (AIA-PSO method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.

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.

Low Thrust Trajectory Design for GSFC Missions

Data.gov (United States)

National Aeronautics and Space Administration — The Evolutionary Mission Trajectory Generator (EMTG) is a global trajectory optimization tool. EMTG is intended for use in designing interplanetary missions which...

Optimal control applied to the control strategy of a parallel hybrid vehicle; Commande optimale appliquee a la strategie de commande d'un vehicule hybride parallele

Energy Technology Data Exchange (ETDEWEB)

Delprat, S.; Guerra, T.M. [Universite de Valenciennes et du Hainaut-Cambresis, LAMIH UMR CNRS 8530, 59 - Valenciennes (France); Rimaux, J. [PSA Peugeot Citroen, DRIA/SARA/EEES, 78 - Velizy Villacoublay (France); Paganelli, G. [Center for Automotive Research, Ohio (United States)

2002-07-01

Control strategies are algorithms that calculate the power repartition between the engine and the motor of an hybrid vehicle in order to minimize the fuel consumption and/or emissions. Some algorithms are devoted to real time application whereas others are designed for global optimization in stimulation. The last ones provide solutions which can be used to evaluate the performances of a given hybrid vehicle or a given real time control strategy. The control strategy problem is firstly written into the form of an optimization under constraints problem. A solution based on optimal control is proposed. Results are given for the European Normalized Cycle and a parallel single shaft hybrid vehicle built at the LAMIH (France). (authors)

Optimization of Hybrid Renewable Energy Systems

Science.gov (United States)

Contreras Cordero, Francisco Jose

Use of diesel generators in remote communities is economically and environmentally unsustainable. Consequently, researchers have focussed on designing hybrid renewable energy systems (HRES) for distributed electricity generation in remote communities. However, the cost-effectiveness of interconnecting multiple remote communities (microgrids) has not been explored. The main objective of this thesis is to develop a methodology for optimal design of HRES and microgrids for remote communities. A set of case studies was developed to test this methodology and it was determined that a combination of stand-alone decentralized HRES and microgrids is the most cost-effective power generation scheme when studying a group of remote communities.

DUKSUP: A Computer Program for High Thrust Launch Vehicle Trajectory Design and Optimization

Science.gov (United States)

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

Super-capacitors fuel-cell hybrid electric vehicle optimization and control strategy development

International Nuclear Information System (INIS)

Paladini, Vanessa; Donateo, Teresa; De Risi, Arturo; Laforgia, Domenico

2007-01-01

In the last decades, due to emissions reduction policies, research focused on alternative powertrains among which hybrid electric vehicles (HEVs) powered by fuel cells are becoming an attractive solution. One of the main issues of these vehicles is the energy management in order to improve the overall fuel economy. The present investigation aims at identifying the best hybrid vehicle configuration and control strategy to reduce fuel consumption. The study focuses on a car powered by a fuel cell and equipped with two secondary energy storage devices: batteries and super-capacitors. To model the powertrain behavior an on purpose simulation program called ECoS has been developed in Matlab/Simulink environment. The fuel cell model is based on the Amphlett theory. The battery and the super-capacitor models account for charge/discharge efficiency. The analyzed powertrain is also equipped with an energy regeneration system to recover braking energy. The numerical optimization of vehicle configuration and control strategy of the hybrid electric vehicle has been carried out with a multi objective genetic algorithm. The goal of the optimization is the reduction of hydrogen consumption while sustaining the battery state of charge. By applying the algorithm to different driving cycles, several optimized configurations have been identified and discussed

Power-optimal force decoupling in a hybrid linear reluctance motor

NARCIS (Netherlands)

Overboom, T.T.; Smeets, J.P.C.; Jansen, J.W.; Lomonova, E.A.; Mavrudieva, D.

2015-01-01

This paper concerns the power-optimal decoupling of the propulsion and normal force created by a hybrid linear reluctance motor. The intrinsic limitations to the decoupling is addressed by the visualizing each force component with a quadric surface in the Euclidean space which is spanned by the

Design of Quiet Rotorcraft Approach Trajectories: Verification Phase

Science.gov (United States)

Padula, Sharon L.

2010-01-01

Flight testing that is planned for October 2010 will provide an opportunity to evaluate rotorcraft trajectory optimization techniques. The flight test will involve a fully instrumented MD-902 helicopter, which will be flown over an array of microphones. In this work, the helicopter approach trajectory is optimized via a multiobjective genetic algorithm to improve community noise, passenger comfort, and pilot acceptance. Previously developed optimization strategies are modified to accommodate new helicopter data and to increase pilot acceptance. This paper describes the MD-902 trajectory optimization plus general optimization strategies and modifications that are needed to reduce the uncertainty in noise predictions. The constraints that are imposed by the flight test conditions and characteristics of the MD-902 helicopter limit the testing possibilities. However, the insights that will be gained through this research will prove highly valuable.

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%.

Optimized efficiency of all-electric ships by dc hybrid power systems

Science.gov (United States)

Zahedi, Bijan; Norum, Lars E.; Ludvigsen, Kristine B.

2014-06-01

Hybrid power systems with dc distribution are being considered for commercial marine vessels to comply with new stringent environmental regulations, and to achieve higher fuel economy. In this paper, detailed efficiency analysis of a shipboard dc hybrid power system is carried out. An optimization algorithm is proposed to minimize fuel consumption under various loading conditions. The studied system includes diesel engines, synchronous generator-rectifier units, a full-bridge bidirectional converter, and a Li-Ion battery bank as energy storage. In order to evaluate potential fuel saving provided by such a system, an online optimization strategy for fuel consumption is implemented. An Offshore Support Vessel (OSV) is simulated over different operating modes using the online control strategy. The resulted consumed fuel in the simulation is compared to that of a conventional ac power system, and also a dc power system without energy storage. The results show that while the dc system without energy storage provides noticeable fuel saving compared to the conventional ac system, optimal utilization of the energy storage in the dc system results in twice as much fuel saving.

Optimal Allocation of Power-Electronic Interfaced Wind Turbines Using a Genetic Algorithm - Monte Carlo Hybrid Optimization Method

DEFF Research Database (Denmark)

Chen, Peiyuan; Siano, Pierluigi; Chen, Zhe

2010-01-01

determined by the wind resource and geographic conditions, the location of wind turbines in a power system network may significantly affect the distribution of power flow, power losses, etc. Furthermore, modern WTs with power-electronic interface have the capability of controlling reactive power output...... limit requirements. The method combines the Genetic Algorithm (GA), gradient-based constrained nonlinear optimization algorithm and sequential Monte Carlo simulation (MCS). The GA searches for the optimal locations and capacities of WTs. The gradient-based optimization finds the optimal power factor...... setting of WTs. The sequential MCS takes into account the stochastic behaviour of wind power generation and load. The proposed hybrid optimization method is demonstrated on an 11 kV 69-bus distribution system....

A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources

International Nuclear Information System (INIS)

Kefayat, M.; Lashkar Ara, A.; Nabavi Niaki, S.A.

2015-01-01

Highlights: • A probabilistic optimization framework incorporated with uncertainty is proposed. • A hybrid optimization approach combining ACO and ABC algorithms is proposed. • The problem is to deal with technical, environmental and economical aspects. • A fuzzy interactive approach is incorporated to solve the multi-objective problem. • Several strategies are implemented to compare with literature methods. - Abstract: In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) and continuous (size optimization) structures to achieve advantages of the global and local search ability of ABC and ACO algorithms, respectively. Also, in the proposed algorithm, a multi-objective ABC is used to produce a set of non-dominated solutions which store in the external archive. The objectives consist of minimizing power losses, total emissions produced by substation and resources, total electrical energy cost, and improving the voltage stability. In order to investigate the impact of the uncertainty in the output of the wind energy and load demands, a probabilistic load flow is necessary. In this study, an efficient point estimate method (PEM) is employed to solve the optimization problem in a stochastic environment. The proposed algorithm is tested on the IEEE 33- and 69-bus distribution systems. The results demonstrate the potential and effectiveness of the proposed algorithm in comparison with those of other evolutionary optimization methods

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)

Trajectory optimization using indirect methods and parametric scramjet cycle analysis

OpenAIRE

Williams, Joseph

2016-01-01

This study investigates the solution of time sensitive regional strike trajectories for hypersonic missiles. This minimum time trajectory is suspected to be best performed by scramjet powered hypersonic missiles which creates strong coupled interaction between the flight dynamics and the performance of the engine. Comprehensive engine models are necessary to gain better insight into scramjet propulsion. Separately, robust and comprehensive trajectory analysis provides references for vehicles ...

A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system

International Nuclear Information System (INIS)

Al-falahi, Monaaf D.A.; Jayasinghe, S.D.G.; Enshaei, H.

2017-01-01

Highlights: • Possible combinations and configurations for standalone PV-WT HES were discussed. • Most recently used assessment parameters for standalone PV-WT HES were explained. • Optimization algorithms and software tools were comprehensively reviewed. • The recent trend of using hybrid algorithms over single algorithms was discussed. • Optimization algorithms for sizing standalone PV-WT HES were critically compared. - Abstract: Electricity demand in remote and island areas are generally supplied by diesel or other fossil fuel based generation systems. Nevertheless, due to the increasing cost and harmful emissions of fossil fuels there is a growing trend to use standalone hybrid renewable energy systems (HRESs). Due to the complementary characteristics, matured technologies and availability in most areas, hybrid systems with solar and wind energy have become the popular choice in such applications. However, the intermittency and high net present cost are the challenges associated with solar and wind energy systems. In this context, optimal sizing is a key factor to attain a reliable supply at a low cost through these standalone systems. Therefore, there has been a growing interest to develop algorithms for size optimization in standalone HRESs. The optimal sizing methodologies reported so far can be broadly categorized as classical algorithms, modern techniques and software tools. Modern techniques, based on single artificial intelligence (AI) algorithms, are becoming more popular than classical algorithms owing to their capabilities in solving complex optimization problems. Moreover, in recent years, there has been a clear trend to use hybrid algorithms over single algorithms mainly due to their ability to provide more promising optimization results. This paper aims to present a comprehensive review on recent developments in size optimization methodologies, as well as a critical comparison of single algorithms, hybrid algorithms, and software tools

An optimized hybrid Convolutional Perfectly Matched Layer for efficient absorption of electromagnetic waves

Science.gov (United States)

Darvish, Amirashkan; Zakeri, Bijan; Radkani, Nafiseh

2018-03-01

A hybrid technique is studied in order to improve the performance of Convolutional Perfectly Matched Layer (CPML) in the Finite Difference Time Domain (FDTD) medium. This technique combines the first order of Higdon's annihilation equation as Absorbing Boundary Condition (ABC) with CPML to vanish the Perfect Electric Conductor (PEC) effects at the end of the CPML region. An optimization algorithm is required to find optimum parameters of the proposed absorber. In this investigation, the Particle Swarm Optimization (PSO) is utilized with two separate objective functions in order to minimize the average and peak value of relative error. Using a standard test, the overall performance of the proposed absorber is compared to the original CPML. The results clearly illustrate this method provides approximately 10 dB enhancements in CPML absorption error. The performance, memory and time requirement of the novel absorber, hybrid CPML (H-CPML), was analyzed during 2D and 3D tests and compared to most reported PMLs. The H-CPML requirement of computer resources is similar to CPML and can simply be implemented to truncate FDTD domains. Furthermore, an optimized set of parameters are provided to generalize the hybrid method.

Resizing Technique-Based Hybrid Genetic Algorithm for Optimal Drift Design of Multistory Steel Frame Buildings

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.

Fuel consumption optimization for smart hybrid electric vehicle during a car-following process

Science.gov (United States)

Li, Liang; Wang, Xiangyu; Song, Jian

2017-03-01

Hybrid electric vehicles (HEVs) provide large potential to save energy and reduce emission, and smart vehicles bring out great convenience and safety for drivers. By combining these two technologies, vehicles may achieve excellent performances in terms of dynamic, economy, environmental friendliness, safety, and comfort. Hence, a smart hybrid electric vehicle (s-HEV) is selected as a platform in this paper to study a car-following process with optimizing the fuel consumption. The whole process is a multi-objective optimal problem, whose optimal solution is not just adding an energy management strategy (EMS) to an adaptive cruise control (ACC), but a deep fusion of these two methods. The problem has more restricted conditions, optimal objectives, and system states, which may result in larger computing burden. Therefore, a novel fuel consumption optimization algorithm based on model predictive control (MPC) is proposed and some search skills are adopted in receding horizon optimization to reduce computing burden. Simulations are carried out and the results indicate that the fuel consumption of proposed method is lower than that of the ACC+EMS method on the condition of ensuring car-following performances.

A novel hybrid particle swarm optimization for economic dispatch with valve-point loading effects

Energy Technology Data Exchange (ETDEWEB)

Niknam, Taher, E-mail: niknam@sutech.ac.i [Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, P.O. 71555-313 (Iran, Islamic Republic of); Mojarrad, Hasan Doagou, E-mail: hasan_doagou@yahoo.co [Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, P.O. 71555-313 (Iran, Islamic Republic of); Meymand, Hamed Zeinoddini, E-mail: h.zeinaddini@gmail.co [Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, P.O. 71555-313 (Iran, Islamic Republic of)

2011-04-15

Economic dispatch (ED) is one of the important problems in the operation and management of the electric power systems which is formulated as an optimization problem. Modern heuristics stochastic optimization techniques appear to be efficient in solving ED problem without any restriction because of their ability to seek the global optimal solution. One of modern heuristic algorithms is particle swarm optimization (PSO). In PSO algorithm, particles change place to get close to the best position and find the global minimum point. Also, differential evolution (DE) is a robust statistical method for solving non-linear and non-convex optimization problem. The fast convergence of DE degrades its performance and reduces its search capability that leads to a higher probability towards obtaining a local optimum. In order to overcome this drawback a hybrid method is presented to solve the ED problem with valve-point loading effect by integrating the variable DE with the fuzzy adaptive PSO called FAPSO-VDE. DE is the main optimizer and the PSO is used to maintain the population diversity and prevent leading to misleading local optima for every improvement in the solution of the DE run. The parameters of proposed hybrid algorithm such as inertia weight, mutation and crossover factors are adaptively adjusted. The feasibility and effectiveness of the proposed hybrid algorithm is demonstrated for two case studies and results are compared with those of other methods. It is shown that FAPSO-VDE has high quality solution, superior convergence characteristics and shorter computation time.

Techno–economic design of hybrid electric vehicles and possibilities of the multi-objective optimization structure

International Nuclear Information System (INIS)

Dimitrova, Zlatina; Maréchal, François

2016-01-01

Highlights: • The full hybrid electric vehicle suits for sustainable urban mobility and customer investment. • The full hybrid electric urban vehicle is efficient, with consumption less than 2 L/100 km. • The range extender vehicle is a technology for low CO_2 emissions – less than 20 g/km CO_2_. • The total CO_2 emissions for range extender and plug-in vehicles are sensitive to the use place. - Abstract: The design criteria for modern sustainable development of vehicle powertrain are the high energy efficiency of the conversion system, the competitive cost and the lowest possible environmental impacts. In this article a multi-objective optimization methodology is applied on hybrid electric vehicles study in order to define the optimal powertrain configurations of the vehicle, estimate the cost of the powertrain equipment and show the environmental impact of the technical choices on the lifecycle perspective of the vehicle. The study illustrates optimal design solutions for low fuel consumption vehicles – between 2 L/100 km and 3 L/100 km. For that a simulation model of a hybrid electric vehicle is made. This model is coupled with a cost model for the vehicle. The techno–economic optimizations are performed for two case studies, illustrating the possibilities of the optimization superstructure. Firstly the life cycle inventory is written as a function of the parameters of the techno–economic model. In this way, the obtained environmental indicators from the life cycle assessment are calculated as a function of the decision variables for the vehicle design. In the second example the parameters of the energy distribution function are included as decision variables in the techno–economic optimization and are simultaneously optimized.

Improved Hybrid Fireworks Algorithm-Based Parameter Optimization in High-Order Sliding Mode Control of Hypersonic Vehicles

Directory of Open Access Journals (Sweden)

Xiaomeng Yin

2018-01-01

Full Text Available With respect to the nonlinear hypersonic vehicle (HV dynamics, achieving a satisfactory tracking control performance under uncertainties is always a challenge. The high-order sliding mode control (HOSMC method with strong robustness has been applied to HVs. However, there are few methods for determining suitable HOSMC parameters for an efficacious control of HV, given that the uncertainties are randomly distributed. In this study, we introduce a hybrid fireworks algorithm- (FWA- based parameter optimization into HV control design to satisfy the design requirements with high probability. First, the complex relation between design parameters and the cost function that evaluates the likelihood of system instability and violation of design requirements is modeled via stochastic robustness analysis. Subsequently, we propose an efficient hybrid FWA to solve the complex optimization problem concerning the uncertainties. The efficiency of the proposed hybrid FWA-based optimization method is demonstrated in the search of the optimal HV controller, in which the proposed method exhibits a better performance when compared with other algorithms.

A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem.

Science.gov (United States)

Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah

2016-01-01

The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.

A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem

Science.gov (United States)

Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah

2016-01-01

The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them. PMID:26819585

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.

Optimizing Thermal-Elastic Properties of C/C–SiC Composites Using a Hybrid Approach and PSO Algorithm

Science.gov (United States)

Xu, Yingjie; Gao, Tian

2016-01-01

Carbon fiber-reinforced multi-layered pyrocarbon–silicon carbide matrix (C/C–SiC) composites are widely used in aerospace structures. The complicated spatial architecture and material heterogeneity of C/C–SiC composites constitute the challenge for tailoring their properties. Thus, discovering the intrinsic relations between the properties and the microstructures and sequentially optimizing the microstructures to obtain composites with the best performances becomes the key for practical applications. The objective of this work is to optimize the thermal-elastic properties of unidirectional C/C–SiC composites by controlling the multi-layered matrix thicknesses. A hybrid approach based on micromechanical modeling and back propagation (BP) neural network is proposed to predict the thermal-elastic properties of composites. Then, a particle swarm optimization (PSO) algorithm is interfaced with this hybrid model to achieve the optimal design for minimizing the coefficient of thermal expansion (CTE) of composites with the constraint of elastic modulus. Numerical examples demonstrate the effectiveness of the proposed hybrid model and optimization method. PMID:28773343

The time optimal trajectory planning with limitation of operating task for the Tokamak inspecting manipulator

Energy Technology Data Exchange (ETDEWEB)

Wang, Hesheng; Lai, Yinping [Department of Automation,Shanghai Jiao Tong University, Shanghai (China); Key Laboratory of System Control and Information Processing, Ministry of Education of China (China); Chen, Weidong, E-mail: wdchen@sjtu.edu.cn [Department of Automation,Shanghai Jiao Tong University, Shanghai (China); Key Laboratory of System Control and Information Processing, Ministry of Education of China (China)

2016-12-15

In this paper, a new optimization model of time optimal trajectory planning with limitation of operating task for the Tokamak inspecting manipulator is designed. The task of this manipulator is to inspect the components of Tokamak, the inspecting velocity of manipulator must be limited in the operating space in order to get the clear pictures. With the limitation of joint velocity, acceleration and jerk, this optimization model can not only get the minimum working time along a specific path, but also ensure the imaging quality of camera through the constraint of inspecting velocity. The upper bound of the scanning speed is not a constant but changes according to the observation distance of camera in real time. The relation between scanning velocity and observation distance is estimated by curve-fitting. Experiment has been carried out to verify the feasibility of optimization model, moreover, the Laplace image sharpness evaluation method is adopted to evaluate the quality of images obtained by the proposed method.

The time optimal trajectory planning with limitation of operating task for the Tokamak inspecting manipulator

International Nuclear Information System (INIS)

Wang, Hesheng; Lai, Yinping; Chen, Weidong

2016-01-01

In this paper, a new optimization model of time optimal trajectory planning with limitation of operating task for the Tokamak inspecting manipulator is designed. The task of this manipulator is to inspect the components of Tokamak, the inspecting velocity of manipulator must be limited in the operating space in order to get the clear pictures. With the limitation of joint velocity, acceleration and jerk, this optimization model can not only get the minimum working time along a specific path, but also ensure the imaging quality of camera through the constraint of inspecting velocity. The upper bound of the scanning speed is not a constant but changes according to the observation distance of camera in real time. The relation between scanning velocity and observation distance is estimated by curve-fitting. Experiment has been carried out to verify the feasibility of optimization model, moreover, the Laplace image sharpness evaluation method is adopted to evaluate the quality of images obtained by the proposed method.

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.

Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems.

Science.gov (United States)

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.

Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems

International Nuclear Information System (INIS)

Xu, Y; Li, N

2014-01-01

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. (paper)

An optimal control strategy for hybrid actuator systems: Application to an artificial muscle with electric motor assist.

Science.gov (United States)

Ishihara, Koji; Morimoto, Jun

2018-03-01

Humans use multiple muscles to generate such joint movements as an elbow motion. With multiple lightweight and compliant actuators, joint movements can also be efficiently generated. Similarly, robots can use multiple actuators to efficiently generate a one degree of freedom movement. For this movement, the desired joint torque must be properly distributed to each actuator. One approach to cope with this torque distribution problem is an optimal control method. However, solving the optimal control problem at each control time step has not been deemed a practical approach due to its large computational burden. In this paper, we propose a computationally efficient method to derive an optimal control strategy for a hybrid actuation system composed of multiple actuators, where each actuator has different dynamical properties. We investigated a singularly perturbed system of the hybrid actuator model that subdivided the original large-scale control problem into smaller subproblems so that the optimal control outputs for each actuator can be derived at each control time step and applied our proposed method to our pneumatic-electric hybrid actuator system. Our method derived a torque distribution strategy for the hybrid actuator by dealing with the difficulty of solving real-time optimal control problems. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Proton Conductive Channel Optimization in Methanol Resistive Hybrid Hyperbranched Polyamide Proton Exchange Membrane

Directory of Open Access Journals (Sweden)

Liying Ma

2017-12-01

Full Text Available Based on a previously developed polyamide proton conductive macromolecule, the nano-scale structure of the self-assembled proton conductive channels (PCCs is adjusted via enlarging the nano-scale pore size within the macromolecules. Hyperbranched polyamide macromolecules with different size are synthesized from different monomers to tune the nano-scale pore size within the macromolecules, and a series of hybrid membranes are prepared from these two micromoles to optimize the PCC structure in the proton exchange membrane. The optimized membrane exhibits methanol permeability low to 2.2 × 10−7 cm2/s, while the proton conductivity of the hybrid membrane can reach 0.25 S/cm at 80 °C, which was much higher than the value of the Nafion 117 membrane (0.192 S/cm. By considering the mechanical, dimensional, and the thermal properties, the hybrid hyperbranched polyamide proton exchange membrane (PEM exhibits promising application potential in direct methanol fuel cells (DMFC.

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...

Influence of the snubbers and matching transformer on an optimal trajectory controlled resonant transistor DC/DC converter

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.

Low Thrust Trajectory Design for GSFC Missions Project

Data.gov (United States)

National Aeronautics and Space Administration — The Evolutionary Mission Trajectory Generator (EMTG) is a global trajectory optimization tool. EMTG is intended for use in designing interplanetary missions which...

Joint cost of energy under an optimal economic policy of hybrid power systems subject to uncertainty

International Nuclear Information System (INIS)

Díaz, Guzmán; Planas, Estefanía; Andreu, Jon; Kortabarria, Iñigo

2015-01-01

Economical optimization of hybrid systems is usually performed by means of LCoE (levelized cost of energy) calculation. Previous works deal with the LCoE calculation of the whole hybrid system disregarding an important issue: the stochastic component of the system units must be jointly considered. This paper deals with this issue and proposes a new fast optimal policy that properly calculates the LCoE of a hybrid system and finds the lowest LCoE. This proposed policy also considers the implied competition among power sources when variability of gas and electricity prices are taken into account. Additionally, it presents a comparative between the LCoE of the hybrid system and its individual technologies of generation by means of a fast and robust algorithm based on vector logical computation. Numerical case analyses based on realistic data are presented that valuate the contribution of technologies in a hybrid power system to the joint LCoE. - Highlights: • We perform the LCoE calculation with the stochastic component jointly considered. • We propose a fast an optimal policy that minimizes the LCoE. • We compare the obtained LCoEs by means of a fast and robust algorithm. • We take into account the competition among gas prices and electricity prices

Optimal and Modular Configuration of Wind Integrated Hybrid Power Plants for Off-Grid Systems

DEFF Research Database (Denmark)

Petersen, Lennart; Iov, Florin; Tarnowski, German Claudio

2018-01-01

This paper focusses on the system configuration of offgrid hybrid power plants including wind power generation. First, a modular and scalable system topology is proposed. Secondly, an optimal sizing algorithm is developed in order to determine the installed capacities of wind turbines, PV system......, battery energy storage system and generator sets. The novelty of this work lies in a robust sizing algorithm with respect to the required resolution of resource data in order to account for intra-hour power variations. Moreover, the involvement of the electrical infrastructure enables a precise estimation...... of power losses within the hybrid power plant as well as the consideration of both active and reactive power load demand for optimally sizing the plant components. The main outcome of this study is a methodology to determine feasible system configurations of modular and scalable wind integrated hybrid...

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.

Modeling and Optimal Control of a Class of Warfare Hybrid Dynamic Systems Based on Lanchester (n,1 Attrition Model

Directory of Open Access Journals (Sweden)

Xiangyong Chen

2014-01-01

hybrid dynamic systems is established based on Lanchester equation in a (n,1 battle, where a heterogeneous force of n different troop types faces a homogeneous force. This model can be characterized by the interaction of continuous-time models (governed by Lanchester equation, and discrete event systems (described by variable tactics. Furthermore, an expository discussion is presented on an optimal variable tactics control problem for warfare hybrid dynamic system. The optimal control strategies are designed based on dynamic programming and differential game theory. As an example of the consequences of this optimal control problem, we take the (2, 1 case and solve the optimal strategies in a (2, 1 case. Simulation results show the feasibility of warfare hybrid system model and the effectiveness of the optimal control strategies designed.

Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators

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Kiran Teeparthi

2017-04-01

Full Text Available In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultaneously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of getting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large interconnected power system to solve MO-SCOPF problem with integration of wind and thermal generators.

Particle swarm optimization of driving torque demand decision based on fuel economy for plug-in hybrid electric vehicle

International Nuclear Information System (INIS)

Shen, Peihong; Zhao, Zhiguo; Zhan, Xiaowen; Li, Jingwei

2017-01-01

In this paper, an energy management strategy based on logic threshold is proposed for a plug-in hybrid electric vehicle. The plug-in hybrid electric vehicle powertrain model is established using MATLAB/Simulink based on experimental tests of the power components, which is validated by the comparison with the verified simulation model which is built in the AVL Cruise. The influence of the driving torque demand decision on the fuel economy of plug-in hybrid electric vehicle is studied using a simulation. The optimization method for the driving torque demand decision, which refers to the relationship between the accelerator pedal opening and driving torque demand, from the perspective of fuel economy is formulated. The dynamically changing inertia weight particle swarm optimization is used to optimize the decision parameters. The simulation results show that the optimized driving torque demand decision can improve the PHEV fuel economy by 15.8% and 14.5% in the fuel economy test driving cycle of new European driving cycle and worldwide harmonized light vehicles test respectively, using the same rule-based energy management strategy. The proposed optimization method provides a theoretical guide for calibrating the parameters of driving torque demand decision to improve the fuel economy of the real plug-in hybrid electric vehicle. - Highlights: • The influence of the driving torque demand decision on the fuel economy is studied. • The optimization method for the driving torque demand decision is formulated. • An improved particle swarm optimization is utilized to optimize the parameters. • Fuel economy is improved by using the optimized driving torque demand decision.

Constrained Optimization Based on Hybrid Evolutionary Algorithm and Adaptive Constraint-Handling Technique

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...

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...

A Hybrid Optimization Method for Reactive Power and Voltage Control Considering Power Loss Minimization

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...

Design, implementation, and experimental validation of optimal power split control for hybrid electric trucks

NARCIS (Netherlands)

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

Trajectory planning of mobile robots using indirect solution of optimal control method in generalized point-to-point task

Science.gov (United States)

Nazemizadeh, M.; Rahimi, H. N.; Amini Khoiy, K.

2012-03-01

This paper presents an optimal control strategy for optimal trajectory planning of mobile robots by considering nonlinear dynamic model and nonholonomic constraints of the system. The nonholonomic constraints of the system are introduced by a nonintegrable set of differential equations which represent kinematic restriction on the motion. The Lagrange's principle is employed to derive the nonlinear equations of the system. Then, the optimal path planning of the mobile robot is formulated as an optimal control problem. To set up the problem, the nonlinear equations of the system are assumed as constraints, and a minimum energy objective function is defined. To solve the problem, an indirect solution of the optimal control method is employed, and conditions of the optimality derived as a set of coupled nonlinear differential equations. The optimality equations are solved numerically, and various simulations are performed for a nonholonomic mobile robot to illustrate effectiveness of the proposed method.

Flight test trajectory control analysis

Science.gov (United States)

Walker, R.; Gupta, N.

1983-01-01

Recent extensions to optimal control theory applied to meaningful linear models with sufficiently flexible software tools provide powerful techniques for designing flight test trajectory controllers (FTTCs). This report describes the principal steps for systematic development of flight trajectory controllers, which can be summarized as planning, modeling, designing, and validating a trajectory controller. The techniques have been kept as general as possible and should apply to a wide range of problems where quantities must be computed and displayed to a pilot to improve pilot effectiveness and to reduce workload and fatigue. To illustrate the approach, a detailed trajectory guidance law is developed and demonstrated for the F-15 aircraft flying the zoom-and-pushover maneuver.

An Integrated Tool for Low Thrust Optimal Control Orbit Transfers in Interplanetary Trajectories

Science.gov (United States)

Dargent, T.; Martinot, V.

In the last recent years a significant progress has been made in optimal control orbit transfers using low thrust electrical propulsion for interplanetary missions. The system objective is always the same: decrease the transfer duration and increase the useful satellite mass. The optimum control strategy to perform the minimum time to orbit or the minimum fuel consumption requires the use of sophisticated mathematical tools, most of the time dedicated to a specific mission and therefore hardly reusable. To improve this situation and enable Alcatel Space to perform rather quick trajectory design as requested by mission analysis, we have developed a software tool T-3D dedicated to optimal control orbit transfers which integrates various initial and terminal rendezvous conditions - e.g. fixed arrival time for planet encounter - and engine thrust profiles -e.g. thrust law variation with respect to the distance to the Sun -. This single and quite versatile tool allows to perform analyses like minimum consumption for orbit insertions around a planet from an hyperbolic trajectory, interplanetary orbit transfers, low thrust minimum time multiple revolution orbit transfers, etc… From a mathematical point of view, the software relies on the minimum principle formulation to find the necessary conditions of optimality. The satellite dynamics is a two body model and relies of an equinoctial formulation of the Gauss equation. This choice has been made for numerical purpose and to solve more quickly the two point boundaries values problem. In order to handle the classical problem of co-state variables initialization, problems simpler than the actual one can be solved straight forward by the tool and the values of the co-state variables are kept as first guess for a more complex problem. Finally, a synthesis of the test cases is presented to illustrate the capacities of the tool, mixing examples of interplanetary mission, orbit insertion, multiple revolution orbit transfers

Valence electronic structure of cobalt phthalocyanine from an optimally tuned range-separated hybrid functional.

Science.gov (United States)

Brumboiu, Iulia Emilia; Prokopiou, Georgia; Kronik, Leeor; Brena, Barbara

2017-07-28

We analyse the valence electronic structure of cobalt phthalocyanine (CoPc) by means of optimally tuning a range-separated hybrid functional. The tuning is performed by modifying both the amount of short-range exact exchange (α) included in the hybrid functional and the range-separation parameter (γ), with two strategies employed for finding the optimal γ for each α. The influence of these two parameters on the structural, electronic, and magnetic properties of CoPc is thoroughly investigated. The electronic structure is found to be very sensitive to the amount and range in which the exact exchange is included. The electronic structure obtained using the optimal parameters is compared to gas-phase photo-electron data and GW calculations, with the unoccupied states additionally compared with inverse photo-electron spectroscopy measurements. The calculated spectrum with tuned γ, determined for the optimal value of α = 0.1, yields a very good agreement with both experimental results and with GW calculations that well-reproduce the experimental data.

A Hybrid Genetic Algorithm Approach for Optimal Power Flow

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Sydulu Maheswarapu

2011-08-01

Full Text Available This paper puts forward a reformed hybrid genetic algorithm (GA based approach to the optimal power flow. In the approach followed here, continuous variables are designed using real-coded GA and discrete variables are processed as binary strings. The outcomes are compared with many other methods like simple genetic algorithm (GA, adaptive genetic algorithm (AGA, differential evolution (DE, particle swarm optimization (PSO and music based harmony search (MBHS on a IEEE30 bus test bed, with a total load of 283.4 MW. Its found that the proposed algorithm is found to offer lowest fuel cost. The proposed method is found to be computationally faster, robust, superior and promising form its convergence characteristics.

Optimization-based power management of hybrid power systems with applications in advanced hybrid electric vehicles and wind farms with battery storage

Science.gov (United States)

Borhan, Hoseinali

Modern hybrid electric vehicles and many stationary renewable power generation systems combine multiple power generating and energy storage devices to achieve an overall system-level efficiency and flexibility which is higher than their individual components. The power or energy management control, "brain" of these "hybrid" systems, determines adaptively and based on the power demand the power split between multiple subsystems and plays a critical role in overall system-level efficiency. This dissertation proposes that a receding horizon optimal control (aka Model Predictive Control) approach can be a natural and systematic framework for formulating this type of power management controls. More importantly the dissertation develops new results based on the classical theory of optimal control that allow solving the resulting optimal control problem in real-time, in spite of the complexities that arise due to several system nonlinearities and constraints. The dissertation focus is on two classes of hybrid systems: hybrid electric vehicles in the first part and wind farms with battery storage in the second part. The first part of the dissertation proposes and fully develops a real-time optimization-based power management strategy for hybrid electric vehicles. Current industry practice uses rule-based control techniques with "else-then-if" logic and look-up maps and tables in the power management of production hybrid vehicles. These algorithms are not guaranteed to result in the best possible fuel economy and there exists a gap between their performance and a minimum possible fuel economy benchmark. Furthermore, considerable time and effort are spent calibrating the control system in the vehicle development phase, and there is little flexibility in real-time handling of constraints and re-optimization of the system operation in the event of changing operating conditions and varying parameters. In addition, a proliferation of different powertrain configurations may

Optimal integration strategies for a syngas fuelled SOFC and gas turbine hybrid

Science.gov (United States)

Zhao, Yingru; Sadhukhan, Jhuma; Lanzini, Andrea; Brandon, Nigel; Shah, Nilay

This article aims to develop a thermodynamic modelling and optimization framework for a thorough understanding of the optimal integration of fuel cell, gas turbine and other components in an ambient pressure SOFC-GT hybrid power plant. This method is based on the coupling of a syngas-fed SOFC model and an associated irreversible GT model, with an optimization algorithm developed using MATLAB to efficiently explore the range of possible operating conditions. Energy and entropy balance analysis has been carried out for the entire system to observe the irreversibility distribution within the plant and the contribution of different components. Based on the methodology developed, a comprehensive parametric analysis has been performed to explore the optimum system behavior, and predict the sensitivity of system performance to the variations in major design and operating parameters. The current density, operating temperature, fuel utilization and temperature gradient of the fuel cell, as well as the isentropic efficiencies and temperature ratio of the gas turbine cycle, together with three parameters related to the heat transfer between subsystems are all set to be controllable variables. Other factors affecting the hybrid efficiency have been further simulated and analysed. The model developed is able to predict the performance characteristics of a wide range of hybrid systems potentially sizing from 2000 to 2500 W m -2 with efficiencies varying between 50% and 60%. The analysis enables us to identify the system design tradeoffs, and therefore to determine better integration strategies for advanced SOFC-GT systems.

Simulation-Based Early Prediction of Rocket, Artillery, and Mortar Trajectories and Real-Time Optimization for Counter-RAM Systems

Directory of Open Access Journals (Sweden)

Arash Ramezani

2017-01-01

Full Text Available The threat imposed by terrorist attacks is a major hazard for military installations, for example, in Iraq and Afghanistan. The large amounts of rockets, artillery projectiles, and mortar grenades (RAM that are available pose serious threats to military forces. An important task for international research and development is to protect military installations and implement an accurate early warning system against RAM threats on conventional computer systems in out-of-area field camps. This work presents a method for determining the trajectory, caliber, and type of a projectile based on the estimation of the ballistic coefficient. A simulation-based optimization process is presented that enables iterative adjustment of predicted trajectories in real time. Analytical and numerical methods are used to reduce computing time for out-of-area missions and low-end computer systems. A GUI is programmed to present the results. It allows for comparison between predicted and actual trajectories. Finally, different aspects and restrictions for measuring the quality of the results are discussed.

Complexity Science Applications to Dynamic Trajectory Management: Research Strategies

Science.gov (United States)

Sawhill, Bruce; Herriot, James; Holmes, Bruce J.; Alexandrov, Natalia

2009-01-01

The promise of the Next Generation Air Transportation System (NextGen) is strongly tied to the concept of trajectory-based operations in the national airspace system. Existing efforts to develop trajectory management concepts are largely focused on individual trajectories, optimized independently, then de-conflicted among each other, and individually re-optimized, as possible. The benefits in capacity, fuel, and time are valuable, though perhaps could be greater through alternative strategies. The concept of agent-based trajectories offers a strategy for automation of simultaneous multiple trajectory management. The anticipated result of the strategy would be dynamic management of multiple trajectories with interacting and interdependent outcomes that satisfy multiple, conflicting constraints. These constraints would include the business case for operators, the capacity case for the Air Navigation Service Provider (ANSP), and the environmental case for noise and emissions. The benefits in capacity, fuel, and time might be improved over those possible under individual trajectory management approaches. The proposed approach relies on computational agent-based modeling (ABM), combinatorial mathematics, as well as application of "traffic physics" concepts to the challenge, and modeling and simulation capabilities. The proposed strategy could support transforming air traffic control from managing individual aircraft behaviors to managing systemic behavior of air traffic in the NAS. A system built on the approach could provide the ability to know when regions of airspace approach being "full," that is, having non-viable local solution space for optimizing trajectories in advance.

Study of particle swarm optimization particle trajectories

CSIR Research Space (South Africa)

Van den Bergh, F

2006-01-01

Full Text Available . These theoretical studies concentrate mainly on simplified PSO systems. This paper overviews current theoretical studies, and extend these studies to investigate particle trajectories for general swarms to include the influence of the inertia term. The paper also...

Designing train-speed trajectory with energy efficiency and service quality

Science.gov (United States)

Jia, Jiannan; Yang, Kai; Yang, Lixing; Gao, Yuan; Li, Shukai

2018-05-01

With the development of automatic train operations, optimal trajectory design is significant to the performance of train operations in railway transportation systems. Considering energy efficiency and service quality, this article formulates a bi-objective train-speed trajectory optimization model to minimize simultaneously the energy consumption and travel time in an inter-station section. This article is distinct from previous studies in that more sophisticated train driving strategies characterized by the acceleration/deceleration gear, the cruising speed, and the speed-shift site are specifically considered. For obtaining an optimal train-speed trajectory which has equal satisfactory degree on both objectives, a fuzzy linear programming approach is applied to reformulate the objectives. In addition, a genetic algorithm is developed to solve the proposed train-speed trajectory optimization problem. Finally, a series of numerical experiments based on a real-world instance of Beijing-Tianjin Intercity Railway are implemented to illustrate the practicability of the proposed model as well as the effectiveness of the solution methodology.

Sizing and Optimization for Hybrid Central in South Algeria Based on Three Different Generators

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Chouaib Ammari

2017-11-01

Full Text Available In this paper, we will size an optimum hybrid central content three different generators, two on renewable energy (solar photovoltaic and wind power and two nonrenewable (diesel generator and storage system because the new central generator has started to consider the green power technology in order for best future to the world, this central will use all the green power resource available and distributes energy to a small isolated village in southwest of Algeria named “Timiaouine”. The consumption of this village estimated with detailed in two season; season low consumption (winter and high consumption (summer, the hybrid central will be optimized by program Hybrid Optimization Model for Electric Renewable (HOMER PRO, this program will simulate in two configuration, the first with storage system, the second without storage system and in the end the program HOMER PRO will choose the best configuration which is the mixture of both economic and ecologic configurations, this central warrants the energetic continuity of village. Article History: Received May 18th 2017; Received in revised form July 17th 2017; Accepted Sept 3rd 2017; Available online How to Cite This Article: Ammari, C., Hamouda,M., and Makhloufi,S. (2017 Sizing and Optimization for Hybrid Central in South Algeria Based on Three Different Generators. International Journal of Renewable Energy Development, 6(3, 263-272. http://doi.org/10.14710/ijred.6.3.263-272

Optimal control of a fuel cell/wind/PV/grid hybrid system with thermal heat pump load

CSIR Research Space (South Africa)

Sichilalu, S

2016-10-01

Full Text Available This paper presents an optimal energy management strategy for a grid-tied photovoltaic–wind-fuel cell hybrid power supply system. The hybrid system meets the load demand consisting of an electrical load and a heat pump water heater supplying thermal...

Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization

International Nuclear Information System (INIS)

Chen, Syuan-Yi; Hung, Yi-Hsuan; Wu, Chien-Hsun; Huang, Siang-Ting

2015-01-01

Highlights: • Online sub-optimal energy management using IPSO. • A second-order HEV model with 5 major segments was built. • IPSO with equivalent-fuel fitness function using 5 particles. • Engine, rule-based control, PSO, IPSO and ECMS are compared. • Max. 31+% fuel economy and 56+% energy consumption improved. - Abstract: This study developed an online suboptimal energy management system by using improved particle swarm optimization (IPSO) for engine/motor hybrid electric vehicles. The vehicle was modeled on the basis of second-order dynamics, and featured five major segments: a battery, a spark ignition engine, a lithium battery, transmission and vehicle dynamics, and a driver model. To manage the power distribution of dual power sources, the IPSO was equipped with three inputs (rotational speed, battery state-of-charge, and demanded torque) and one output (power split ratio). Five steps were developed for IPSO: (1) initialization; (2) determination of the fitness function; (3) selection and memorization; (4) modification of position and velocity; and (5) a stopping rule. Equivalent fuel consumption by the engine and motor was used as the fitness function with five particles, and the IPSO-based vehicle control unit was completed and integrated with the vehicle simulator. To quantify the energy improvement of IPSO, a four-mode rule-based control (system ready, motor only, engine only, and hybrid modes) was designed according to the engine efficiency and rotational speed. A three-loop Equivalent Consumption Minimization Strategy (ECMS) was coded as the best case. The simulation results revealed that IPSO searches the optimal solution more efficiently than conventional PSO does. In two standard driving cycles, ECE and FTP, the improvements in the equivalent fuel consumption and energy consumption compared to baseline were (24.25%, 45.27%) and (31.85%, 56.41%), respectively, for the IPSO. The CO_2 emission for all five cases (pure engine, rule-based, PSO

On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles

Directory of Open Access Journals (Sweden)

Imran Rahman

2016-03-01

Full Text Available Transportation electrification has undergone major changes since the last decade. Success of smart grid with renewable energy integration solely depends upon the large-scale penetration of plug-in hybrid electric vehicles (PHEVs for a sustainable and carbon-free transportation sector. One of the key performance indicators in hybrid electric vehicle is the State-of-Charge (SoC which needs to be optimized for the betterment of charging infrastructure using stochastic computational methods. In this paper, a newly emerged Accelerated particle swarm optimization (APSO technique was applied and compared with standard particle swarm optimization (PSO considering charging time and battery capacity. Simulation results obtained for maximizing the highly nonlinear objective function indicate that APSO achieves some improvements in terms of best fitness and computation time.

Optimal sizing of a hybrid grid-connected photovoltaic and wind power system

International Nuclear Information System (INIS)

González, Arnau; Riba, Jordi-Roger; Rius, Antoni; Puig, Rita

2015-01-01

Highlights: • Hybrid renewable energy systems are efficient mechanisms to generate electrical power. • This work optimally sizes hybrid grid-connected photovoltaic–wind power systems. • It deals with hourly wind, solar irradiation and electricity demand data. • The system cost is minimized while matching the electricity supply with the demand. • A sensitivity analysis to detect the most critical design variables has been done. - Abstract: Hybrid renewable energy systems (HRES) have been widely identified as an efficient mechanism to generate electrical power based on renewable energy sources (RES). This kind of energy generation systems are based on the combination of one or more RES allowing to complement the weaknesses of one with strengths of another and, therefore, reducing installation costs with an optimized installation. To do so, optimization methodologies are a trendy mechanism because they allow attaining optimal solutions given a certain set of input parameters and variables. This work is focused on the optimal sizing of hybrid grid-connected photovoltaic–wind power systems from real hourly wind and solar irradiation data and electricity demand from a certain location. The proposed methodology is capable of finding the sizing that leads to a minimum life cycle cost of the system while matching the electricity supply with the local demand. In the present article, the methodology is tested by means of a case study in which the actual hourly electricity retail and market prices have been implemented to obtain realistic estimations of life cycle costs and benefits. A sensitivity analysis that allows detecting to which variables the system is more sensitive has also been performed. Results presented show that the model responds well to changes in the input parameters and variables while providing trustworthy sizing solutions. According to these results, a grid-connected HRES consisting of photovoltaic (PV) and wind power technologies would be

A New Hybrid Nelder-Mead Particle Swarm Optimization for Coordination Optimization of Directional Overcurrent Relays

Directory of Open Access Journals (Sweden)

An Liu

2012-01-01

Full Text Available Coordination optimization of directional overcurrent relays (DOCRs is an important part of an efficient distribution system. This optimization problem involves obtaining the time dial setting (TDS and pickup current (Ip values of each DOCR. The optimal results should have the shortest primary relay operating time for all fault lines. Recently, the particle swarm optimization (PSO algorithm has been considered an effective tool for linear/nonlinear optimization problems with application in the protection and coordination of power systems. With a limited runtime period, the conventional PSO considers the optimal solution as the final solution, and an early convergence of PSO results in decreased overall performance and an increase in the risk of mistaking local optima for global optima. Therefore, this study proposes a new hybrid Nelder-Mead simplex search method and particle swarm optimization (proposed NM-PSO algorithm to solve the DOCR coordination optimization problem. PSO is the main optimizer, and the Nelder-Mead simplex search method is used to improve the efficiency of PSO due to its potential for rapid convergence. To validate the proposal, this study compared the performance of the proposed algorithm with that of PSO and original NM-PSO. The findings demonstrate the outstanding performance of the proposed NM-PSO in terms of computation speed, rate of convergence, and feasibility.

A Hybrid Optimization Method for Solving Bayesian Inverse Problems under Uncertainty.

Directory of Open Access Journals (Sweden)

Kai Zhang

Full Text Available In this paper, we investigate the application of a new method, the Finite Difference and Stochastic Gradient (Hybrid method, for history matching in reservoir models. History matching is one of the processes of solving an inverse problem by calibrating reservoir models to dynamic behaviour of the reservoir in which an objective function is formulated based on a Bayesian approach for optimization. The goal of history matching is to identify the minimum value of an objective function that expresses the misfit between the predicted and measured data of a reservoir. To address the optimization problem, we present a novel application using a combination of the stochastic gradient and finite difference methods for solving inverse problems. The optimization is constrained by a linear equation that contains the reservoir parameters. We reformulate the reservoir model's parameters and dynamic data by operating the objective function, the approximate gradient of which can guarantee convergence. At each iteration step, we obtain the relatively 'important' elements of the gradient, which are subsequently substituted by the values from the Finite Difference method through comparing the magnitude of the components of the stochastic gradient, which forms a new gradient, and we subsequently iterate with the new gradient. Through the application of the Hybrid method, we efficiently and accurately optimize the objective function. We present a number numerical simulations in this paper that show that the method is accurate and computationally efficient.

Prediction of human gait trajectories during the SSP using a neuromusculoskeletal modeling: A challenge for parametric optimization.

Science.gov (United States)

Seyed, Mohammadali Rahmati; Mostafa, Rostami; Borhan, Beigzadeh

2018-04-27

The parametric optimization techniques have been widely employed to predict human gait trajectories; however, their applications to reveal the other aspects of gait are questionable. The aim of this study is to investigate whether or not the gait prediction model is able to justify the movement trajectories for the higher average velocities. A planar, seven-segment model with sixteen muscle groups was used to represent human neuro-musculoskeletal dynamics. At first, the joint angles, ground reaction forces (GRFs) and muscle activations were predicted and validated for normal average velocity (1.55 m/s) in the single support phase (SSP) by minimizing energy expenditure, which is subject to the non-linear constraints of the gait. The unconstrained system dynamics of extended inverse dynamics (USDEID) approach was used to estimate muscle activations. Then by scaling time and applying the same procedure, the movement trajectories were predicted for higher average velocities (from 2.07 m/s to 4.07 m/s) and compared to the pattern of movement with fast walking speed. The comparison indicated a high level of compatibility between the experimental and predicted results, except for the vertical position of the center of gravity (COG). It was concluded that the gait prediction model can be effectively used to predict gait trajectories for higher average velocities.

Optimal Recovery Trajectories for Automatic Ground Collision Avoidance Systems (Auto GCAS)

Science.gov (United States)

Suplisson, Angela W.

The US Air Force recently fielded the F-16 Automatic Ground Collision Avoidance System (Auto GCAS). This system meets the operational requirements of being both aggressive and timely, meaning that extremely agile avoidance maneuvers will be executed at the last second to avoid the ground. This small window of automatic operation maneuvering in close proximity to the ground makes the problem challenging. There currently exists no similar Auto GCAS for manned military 'heavy' aircraft with lower climb performance such as transport, tanker, or bomber aircraft. The F-16 Auto GCAS recovery is a single pre-planned roll to wings-level and 5-g pull-up which is very effective for fighters due to their high g and climb performance, but it is not suitable for military heavy aircraft. This research proposes a new optimal control approach to the ground collision avoidance problem for heavy aircraft by mapping the aggressive and timely requirements of the automatic recovery to the optimal control formulation which includes lateral maneuvers around terrain. This novel mapping creates two ways to pose the optimal control problem for Auto GCAS; one as a Max Distance with a Timely Trigger formulation and the other as a Min Control with an Aggressive Trigger formulation. Further, the optimal path and optimal control admitted by these two formulations are demonstrated to be equivalent at the point the automatic recovery is initiated for the simplified 2-D case. The Min Control formulation was demonstrated to have faster computational speed and was chosen for the 3-D case. Results are presented for representative heavy aircraft scenarios against 3-D digital terrain. The Min Control formulation was then compared to a Multi-Trajectory Auto GCAS with five pre-planned maneuvers. Metrics were developed to quantify the improvement from using an optimal approach versus the pre-planned maneuvers. The proposed optimal Min Control method was demonstrated to require less control or trigger later

Conceptual Design and Optimal Power Control Strategy for AN Eco-Friendly Hybrid Vehicle

Science.gov (United States)

Nasiri, N. Mir; Chieng, Frederick T. A.

2011-06-01

This paper presents a new concept for a hybrid vehicle using a torque and speed splitting technique. It is implemented by the newly developed controller in combination with a two degree of freedom epicyclic gear transmission. This approach enables optimization of the power split between the less powerful electrical motor and more powerful engine while driving a car load. The power split is fundamentally a dual-energy integration mechanism as it is implemented by using the epicyclic gear transmission that has two inputs and one output for a proper power distribution. The developed power split control system manages the operation of both the inputs to have a known output with the condition of maintaining optimum operating efficiency of the internal combustion engine and electrical motor. This system has a huge potential as it is possible to integrate all the features of hybrid vehicle known to-date such as the regenerative braking system, series hybrid, parallel hybrid, series/parallel hybrid, and even complex hybrid (bidirectional). By using the new power split system it is possible to further reduce fuel consumption and increase overall efficiency.

[Optimization and assessment of a reverse hybridization system for the detection of HBV drug-resistant mutations].

Science.gov (United States)

Liu, Yan-chen; Huang, Ai-long; Hu, Yuan; Hu, Jie-li; Lai, Guo-qi; Zhang, Wen-lu

2011-12-01

To establish a detection method for HBV drug-resistant mutations related to lamivudine, adefovir and entecavir by optimization and assessment of reverse hybridization system. 26 degenerated probes covering 10 drug-resistant hotspots of 3 drugs were synthesized and immobilized on the same positively charged nylon membrane. PCR products labeled with digoxigenin were hybridized with corresponding probes. To improve the sensitivity and specificity, 4 reaction steps of reverse hybridization were optimized including the number of labeled digoxigenin, the energy intensity of UV cross-linking, hybridization and stringency wash conditions. To prove the feasibility, the specificity, sensitivity and accuracy of this system were assessed respectively. Sensitive and specific results are obtained by the optimization of the following 4 reaction steps: the primers labeled with 3 digoxigenin, energy intensity of UV cross-linking for 1500 x 0.1 mJ/cm², hybridization at 42 degrees C and stringency wash with 0.5 x SSC and 0.1% SDS solution at 44 degrees C for 30 min. In the assessment of system, the majority of probes have high specificity. The quantity of PCR product with a concentration of 10 ng/μl or above can be detected by this method. The concordant rate between reverse hybridization and direct sequencing is 93.9% in the clinical sample test. Though the specificity of several probes needs to be improved further, it is a simple, rapid and sensitive method which can detect HBV resistant mutations related to lamivudine, adefovir and entecavir simultaneously. Due to the short distance between 180 and 181, likewise 202 and 204, the sequence of the same probe covers two codon positions, and hybridization will be interfered by each other. To avoid such interference, the possible solution is that probes are designed by arranging and combining various forms of two near codons.

Hybrid glowworm swarm optimization for task scheduling in the cloud environment

Science.gov (United States)

Zhou, Jing; Dong, Shoubin

2018-06-01

In recent years many heuristic algorithms have been proposed to solve task scheduling problems in the cloud environment owing to their optimization capability. This article proposes a hybrid glowworm swarm optimization (HGSO) based on glowworm swarm optimization (GSO), which uses a technique of evolutionary computation, a strategy of quantum behaviour based on the principle of neighbourhood, offspring production and random walk, to achieve more efficient scheduling with reasonable scheduling costs. The proposed HGSO reduces the redundant computation and the dependence on the initialization of GSO, accelerates the convergence and more easily escapes from local optima. The conducted experiments and statistical analysis showed that in most cases the proposed HGSO algorithm outperformed previous heuristic algorithms to deal with independent tasks.

Shaping low-thrust trajectories with thrust-handling feature

Science.gov (United States)

Taheri, Ehsan; Kolmanovsky, Ilya; Atkins, Ella

2018-02-01

Shape-based methods are becoming popular in low-thrust trajectory optimization due to their fast computation speeds. In existing shape-based methods constraints are treated at the acceleration level but not at the thrust level. These two constraint types are not equivalent since spacecraft mass decreases over time as fuel is expended. This paper develops a shape-based method based on a Fourier series approximation that is capable of representing trajectories defined in spherical coordinates and that enforces thrust constraints. An objective function can be incorporated to minimize overall mission cost, i.e., achieve minimum ΔV . A representative mission from Earth to Mars is studied. The proposed Fourier series technique is demonstrated capable of generating feasible and near-optimal trajectories. These attributes can facilitate future low-thrust mission designs where different trajectory alternatives must be rapidly constructed and evaluated.

Optimal energy management for a mechanical-hybrid vehicle with cold start conditions

NARCIS (Netherlands)

Berkel, van K.; Klemm, W.P.A.; Hofman, T.; Vroemen, B.G.; Steinbuch, M.

2013-01-01

This paper presents the design of an optimal Energy Management Strategy (EMS) for a hybrid vehicle that starts with a cold powertrain. The cold start negatively affects the combustion and transmission efficiency of the powertrain, caused by the higher frictional losses due to increased hydrodynamic

Lipid-polymer hybrid nanoparticles: Development & statistical optimization of norfloxacin for topical drug delivery system

Directory of Open Access Journals (Sweden)

Vivek Dave

2017-12-01

Full Text Available Poly lactic acid is a biodegradable, biocompatible, and non-toxic polymer, widely used in many pharmaceutical preparations such as controlled release formulations, parenteral preparations, surgical treatment applications, and tissue engineering. In this study, we prepared lipid-polymer hybrid nanoparticles for topical and site targeting delivery of Norfloxacin by emulsification solvent evaporation method (ESE. The design of experiment (DOE was done by using software to optimize the result, and then a surface plot was generated to compare with the practical results. The surface morphology, particle size, zeta potential and composition of the lipid-polymer hybrid nanoparticles were characterized by SEM, TEM, AFM, and FTIR. The thermal behavior of the lipid-polymer hybrid nanoparticles was characterized by DSC and TGA. The prepared lipid-polymer hybrid nanoparticles of Norfloxacin exhibited an average particle size from 178.6 ± 3.7 nm to 220.8 ± 2.3 nm, and showed very narrow distribution with polydispersity index ranging from 0.206 ± 0.36 to 0.383 ± 0.66. The surface charge on the lipid-polymer hybrid nanoparticles were confirmed by zeta potential, showed the value from +23.4 ± 1.5 mV to +41.5 ± 3.4 mV. An Antimicrobial study was done against Staphylococcus aureus and Pseudomonas aeruginosa, and the lipid-polymer hybrid nanoparticles showed potential activity against these two. Lipid-polymer hybrid nanoparticles of Norfloxacin showed the %cumulative drug release of 89.72% in 24 h. A stability study of the optimized formulation showed the suitable condition for the storage of lipid-polymer hybrid nanoparticles was at 4 ± 2 °C/60 ± 5% RH. These results illustrated high potential of lipid-polymer hybrid nanoparticles Norfloxacin for usage as a topical antibiotic drug carriers.

Artificial root foraging optimizer algorithm with hybrid strategies

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Yang Liu

2017-02-01

Full Text Available In this work, a new plant-inspired optimization algorithm namely the hybrid artificial root foraging optimizion (HARFO is proposed, which mimics the iterative root foraging behaviors for complex optimization. In HARFO model, two innovative strategies were developed: one is the root-to-root communication strategy, which enables the individual exchange information with each other in different efficient topologies that can essentially improve the exploration ability; the other is co-evolution strategy, which can structure the hierarchical spatial population driven by evolutionary pressure of multiple sub-populations that ensure the diversity of root population to be well maintained. The proposed algorithm is benchmarked against four classical evolutionary algorithms on well-designed test function suites including both classical and composition test functions. Through the rigorous performance analysis that of all these tests highlight the significant performance improvement, and the comparative results show the superiority of the proposed algorithm.

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

Science.gov (United States)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

Science.gov (United States)

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

Targeted 2D/3D registration using ray normalization and a hybrid optimizer

International Nuclear Information System (INIS)

Dey, Joyoni; Napel, Sandy

2006-01-01

X-ray images are often used to guide minimally invasive procedures in interventional radiology. The use of a preoperatively obtained 3D volume can enhance the visualization needed for guiding catheters and other surgical devices. However, for intraoperative usefulness, the 3D dataset needs to be registered to the 2D x-ray images of the patient. We investigated the effect of targeting subvolumes of interest in the 3D datasets and registering the projections with C-arm x-ray images. We developed an intensity-based 2D/3D rigid-body registration using a Monte Carlo-based hybrid algorithm as the optimizer, using a single view for registration. Pattern intensity (PI) and mutual information (MI) were two metrics tested. We used normalization of the rays to address the problems due to truncation in 3D necessary for targeting. We tested the algorithm on a C-arm x-ray image of a pig's head and a 3D dataset reconstructed from multiple views of the C-arm. PI and MI were comparable in performance. For two subvolumes starting with a set of initial poses from +/-15 mm in x, from +/-3 mm (random), in y and z and +/-4 deg in the three angles, the robustness was 94% for PI and 91% for MI, with accuracy of 2.4 mm (PI) and 2.6 mm (MI), using the hybrid algorithm. The hybrid optimizer, when compared with a standard Powell's direction set method, increased the robustness from 59% (Powell) to 94% (hybrid). Another set of 50 random initial conditions from [+/-20] mm in x,y,z and [+/-10] deg in the three angles, yielded robustness of 84% (hybrid) versus 38% (Powell) using PI as metric, with accuracies 2.1 mm (hybrid) versus 2.0 mm (Powell)

Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block.

Science.gov (United States)

Kora, Padmavathi; Kalva, Sri Ramakrishna

2015-01-01

Abnormal cardiac beat identification is a key process in the detection of heart diseases. Our present study describes a procedure for the detection of left and right bundle branch block (LBBB and RBBB) Electrocardiogram (ECG) patterns. The electrical impulses that control the cardiac beat face difficulty in moving inside the heart. This problem is termed as bundle branch block (BBB). BBB makes it harder for the heart to pump blood effectively through the heart circulatory system. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining two heuristic optimization methods: Bacterial Forging Optimization (BFO) and Particle Swarm Optimization (PSO) for the feature selection of ECG signals. One of the major controlling forces of BFO algorithm is the chemotactic movement of a bacterium that models a test solution. The chemotaxis process of the BFO depends on random search directions which may lead to a delay in achieving the global optimum solution. The hybrid technique: Bacterial Forging-Particle Swarm Optimization (BFPSO) incorporates the concepts from BFO and PSO and it creates individuals in a new generation. This BFPSO method performs local search through the chemotactic movement of BFO and the global search over the entire search domain is accomplished by a PSO operator. The BFPSO feature values are given as the input for the Levenberg-Marquardt Neural Network classifier.

Analysis and optimization of hybrid electric vehicle thermal management systems

Science.gov (United States)

Hamut, H. S.; Dincer, I.; Naterer, G. F.

2014-02-01

In this study, the thermal management system of a hybrid electric vehicle is optimized using single and multi-objective evolutionary algorithms in order to maximize the exergy efficiency and minimize the cost and environmental impact of the system. The objective functions are defined and decision variables, along with their respective system constraints, are selected for the analysis. In the multi-objective optimization, a Pareto frontier is obtained and a single desirable optimal solution is selected based on LINMAP decision-making process. The corresponding solutions are compared against the exergetic, exergoeconomic and exergoenvironmental single objective optimization results. The results show that the exergy efficiency, total cost rate and environmental impact rate for the baseline system are determined to be 0.29, ¢28 h-1 and 77.3 mPts h-1 respectively. Moreover, based on the exergoeconomic optimization, 14% higher exergy efficiency and 5% lower cost can be achieved, compared to baseline parameters at an expense of a 14% increase in the environmental impact. Based on the exergoenvironmental optimization, a 13% higher exergy efficiency and 5% lower environmental impact can be achieved at the expense of a 27% increase in the total cost.

Switching and optimizing control for coal flotation process based on a hybrid model

Science.gov (United States)

Dong, Zhiyong; Wang, Ranfeng; Fan, Minqiang; Fu, Xiang

2017-01-01

Flotation is an important part of coal preparation, and the flotation column is widely applied as efficient flotation equipment. This process is complex and affected by many factors, with the froth depth and reagent dosage being two of the most important and frequently manipulated variables. This paper proposes a new method of switching and optimizing control for the coal flotation process. A hybrid model is built and evaluated using industrial data. First, wavelet analysis and principal component analysis (PCA) are applied for signal pre-processing. Second, a control model for optimizing the set point of the froth depth is constructed based on fuzzy control, and a control model is designed to optimize the reagent dosages based on expert system. Finally, the least squares-support vector machine (LS-SVM) is used to identify the operating conditions of the flotation process and to select one of the two models (froth depth or reagent dosage) for subsequent operation according to the condition parameters. The hybrid model is developed and evaluated on an industrial coal flotation column and exhibits satisfactory performance. PMID:29040305

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...

Lunar Cube Transfer Trajectory Options

Science.gov (United States)

Folta, David; Dichmann, Donald James; Clark, Pamela E.; Haapala, Amanda; Howell, Kathleen

2015-01-01

Numerous Earth-Moon trajectory and lunar orbit options are available for Cubesat missions. Given the limited Cubesat injection infrastructure, transfer trajectories are contingent upon the modification of an initial condition of the injected or deployed orbit. Additionally, these transfers can be restricted by the selection or designs of Cubesat subsystems such as propulsion or communication. Nonetheless, many trajectory options can b e considered which have a wide range of transfer duration, fuel requirements, and final destinations. Our investigation of potential trajectories highlights several options including deployment from low Earth orbit (LEO) geostationary transfer orbits (GTO) and higher energy direct lunar transfer and the use of longer duration Earth-Moon dynamical systems. For missions with an intended lunar orbit, much of the design process is spent optimizing a ballistic capture while other science locations such as Sun-Earth libration or heliocentric orbits may simply require a reduced Delta-V imparted at a convenient location along the trajectory.

Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms

Science.gov (United States)

Lopez, Nicolas

This dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a single objective optimization version of the problem are presented, in order to minimize cost and to minimize global warming potential (GWP) followed by a multi-objective implementation of the offered methodology, by utilizing a non-sorting Genetic Algorithm embedded with a monte Carlo Simulation. The method is validated by solving a small instance of the problem with known solution via a full enumeration algorithm developed by NREL in their software HOMER. The dissertation concludes that the evolutionary algorithms embedded with Monte Carlo simulation namely modified Genetic Algorithms are an efficient form of solving the problem, by finding approximate solutions in the case of single objective optimization, and by approximating the true Pareto front in the case of multiple objective optimization of the Renewable Energy Integration Problem.

Efficient Trajectory Options Allocation for the Collaborative Trajectory Options Program

Science.gov (United States)

Rodionova, Olga; Arneson, Heather; Sridhar, Banavar; Evans, Antony

2017-01-01

The Collaborative Trajectory Options Program (CTOP) is a Traffic Management Initiative (TMI) intended to control the air traffic flow rates at multiple specified Flow Constrained Areas (FCAs), where demand exceeds capacity. CTOP allows flight operators to submit the desired Trajectory Options Set (TOS) for each affected flight with associated Relative Trajectory Cost (RTC) for each option. CTOP then creates a feasible schedule that complies with capacity constraints by assigning affected flights with routes and departure delays in such a way as to minimize the total cost while maintaining equity across flight operators. The current version of CTOP implements a Ration-by-Schedule (RBS) scheme, which assigns the best available options to flights based on a First-Scheduled-First-Served heuristic. In the present study, an alternative flight scheduling approach is developed based on linear optimization. Results suggest that such an approach can significantly reduce flight delays, in the deterministic case, while maintaining equity as defined using a Max-Min fairness scheme.

Optimization of the core configuration design using a hybrid artificial intelligence algorithm for research reactors

International Nuclear Information System (INIS)

Hedayat, Afshin; Davilu, Hadi; Barfrosh, Ahmad Abdollahzadeh; Sepanloo, Kamran

2009-01-01

To successfully carry out material irradiation experiments and radioisotope productions, a high thermal neutron flux at irradiation box over a desired life time of a core configuration is needed. On the other hand, reactor safety and operational constraints must be preserved during core configuration selection. Two main objectives and two safety and operational constraints are suggested to optimize reactor core configuration design. Suggested parameters and conditions are considered as two separate fitness functions composed of two main objectives and two penalty functions. This is a constrained and combinatorial type of a multi-objective optimization problem. In this paper, a fast and effective hybrid artificial intelligence algorithm is introduced and developed to reach a Pareto optimal set. The hybrid algorithm is composed of a fast and elitist multi-objective genetic algorithm (GA) and a fast fitness function evaluating system based on the cascade feed forward artificial neural networks (ANNs). A specific GA representation of core configuration and also special GA operators are introduced and used to overcome the combinatorial constraints of this optimization problem. A software package (Core Pattern Calculator 1) is developed to prepare and reform required data for ANNs training and also to revise the optimization results. Some practical test parameters and conditions are suggested to adjust main parameters of the hybrid algorithm. Results show that introduced ANNs can be trained and estimate selected core parameters of a research reactor very quickly. It improves effectively optimization process. Final optimization results show that a uniform and dense diversity of Pareto fronts are gained over a wide range of fitness function values. To take a more careful selection of Pareto optimal solutions, a revision system is introduced and used. The revision of gained Pareto optimal set is performed by using developed software package. Also some secondary operational

Optimization of the core configuration design using a hybrid artificial intelligence algorithm for research reactors

Energy Technology Data Exchange (ETDEWEB)

Hedayat, Afshin, E-mail: ahedayat@aut.ac.i [Department of Nuclear Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, P.O. Box 15875-4413, Tehran (Iran, Islamic Republic of); Reactor Research and Development School, Nuclear Science and Technology Research Institute (NSTRI), End of North Karegar Street, P.O. Box 14395-836, Tehran (Iran, Islamic Republic of); Davilu, Hadi [Department of Nuclear Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, P.O. Box 15875-4413, Tehran (Iran, Islamic Republic of); Barfrosh, Ahmad Abdollahzadeh [Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, P.O. Box 15875-4413, Tehran (Iran, Islamic Republic of); Sepanloo, Kamran [Reactor Research and Development School, Nuclear Science and Technology Research Institute (NSTRI), End of North Karegar Street, P.O. Box 14395-836, Tehran (Iran, Islamic Republic of)

2009-12-15

To successfully carry out material irradiation experiments and radioisotope productions, a high thermal neutron flux at irradiation box over a desired life time of a core configuration is needed. On the other hand, reactor safety and operational constraints must be preserved during core configuration selection. Two main objectives and two safety and operational constraints are suggested to optimize reactor core configuration design. Suggested parameters and conditions are considered as two separate fitness functions composed of two main objectives and two penalty functions. This is a constrained and combinatorial type of a multi-objective optimization problem. In this paper, a fast and effective hybrid artificial intelligence algorithm is introduced and developed to reach a Pareto optimal set. The hybrid algorithm is composed of a fast and elitist multi-objective genetic algorithm (GA) and a fast fitness function evaluating system based on the cascade feed forward artificial neural networks (ANNs). A specific GA representation of core configuration and also special GA operators are introduced and used to overcome the combinatorial constraints of this optimization problem. A software package (Core Pattern Calculator 1) is developed to prepare and reform required data for ANNs training and also to revise the optimization results. Some practical test parameters and conditions are suggested to adjust main parameters of the hybrid algorithm. Results show that introduced ANNs can be trained and estimate selected core parameters of a research reactor very quickly. It improves effectively optimization process. Final optimization results show that a uniform and dense diversity of Pareto fronts are gained over a wide range of fitness function values. To take a more careful selection of Pareto optimal solutions, a revision system is introduced and used. The revision of gained Pareto optimal set is performed by using developed software package. Also some secondary operational

Application of Hybrid Meta-Heuristic Techniques for Optimal Load Shedding Planning and Operation in an Islanded Distribution Network Integrated with Distributed Generation

Directory of Open Access Journals (Sweden)

Jafar Jallad

2018-05-01

Full Text Available In a radial distribution network integrated with distributed generation (DG, frequency and voltage instability could occur due to grid disconnection, which would result in an islanded network. This paper proposes an optimal load shedding scheme to balance the electricity demand and the generated power of DGs. The integration of the Firefly Algorithm and Particle Swarm Optimization (FAPSO is proposed for the application of the planned load shedding and under frequency load shedding (UFLS scheme. In planning mode, the hybrid optimization maximizes the amount of load remaining and improves the voltage profile of load buses within allowable limits. Moreover, the hybrid optimization can be used in UFLS scheme to identify the optimal combination of loads that need to be shed from a network in operation mode. In order to assess the capabilities of the hybrid optimization, the IEEE 33-bus radial distribution system and part of the Malaysian distribution network with different types of DGs were used. The response of the proposed optimization method in planning and operation were compared with other optimization techniques. The simulation results confirmed the effectiveness of the proposed hybrid optimization in planning mode and demonstrated that the proposed UFLS scheme is quick enough to restore the system frequency without overshooting in less execution time.

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

Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

Directory of Open Access Journals (Sweden)

Wen-Yeau Chang

2013-09-01

Full Text Available High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

Robust Trajectory Optimization of a Ski Jumper for Uncertainty Influence and Safety Quantification

Directory of Open Access Journals (Sweden)

Patrick Piprek

2018-02-01

Full Text Available This paper deals with the development of a robust optimal control framework for a previously developed multi-body ski jumper simulation model by the authors. This framework is used to model uncertainties acting on the jumper during his jump, e.g., wind or mass, to enhance the performance, but also to increase the fairness and safety of the competition. For the uncertainty modeling the method of generalized polynomial chaos together with the discrete expansion by stochastic collocation is applied: This methodology offers a very flexible framework to model multiple uncertainties using a small number of required optimizations to calculate an uncertain trajectory. The results are then compared to the results of the Latin-Hypercube sampling method to show the correctness of the applied methods. Finally, the results are examined with respect to two major metrics: First, the influence of the uncertainties on the jumper, his positioning with respect to the air, and his maximal achievable flight distance are examined. Then, the results are used in a further step to quantify the safety of the jumper.

Optimal operation of hybrid-SITs under a SBO accident

International Nuclear Information System (INIS)

Jeon, In Seop; Heo, Sun; Kang, Hyun Gook

2016-01-01

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.

MGA trajectory planning with an ACO-inspired algorithm

Science.gov (United States)

Ceriotti, Matteo; Vasile, Massimiliano

2010-11-01

Given a set of celestial bodies, the problem of finding an optimal sequence of swing-bys, deep space manoeuvres (DSM) and transfer arcs connecting the elements of the set is combinatorial in nature. The number of possible paths grows exponentially with the number of celestial bodies. Therefore, the design of an optimal multiple gravity assist (MGA) trajectory is a NP-hard mixed combinatorial-continuous problem. Its automated solution would greatly improve the design of future space missions, allowing the assessment of a large number of alternative mission options in a short time. This work proposes to formulate the complete automated design of a multiple gravity assist trajectory as an autonomous planning and scheduling problem. The resulting scheduled plan will provide the optimal planetary sequence and a good estimation of the set of associated optimal trajectories. The trajectory model consists of a sequence of celestial bodies connected by two-dimensional transfer arcs containing one DSM. For each transfer arc, the position of the planet and the spacecraft, at the time of arrival, are matched by varying the pericentre of the preceding swing-by, or the magnitude of the launch excess velocity, for the first arc. For each departure date, this model generates a full tree of possible transfers from the departure to the destination planet. Each leaf of the tree represents a planetary encounter and a possible way to reach that planet. An algorithm inspired by ant colony optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination adding one node at the time: every time an ant is at a node, a probability function is used to select a feasible direction. This approach to automatic trajectory planning is applied to the design of optimal transfers to Saturn and among the Galilean moons of Jupiter. Solutions are compared to those found through more traditional genetic-algorithm techniques.

Hybrid inflation along waterfall trajectories

International Nuclear Information System (INIS)

Clesse, Sebastien

2011-01-01

We identify a new inflationary regime for which more than 60 e-folds are generated classically during the waterfall phase occurring after the usual hybrid inflation. By performing a Bayesian Monte-Carlo-Markov-Chain analysis, this scenario is shown to take place in a large part of the parameter space of the model. When this occurs, the observable perturbation modes leave the Hubble radius during waterfall inflation. The power spectrum of adiabatic perturbations is red, possibly in agreement with CMB constraints. Particular attention has been given to study only the regions for which quantum backreactions do not affect the classical dynamics. Implications concerning the preheating and the absence of topological defects in our Universe are discussed.

Optimization of CHA-PCFC Hybrid Material for the Removal of Radioactive Cs from Waste Seawater

Energy Technology Data Exchange (ETDEWEB)

Lee, Keun-Young; Kim, Jimin; Park, Minsung; Kim, Kwang-Wook; Lee, Eil-Hee; Chung, Dong-Yong; Moon, Jei-Kwon [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2015-05-15

The liquid waste treatment processes in the normal operation of nuclear power plant are commercialized, those in the abnormal accidents have not been fully developed until now. In the present study, as a preliminary research for the development of precipitation-based treatment process specialized for the removal of Cs from waste seawater generated in the emergency case, the performance test of a hybrid material combining chabazite and potassium cobalt ferrocyanide was conducted. Also the synthesis method for the hybrid adsorbent was optimized for the best Cs removal efficiency on the actual contamination level of waste seawater. Because the temperature effect on the synthesis of PCFC was confirmed by preliminary experiments, the optimization of CHA-PCFC synthesis was also conducted. The hybrid material synthesized at 40 .deg. C showed the highest distribution coefficient of Cs in the same manner of the performance of PCFC synthesized at the lower temperature than that of conventional methods.

A Novel Design and Optimization Software for Autonomous PV/Wind/Battery Hybrid Power Systems

Directory of Open Access Journals (Sweden)

Ali M. Eltamaly

2014-01-01

Full Text Available This paper introduces a design and optimization computer simulation program for autonomous hybrid PV/wind/battery energy system. The main function of the new proposed computer program is to determine the optimum size of each component of the hybrid energy system for the lowest price of kWh generated and the best loss of load probability at highest reliability. This computer program uses the hourly wind speed, hourly radiation, and hourly load power with several numbers of wind turbine (WT and PV module types. The proposed computer program changes the penetration ratio of wind/PV with certain increments and calculates the required size of all components and the optimum battery size to get the predefined lowest acceptable probability. This computer program has been designed in flexible fashion that is not available in market available software like HOMER and RETScreen. Actual data for Saudi sites have been used with this computer program. The data obtained have been compared with these market available software. The comparison shows the superiority of this computer program in the optimal design of the autonomous PV/wind/battery hybrid system. The proposed computer program performed the optimal design steps in very short time and with accurate results. Many valuable results can be extracted from this computer program that can help researchers and decision makers.

Hybrid K-means Dan Particle Swarm Optimization Untuk Clustering Nasabah Kredit

Directory of Open Access Journals (Sweden)

Yusuf Priyo Anggodo

2017-05-01

Credit is the biggest revenue for the bank. However, banks have to be selective in deciding which clients can receive the credit. This issue is becoming increasingly complex because when the bank was wrong to give credit to customers can do harm, apart of that a large number of deciding parameter in determining customer credit. Clustering is one way to be able to resolve this issue. K-means is a simple and popular method for solving clustering. However, K-means pure can’t provide optimum solutions so that needs to be done to get the optimum solution to improve. One method of optimization that can solve the problems of optimization with particle swarm optimization is good (PSO. PSO is very helpful in the process of clustering to perform optimization on the central point of each cluster. To improve better results on PSO there are some that do improve. The first use of time-variant inertia to make the dynamic value of inertial w each iteration. Both control the speed of the particle velocity or clamping to get the best position. Besides to overcome premature convergence do hybrid PSO with random injection. The results of this research provide the optimum results for solving clustering of customer credits. The test results showed the hybrid PSO K-means provide the greatest results than K-means and PSO K-means, where the silhouette of the K-means, PSO K-means, and hybrid PSO K-means respectively 0.57343, 0.792045, 1. Keywords: Credit, Clustering, PSO, K-means, Random Injection

Simulation-optimization framework for multi-site multi-season hybrid stochastic streamflow modeling

Science.gov (United States)

Srivastav, Roshan; Srinivasan, K.; Sudheer, K. P.

2016-11-01

A simulation-optimization (S-O) framework is developed for the hybrid stochastic modeling of multi-site multi-season streamflows. The multi-objective optimization model formulated is the driver and the multi-site, multi-season hybrid matched block bootstrap model (MHMABB) is the simulation engine within this framework. The multi-site multi-season simulation model is the extension of the existing single-site multi-season simulation model. A robust and efficient evolutionary search based technique, namely, non-dominated sorting based genetic algorithm (NSGA - II) is employed as the solution technique for the multi-objective optimization within the S-O framework. The objective functions employed are related to the preservation of the multi-site critical deficit run sum and the constraints introduced are concerned with the hybrid model parameter space, and the preservation of certain statistics (such as inter-annual dependence and/or skewness of aggregated annual flows). The efficacy of the proposed S-O framework is brought out through a case example from the Colorado River basin. The proposed multi-site multi-season model AMHMABB (whose parameters are obtained from the proposed S-O framework) preserves the temporal as well as the spatial statistics of the historical flows. Also, the other multi-site deficit run characteristics namely, the number of runs, the maximum run length, the mean run sum and the mean run length are well preserved by the AMHMABB model. Overall, the proposed AMHMABB model is able to show better streamflow modeling performance when compared with the simulation based SMHMABB model, plausibly due to the significant role played by: (i) the objective functions related to the preservation of multi-site critical deficit run sum; (ii) the huge hybrid model parameter space available for the evolutionary search and (iii) the constraint on the preservation of the inter-annual dependence. Split-sample validation results indicate that the AMHMABB model is

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

Science.gov (United States)

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses

International Nuclear Information System (INIS)

Li, Liang; You, Sixiong; Yang, Chao; Yan, Bingjie; Song, Jian; Chen, Zheng

2016-01-01

Highlights: • The novel approximated global optimal energy management strategy has been proposed for hybrid powertrains. • Eight typical driving behaviors have been classified with K-means to deal with the multiplicative traffic conditions. • The stochastic driver models of different driving behaviors were established based on the Markov chains. • ECMS was used to modify the SMPC-based energy management strategy to improve its fuel economy. • The approximated global optimal energy management strategy for plug-in hybrid electric buses has been verified and analyzed. - Abstract: Driving cycles of a city bus is statistically characterized by some repetitive features, which makes the predictive energy management strategy very desirable to obtain approximate optimal fuel economy of a plug-in hybrid electric bus. But dealing with the complicated traffic conditions and finding an approximated global optimal strategy which is applicable to the plug-in hybrid electric bus still remains a challenging technique. To solve this problem, a novel driving-behavior-aware modified stochastic model predictive control method is proposed for the plug-in hybrid electric bus. Firstly, the K-means is employed to classify driving behaviors, and the driver models based on Markov chains is obtained under different kinds of driving behaviors. While the obtained driver behaviors are regarded as stochastic disturbance inputs, the local minimum fuel consumption might be obtained with a traditional stochastic model predictive control at each step, taking tracking the reference battery state of charge trajectory into consideration in the finite predictive horizons. However, this technique is still accompanied by some working points with reduced/worsened fuel economy. Thus, the stochastic model predictive control is modified with the equivalent consumption minimization strategy to eliminate these undesirable working points. The results in real-world city bus routines show that the

A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-08-25

This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.

Optimal Photovoltaic System Sizing of a Hybrid Diesel/PV System

Directory of Open Access Journals (Sweden)

Ahmed Belhamadia

2017-03-01

Full Text Available This paper presents a cost analysis study of a hybrid diesel and Photovoltaic (PV system in Kuala Terengganu, Malaysia. It first presents the climate conditions of the city followed by the load profile of a 2MVA network; the system was evaluated as a standalone system. Diesel generator rating was considered such that it follows ISO 8528. The maximum size of the PV system was selected such that its penetration would not exceed 25%. Several sizes were considered but the 400kWp system was found to be the most cost efficient. Cost estimation was done using Hybrid Optimization Model for Electric Renewable (HOMER. Based on the simulation results, the climate conditions and the NEC 960, the numbers of the maximum and minimum series modules were suggested as well as the maximum number of the parallel strings.

An optimal control-based algorithm for hybrid electric vehicle using preview route information

NARCIS (Netherlands)

Ngo, D.V.; Hofman, T.; Steinbuch, M.; Serrarens, A.F.A.

2010-01-01

Control strategies for Hybrid Electric Vehicles (HEVs) are generally aimed at optimally choosing the power distribution between the internal combustion engine and the electric motor in order to minimize the fuel consumption and/or emissions. Using vehicle navigation systems in combination with

Optimizing hybrid spreading in metapopulations.

Science.gov (United States)

Zhang, Changwang; Zhou, Shi; Miller, Joel C; Cox, Ingemar J; Chain, Benjamin M

2015-04-29

Epidemic spreading phenomena are ubiquitous in nature and society. Examples include the spreading of diseases, information, and computer viruses. Epidemics can spread by local spreading, where infected nodes can only infect a limited set of direct target nodes and 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. We show the existence of critically hybrid epidemics where neither spreading mechanism alone can cause a noticeable spread but a combination of the two spreading mechanisms would produce an enormous outbreak. Our results provide new strategies for maximising beneficial epidemics and estimating the worst outcome of damaging hybrid epidemics.

Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks

Science.gov (United States)

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. PMID:25734182

Hybrid discrete PSO and OPF approach for optimization of biomass fueled micro-scale energy system

International Nuclear Information System (INIS)

Gómez-González, M.; López, A.; Jurado, F.

2013-01-01

Highlights: ► Method to determine the optimal location and size of biomass power plants. ► The proposed approach is a hybrid of PSO algorithm and optimal power flow. ► Comparison among the proposed algorithm and other methods. ► Computational costs are enough lower than that required for exhaustive search. - Abstract: This paper addresses generation of electricity in the specific aspect of finding the best location and sizing of biomass fueled gas micro-turbine power plants, taking into account the variables involved in the problem, such as the local distribution of biomass resources, biomass transportation and extraction costs, operation and maintenance costs, power losses costs, network operation costs, and technical constraints. In this paper a hybrid method is introduced employing discrete particle swarm optimization and optimal power flow. The approach can be applied to search the best sites and capacities to connect biomass fueled gas micro-turbine power systems in a distribution network among a large number of potential combinations and considering the technical constraints of the network. A fair comparison among the proposed algorithm and other methods is performed.

Optimization of Wind-Marine Hybrid Power System Configuration Based on Genetic Algorithm

Institute of Scientific and Technical Information of China (English)

SHI Hongda; LI Linna; ZHAO Chenyu

2017-01-01

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.

Hybrid Robust Optimization for the Design of a Smartphone Metal Frame Antenna

Directory of Open Access Journals (Sweden)

Sungwoo Lee

2018-01-01

Full Text Available Hybrid robust optimization that combines a genetical swarm optimization (GSO scheme with an orthogonal array (OA is proposed to design an antenna robust to the tolerances arising during the fabrication process of the antenna in this paper. An inverted-F antenna with a metal frame serves as an example to explain the procedure of the proposed method. GSO is adapted to determine the design variables of the antenna, which operates on the GSM850 band (824–894 MHz. The robustness of the antenna is evaluated through a noise test using the OA. The robustness of the optimized antenna is improved by approximately 61.3% relative to that of a conventional antenna. Conventional and optimized antennas are fabricated and measured to validate the experimental results.

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

Science.gov (United States)

Chiadamrong, N.; Piyathanavong, V.

2017-12-01

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.

Hybrid Control Design for a Wheeled Mobile Robot

DEFF Research Database (Denmark)

Bak, Thomas; Bendtsen, Jan Dimon; Ravn, Anders Peter

We present a hybrid systems solution to the problem of trajectory tracking for a four-wheel steered four-wheel driven mobile robot. The robot is modelled as a non-holonomic dynamic system subject to pure rolling, no-slip constraints. Under normal driving conditions, a nonlinear trajectory tracking...

Hybrid Control Design for a Wheeled Mobile Robot

DEFF Research Database (Denmark)

Bak, Thomas; Bendtsen, Jan Dimon; Ravn, Anders Peter

2003-01-01

We present a hybrid systems solution to the problem of trajectory tracking for a four-wheel steered four-wheel driven mobile robot. The robot is modelled as a non-holonomic dynamic system subject to pure rolling, no-slip constraints. Under normal driving conditions, a nonlinear trajectory tracking...

Hybrid Control Design for a Wheeled Mobile Robot

DEFF Research Database (Denmark)

Bak, Thomas; Bendtsen, Jan Dimon; Ravn, Anders Peter

2003-01-01

We present a hybrid systems solution to the problem of trajectory tracking for a four-wheel steered four-wheel driven mobile robot. The robot is modelled as a non-holonomic dynamic system subject to pure rolling, no-slip constraints. Under normal driving conditions, a nonlinear trajectory trackin...

Multi-objective optimization in the presence of practical constraints using non-dominated sorting hybrid cuckoo search algorithm

Directory of Open Access Journals (Sweden)

M. Balasubbareddy

2015-12-01

Full Text Available A novel optimization algorithm is proposed to solve single and multi-objective optimization problems with generation fuel cost, emission, and total power losses as objectives. The proposed method is a hybridization of the conventional cuckoo search algorithm and arithmetic crossover operations. Thus, the non-linear, non-convex objective function can be solved under practical constraints. The effectiveness of the proposed algorithm is analyzed for various cases to illustrate the effect of practical constraints on the objectives' optimization. Two and three objective multi-objective optimization problems are formulated and solved using the proposed non-dominated sorting-based hybrid cuckoo search algorithm. The effectiveness of the proposed method in confining the Pareto front solutions in the solution region is analyzed. The results for single and multi-objective optimization problems are physically interpreted on standard test functions as well as the IEEE-30 bus test system with supporting numerical and graphical results and also validated against existing methods.

Wave propagation near the lower hybrid resonance in toroidal plasmas

International Nuclear Information System (INIS)

Ohkubo, K.; Ohasa, K.; Matsuura, K.

1975-10-01

Dielectric tensor and equipotential curves (ray trajectories) of an electrostatic wave near the lower hybrid resonance are investigated for the toroidal plasma with a shear magnetic field. The ray trajectories start from the vicinity of the plasma surface, and rotate in a spiral form around the magnetic axis, and then reach the lower or upper parts of lower hybrid resonance layer. The numerical computations are performed on the parameters of JIPP T-II device with two dimensional inhomogeneity. (auth.)

First principles molecular dynamics without self-consistent field optimization

International Nuclear Information System (INIS)

Souvatzis, Petros; Niklasson, Anders M. N.

2014-01-01

We present a first principles molecular dynamics approach that is based on time-reversible extended Lagrangian Born-Oppenheimer molecular dynamics [A. M. N. Niklasson, Phys. Rev. Lett. 100, 123004 (2008)] in the limit of vanishing self-consistent field optimization. The optimization-free dynamics keeps the computational cost to a minimum and typically provides molecular trajectories that closely follow the exact Born-Oppenheimer potential energy surface. Only one single diagonalization and Hamiltonian (or Fockian) construction are required in each integration time step. The proposed dynamics is derived for a general free-energy potential surface valid at finite electronic temperatures within hybrid density functional theory. Even in the event of irregular functional behavior that may cause a dynamical instability, the optimization-free limit represents a natural starting guess for force calculations that may require a more elaborate iterative electronic ground state optimization. Our optimization-free dynamics thus represents a flexible theoretical framework for a broad and general class of ab initio molecular dynamics simulations

Exergy analysis and optimization of a biomass gasification, solid oxide fuel cell and micro gas turbine hybrid system

DEFF Research Database (Denmark)

Bang-Møller, Christian; Rokni, Masoud; Elmegaard, Brian

2011-01-01

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...

Optimal Power Flow Modelling and Analysis of Hybrid AC-DC Grids with Offshore Wind Power Plant

DEFF Research Database (Denmark)

Dhua, Debasish; Huang, Shaojun; Wu, Qiuwei

2017-01-01

In order to develop renewables based energy systems, the installation of the offshore wind power plants (WPPs) is globally encouraged. However, wind power generation is intermittent and uncertain. An accurate modelling and evaluation reduces investment and provide better operation. Hence......, the wind power production level also plays a major role in a hybrid system on transmission loss evaluation. The developed model is tested in Low, Medium and High wind power production levels to determine the objective function of the OPF solution. MATLAB Optimization Toolbox and MATLAB script are used......, it is essential to develop a suitable model and apply optimization algorithms for different application scenarios. The objective of this work is to develop a generalized model and evaluate the Optimal Power Flow (OPF) solutions in a hybrid AC/DC system including HVDC (LCC based) and offshore WPP (VSC based...

Powertrain Matching and Optimization of Dual-Motor Hybrid Driving System for Electric Vehicle Based on Quantum Genetic Intelligent Algorithm

Directory of Open Access Journals (Sweden)

Yong Wang

2014-01-01

Full Text Available In order to increase the driving range and improve the overall performance of all-electric vehicles, a new dual-motor hybrid driving system with two power sources was proposed. This system achieved torque-speed coupling between the two power sources and greatly improved the high performance working range of the motors; at the same time, continuously variable transmission (CVT was achieved to efficiently increase the driving range. The power system parameters were determined using the “global optimization method”; thus, the vehicle’s dynamics and economy were used as the optimization indexes. Based on preliminary matches, quantum genetic algorithm was introduced to optimize the matching in the dual-motor hybrid power system. Backward simulation was performed on the combined simulation platform of Matlab/Simulink and AVL-Cruise to optimize, simulate, and verify the system parameters of the transmission system. Results showed that quantum genetic algorithms exhibited good global optimization capability and convergence in dealing with multiobjective and multiparameter optimization. The dual-motor hybrid-driving system for electric cars satisfied the dynamic performance and economy requirements of design, efficiently increasing the driving range of the car, having high performance, and reducing energy consumption of 15.6% compared with the conventional electric vehicle with single-speed reducers.

A globally nonsingular quaternion-based formulation for all-electric satellite trajectory optimization

Science.gov (United States)

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 novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

Science.gov (United States)

Wang, Aimeng; Guo, Jiayu

2017-12-01

A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

Global Optimization of Low-Thrust Interplanetary Trajectories Subject to Operational Constraints

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Englander, Jacob Aldo; Vavrina, Matthew; Hinckley, David

2016-01-01

Low-thrust electric propulsion provides many advantages for mission to difficult targets-Comets and asteroids-Mercury-Outer planets (with sufficient power supply)Low-thrust electric propulsion is characterized by high power requirements but also very high specific impulse (Isp), leading to very good mass fractions. Low-thrust trajectory design is a very different process from chemical trajectory.

Dynamic Modeling and Control Strategy Optimization for a Hybrid Electric Tracked Vehicle

Directory of Open Access Journals (Sweden)

Hong Wang

2015-01-01

Full Text Available A new hybrid electric tracked bulldozer composed of an engine generator, two driving motors, and an ultracapacitor is put forward, which can provide high efficiencies and less fuel consumption comparing with traditional ones. This paper first presents the terramechanics of this hybrid electric tracked bulldozer. The driving dynamics for this tracked bulldozer is then analyzed. After that, based on analyzing the working characteristics of the engine, generator, and driving motors, the power train system model and control strategy optimization is established by using MATLAB/Simulink and OPTIMUS software. Simulation is performed under a representative working condition, and the results demonstrate that fuel economy of the HETV can be significantly improved.

A Hybrid Interval-Robust Optimization Model for Water Quality Management.

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Xu, Jieyu; Li, Yongping; Huang, Guohe

2013-05-01

In water quality management problems, uncertainties may exist in many system components and pollution-related processes ( i.e. , random nature of hydrodynamic conditions, variability in physicochemical processes, dynamic interactions between pollutant loading and receiving water bodies, and indeterminacy of available water and treated wastewater). These complexities lead to difficulties in formulating and solving the resulting nonlinear optimization problems. In this study, a hybrid interval-robust optimization (HIRO) method was developed through coupling stochastic robust optimization and interval linear programming. HIRO can effectively reflect the complex system features under uncertainty, where implications of water quality/quantity restrictions for achieving regional economic development objectives are studied. By delimiting the uncertain decision space through dimensional enlargement of the original chemical oxygen demand (COD) discharge constraints, HIRO enhances the robustness of the optimization processes and resulting solutions. This method was applied to planning of industry development in association with river-water pollution concern in New Binhai District of Tianjin, China. Results demonstrated that the proposed optimization model can effectively communicate uncertainties into the optimization process and generate a spectrum of potential inexact solutions supporting local decision makers in managing benefit-effective water quality management schemes. HIRO is helpful for analysis of policy scenarios related to different levels of economic penalties, while also providing insight into the tradeoff between system benefits and environmental requirements.

A Hybrid Interval–Robust Optimization Model for Water Quality Management

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Xu, Jieyu; Li, Yongping; Huang, Guohe

2013-01-01

Abstract In water quality management problems, uncertainties may exist in many system components and pollution-related processes (i.e., random nature of hydrodynamic conditions, variability in physicochemical processes, dynamic interactions between pollutant loading and receiving water bodies, and indeterminacy of available water and treated wastewater). These complexities lead to difficulties in formulating and solving the resulting nonlinear optimization problems. In this study, a hybrid interval–robust optimization (HIRO) method was developed through coupling stochastic robust optimization and interval linear programming. HIRO can effectively reflect the complex system features under uncertainty, where implications of water quality/quantity restrictions for achieving regional economic development objectives are studied. By delimiting the uncertain decision space through dimensional enlargement of the original chemical oxygen demand (COD) discharge constraints, HIRO enhances the robustness of the optimization processes and resulting solutions. This method was applied to planning of industry development in association with river-water pollution concern in New Binhai District of Tianjin, China. Results demonstrated that the proposed optimization model can effectively communicate uncertainties into the optimization process and generate a spectrum of potential inexact solutions supporting local decision makers in managing benefit-effective water quality management schemes. HIRO is helpful for analysis of policy scenarios related to different levels of economic penalties, while also providing insight into the tradeoff between system benefits and environmental requirements. PMID:23922495

Optimization of hybrid iterative reconstruction level in pediatric body CT.

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Karmazyn, Boaz; Liang, Yun; Ai, Huisi; Eckert, George J; Cohen, Mervyn D; Wanner, Matthew R; Jennings, S Gregory

2014-02-01

The objective of our study was to attempt to optimize the level of hybrid iterative reconstruction (HIR) in pediatric body CT. One hundred consecutive chest or abdominal CT examinations were selected. For each examination, six series were obtained: one filtered back projection (FBP) and five HIR series (iDose(4)) levels 2-6. Two pediatric radiologists, blinded to noise measurements, independently chose the optimal HIR level and then rated series quality. We measured CT number (mean in Hounsfield units) and noise (SD in Hounsfield units) changes by placing regions of interest in the liver, muscles, subcutaneous fat, and aorta. A mixed-model analysis-of-variance test was used to analyze correlation of noise reduction with the optimal HIR level compared with baseline FBP noise. One hundred CT examinations were performed of 88 patients (52 females and 36 males) with a mean age of 8.5 years (range, 19 days-18 years); 12 patients had both chest and abdominal CT studies. Radiologists agreed to within one level of HIR in 92 of 100 studies. The mean quality rating was significantly higher for HIR than FBP (3.6 vs 3.3, respectively; p optimal HIR level was used (p optimal for most studies. The optimal HIR level was less effective in reducing liver noise in children with lower baseline noise.

A study of variable thrust, variable specific impulse trajectories for solar system exploration

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Sakai, Tadashi

A study has been performed to determine the advantages and disadvantages of variable thrust and variable Isp (specific impulse) trajectories for solar system exploration. There have been several numerical research efforts for variable thrust, variable Isp, power-limited trajectory optimization problems. All of these results conclude that variable thrust, variable Isp (variable specific impulse, or VSI) engines are superior to constant thrust, constant Isp (constant specific impulse; or CSI) engines. However, most of these research efforts assume a mission from Earth to Mars, and some of them further assume that these planets are circular and coplanar. Hence they still lack the generality. This research has been conducted to answer the following questions: (1) Is a VSI engine always better than a CSI engine or a high thrust engine for any mission to any planet with any time of flight considering lower propellant mass as the sole criterion? (2) If a planetary swing-by is used for a VSI trajectory, is the fuel savings of a VSI swing-by trajectory better than that of a CSI swing-by or high thrust swing-by trajectory? To support this research, an unique, new computer-based interplanetary trajectory calculation program has been created. This program utilizes a calculus of variations algorithm to perform overall optimization of thrust, Isp, and thrust vector direction along a trajectory that minimizes fuel consumption for interplanetary travel. It is assumed that the propulsion system is power-limited, and thus the compromise between thrust and Isp is a variable to be optimized along the flight path. This program is capable of optimizing not only variable thrust trajectories but also constant thrust trajectories in 3-D space using a planetary ephemeris database. It is also capable of conducting planetary swing-bys. Using this program, various Earth-originating trajectories have been investigated and the optimized results have been compared to traditional CSI and high

Mars entry-to-landing trajectory optimization and closed loop guidance

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Ilgen, Marc R.; Manning, Raymund A.; Cruz, Manuel I.

1991-01-01

The guidance strategy of the Mars Rover Sample Return mission is presented in detail. Aeromaneuver versus aerobrake trades are examined, and an aerobrake analysis is presented which takes into account targeting, guidance, flight control, trajectory profile, delivery accuracy. An aeromaneuver analysis is given which includes the entry corridor, maneuver footprint, guidance, preentry phase, constant drag phase, equilibrium guide phase, variable drag phase, influence of trajectory profile on the entry flight loads, parachute deployment conditions and strategies, and landing accuracy. The Mars terminal descent phase is analyzed.

Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem.

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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.

Developmental trajectories and breakdown in F1 interpopulation hybrids of Tribolium castaneum

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Drury, Douglas W; Ehmke, Ross C; Jideonwo, Victoria N; Wade, Michael J

2013-01-01

When hybrid inviability is an indirect by-product of local adaptation, we expect its degree of severity between pairs of populations to vary and to be sensitive to the environment. While complete reciprocal hybrid inviability is the outcome of the gradual process of local adaptation, it is not representative of the process of accumulation of incompatibility. In the flour beetle, Tribolium castaneum, some pairs of populations exhibit complete, reciprocal F1 hybrid incompatibility while other pairs are fully or partially compatible. We characterize this naturally occurring variation in the degree and timing of expression of the hybrid incompatible phenotype to better understand the number of genes or developmental processes contributing to speciation. We assessed the morphological and developmental variation in four Tribolium castaneum populations and their 12 possible F1 hybrids at each life-history stage from egg to adult. We find that the rate of hybrid larval development is affected in all interpopulation crosses, including those eventually producing viable, fertile adults. Hybrid incompatibility manifests early in development as changes in the duration of instars and diminished success in the transition between instars are relative to the parent populations. Parent populations with similar developmental profiles may produce hybrids with disrupted development. The degree and timing of expression of hybrid inviability depends upon populations crossed, direction of the cross, and environment in which hybrids are raised. Our findings suggest that the coordinated expression of genes involved in transitional periods of development is the underlying cause of hybrid incompatibility in this species. PMID:23919145

A fully adaptive hybrid optimization of aircraft engine blades

Science.gov (United States)

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.

Mechanisms of Surface-Mediated DNA Hybridization

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