Optimal control of operation efficiency of belt conveyor systems

International Nuclear Information System (INIS)

Zhang, Shirong; Xia, Xiaohua

2010-01-01

The improvement of the energy efficiency of belt conveyor systems can be achieved at equipment or operation levels. Switching control and variable speed control are proposed in literature to improve energy efficiency of belt conveyors. The current implementations mostly focus on lower level control loops or an individual belt conveyor without operational considerations at the system level. In this paper, an optimal switching control and a variable speed drive (VSD) based optimal control are proposed to improve the energy efficiency of belt conveyor systems at the operational level, where time-of-use (TOU) tariff, ramp rate of belt speed and other system constraints are considered. A coal conveying system in a coal-fired power plant is taken as a case study, where great saving of energy cost is achieved by the two optimal control strategies. Moreover, considerable energy saving resulting from VSD based optimal control is also proved by the case study.

Optimal control of operation efficiency of belt conveyor systems

Energy Technology Data Exchange (ETDEWEB)

Zhang, Shirong [Department of Automation, Wuhan University, Wuhan 430072 (China); Xia, Xiaohua [Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002 (South Africa)

2010-06-15

The improvement of the energy efficiency of belt conveyor systems can be achieved at equipment or operation levels. Switching control and variable speed control are proposed in literature to improve energy efficiency of belt conveyors. The current implementations mostly focus on lower level control loops or an individual belt conveyor without operational considerations at the system level. In this paper, an optimal switching control and a variable speed drive (VSD) based optimal control are proposed to improve the energy efficiency of belt conveyor systems at the operational level, where time-of-use (TOU) tariff, ramp rate of belt speed and other system constraints are considered. A coal conveying system in a coal-fired power plant is taken as a case study, where great saving of energy cost is achieved by the two optimal control strategies. Moreover, considerable energy saving resulting from VSD based optimal control is also proved by the case study. (author)

Optimal control of quantum systems: a projection approach

International Nuclear Information System (INIS)

Cheng, C.-J.; Hwang, C.-C.; Liao, T.-L.; Chou, G.-L.

2005-01-01

This paper considers the optimal control of quantum systems. The controlled quantum systems are described by the probability-density-matrix-based Liouville-von Neumann equation. Using projection operators, the states of the quantum system are decomposed into two sub-spaces, namely the 'main state' space and the 'remaining state' space. Since the control energy is limited, a solution for optimizing the external control force is proposed in which the main state is brought to the desired main state at a certain target time, while the population of the remaining state is simultaneously suppressed in order to diminish its effects on the final population of the main state. The optimization problem is formulated by maximizing a general cost functional of states and control force. An efficient algorithm is developed to solve the optimization problem. Finally, using the hydrogen fluoride (HF) molecular population transfer problem as an illustrative example, the effectiveness of the proposed scheme for a quantum system initially in a mixed state or in a pure state is investigated through numerical simulations

Optimal Control and Forecasting of Complex Dynamical Systems

CERN Document Server

Grigorenko, Ilya

2006-01-01

This important book reviews applications of optimization and optimal control theory to modern problems in physics, nano-science and finance. The theory presented here can be efficiently applied to various problems, such as the determination of the optimal shape of a laser pulse to induce certain excitations in quantum systems, the optimal design of nanostructured materials and devices, or the control of chaotic systems and minimization of the forecast error for a given forecasting model (for example, artificial neural networks). Starting from a brief review of the history of variational calcul

Optimal control of complex atomic quantum systems.

Science.gov (United States)

van Frank, S; Bonneau, M; Schmiedmayer, J; Hild, S; Gross, C; Cheneau, M; Bloch, I; Pichler, T; Negretti, A; Calarco, T; Montangero, S

2016-10-11

Quantum technologies will ultimately require manipulating many-body quantum systems with high precision. Cold atom experiments represent a stepping stone in that direction: a high degree of control has been achieved on systems of increasing complexity. However, this control is still sub-optimal. In many scenarios, achieving a fast transformation is crucial to fight against decoherence and imperfection effects. Optimal control theory is believed to be the ideal candidate to bridge the gap between early stage proof-of-principle demonstrations and experimental protocols suitable for practical applications. Indeed, it can engineer protocols at the quantum speed limit - the fastest achievable timescale of the transformation. Here, we demonstrate such potential by computing theoretically and verifying experimentally the optimal transformations in two very different interacting systems: the coherent manipulation of motional states of an atomic Bose-Einstein condensate and the crossing of a quantum phase transition in small systems of cold atoms in optical lattices. We also show that such processes are robust with respect to perturbations, including temperature and atom number fluctuations.

Optimization and Control of Cyber-Physical Vehicle Systems

Directory of Open Access Journals (Sweden)

Justin M. Bradley

2015-09-01

Full Text Available A cyber-physical system (CPS is composed of tightly-integrated computation, communication and physical elements. Medical devices, buildings, mobile devices, robots, transportation and energy systems can benefit from CPS co-design and optimization techniques. Cyber-physical vehicle systems (CPVSs are rapidly advancing due to progress in real-time computing, control and artificial intelligence. Multidisciplinary or multi-objective design optimization maximizes CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modeling errors and uncertainties. CPVS optimization occurs at design-time and at run-time. This paper surveys the run-time cooperative optimization or co-optimization of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilized in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimization and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined.

Optimization and Control of Cyber-Physical Vehicle Systems.

Science.gov (United States)

Bradley, Justin M; Atkins, Ella M

2015-09-11

A cyber-physical system (CPS) is composed of tightly-integrated computation, communication and physical elements. Medical devices, buildings, mobile devices, robots, transportation and energy systems can benefit from CPS co-design and optimization techniques. Cyber-physical vehicle systems (CPVSs) are rapidly advancing due to progress in real-time computing, control and artificial intelligence. Multidisciplinary or multi-objective design optimization maximizes CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modeling errors and uncertainties. CPVS optimization occurs at design-time and at run-time. This paper surveys the run-time cooperative optimization or co-optimization of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilized in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimization and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined.

Optimal Investment Control of Macroeconomic Systems

Institute of Scientific and Technical Information of China (English)

ZHAO Ke-jie; LIU Chuan-zhe

2006-01-01

Economic growth is always accompanied by economic fluctuation. The target of macroeconomic control is to keep a basic balance of economic growth, accelerate the optimization of economic structures and to lead a rapid, sustainable and healthy development of national economies, in order to propel society forward. In order to realize the above goal, investment control must be regarded as the most important policy for economic stability. Readjustment and control of investment includes not only control of aggregate investment, but also structural control which depends on economic-technology relationships between various industries of a national economy. On the basis of the theory of a generalized system, an optimal investment control model for government has been developed. In order to provide a scientific basis for government to formulate a macroeconomic control policy, the model investigates the balance of total supply and aggregate demand through an adjustment in investment decisions realizes a sustainable and stable growth of the national economy. The optimal investment decision function proposed by this study has a unique and specific expression, high regulating precision and computable characteristics.

Optimal control of quantum systems: Origins of inherent robustness to control field fluctuations

International Nuclear Information System (INIS)

Rabitz, Herschel

2002-01-01

The impact of control field fluctuations on the optimal manipulation of quantum dynamics phenomena is investigated. The quantum system is driven by an optimal control field, with the physical focus on the evolving expectation value of an observable operator. A relationship is shown to exist between the system dynamics and the control field fluctuations, wherein the process of seeking optimal performance assures an inherent degree of system robustness to such fluctuations. The presence of significant field fluctuations breaks down the evolution of the observable expectation value into a sequence of partially coherent robust steps. Robustness occurs because the optimization process reduces sensitivity to noise-driven quantum system fluctuations by taking advantage of the observable expectation value being bilinear in the evolution operator and its adjoint. The consequences of this inherent robustness are discussed in the light of recent experiments and numerical simulations on the optimal control of quantum phenomena. The analysis in this paper bodes well for the future success of closed-loop quantum optimal control experiments, even in the presence of reasonable levels of field fluctuations

Price-based Optimal Control of Electrical Power Systems

Energy Technology Data Exchange (ETDEWEB)

Jokic, A.

2007-09-10

The research presented in this thesis is motivated by the following issue of concern for the operation of future power systems: Future power systems will be characterized by significantly increased uncertainties at all time scales and, consequently, their behavior in time will be difficult to predict. In Chapter 2 we will present a novel explicit, dynamic, distributed feedback control scheme that utilizes nodal-prices for real-time optimal power balance and network congestion control. The term explicit means that the controller is not based on solving an optimization problem on-line. Instead, the nodal prices updates are based on simple, explicitly defined and easily comprehensible rules. We prove that the developed control scheme, which acts on the measurements from the current state of the system, always provide the correct nodal prices. In Chapter 3 we will develop a novel, robust, hybrid MPC control (model predictive controller) scheme for power balance control with hard constraints on line power flows and network frequency deviations. The developed MPC controller acts in parallel with the explicit controller from Chapter 2, and its task is to enforce the constraints during the transient periods following suddenly occurring power imbalances in the system. In Chapter 4 the concept of autonomous power networks will be presented as a concise formulation to deal with economic, technical and reliability issues in power systems with a large penetration of distributed generating units. With autonomous power networks as new market entities, we propose a novel operational structure of ancillary service markets. In Chapter 5 we will consider the problem of controlling a general linear time-invariant dynamical system to an economically optimal operating point, which is defined by a multiparametric constrained convex optimization problem related with the steady-state operation of the system. The parameters in the optimization problem are values of the exogenous inputs to

Existence of optimal controls for systems governed by mean-field ...

African Journals Online (AJOL)

In this paper, we study the existence of an optimal control for systems, governed by stochastic dierential equations of mean-eld type. For non linear systems, we prove the existence of an optimal relaxed control, by using tightness techniques and Skorokhod selection theorem. The optimal control is a measure valued process ...

Optimal boundary control and boundary stabilization of hyperbolic systems

CERN Document Server

Gugat, Martin

2015-01-01

This brief considers recent results on optimal control and stabilization of systems governed by hyperbolic partial differential equations, specifically those in which the control action takes place at the boundary.  The wave equation is used as a typical example of a linear system, through which the author explores initial boundary value problems, concepts of exact controllability, optimal exact control, and boundary stabilization.  Nonlinear systems are also covered, with the Korteweg-de Vries and Burgers Equations serving as standard examples.  To keep the presentation as accessible as possible, the author uses the case of a system with a state that is defined on a finite space interval, so that there are only two boundary points where the system can be controlled.  Graduate and post-graduate students as well as researchers in the field will find this to be an accessible introduction to problems of optimal control and stabilization.

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.

Optimal dynamic control of resources in a distributed system

Science.gov (United States)

Shin, Kang G.; Krishna, C. M.; Lee, Yann-Hang

1989-01-01

The authors quantitatively formulate the problem of controlling resources in a distributed system so as to optimize a reward function and derive optimal control strategies using Markov decision theory. The control variables treated are quite general; they could be control decisions related to system configuration, repair, diagnostics, files, or data. Two algorithms for resource control in distributed systems are derived for time-invariant and periodic environments, respectively. A detailed example to demonstrate the power and usefulness of the approach is provided.

Optimal pole shifting controller for interconnected power system

International Nuclear Information System (INIS)

Yousef, Ali M.; Kassem, Ahmed M.

2011-01-01

Research highlights: → Mathematical model represents a power system which consists of synchronous machine connected to infinite bus through transmission line. → Power system stabilizer was designed based on optimal pole shifting controller. → The system performances was tested through load disturbances at different operating conditions. → The system performance with the proposed optimal pole shifting controller is compared with the conventional pole placement controller. → The digital simulation results indicated that the proposed controller has a superior performance. -- Abstract: Power system stabilizer based on optimal pole shifting is proposed. An approach for shifting the real parts of the open-loop poles to any desired positions while preserving the imaginary parts is presented. In each step of this approach, it is required to solve a first-order or a second-order linear matrix Lyapunov equation for shifting one real pole or two complex conjugate poles, respectively. This presented method yields a solution, which is optimal with respect to a quadratic performance index. The attractive feature of this method is that it enables solutions of the complex problem to be easily found without solving any non-linear algebraic Riccati equation. The present power system stabilizer is based on Riccati equation approach. The control law depends on finding the feedback gain matrix, and then the control signal is synthesized by multiplying the state variables of the power system with determined gain matrix. The gain matrix is calculated one time only, and it works over wide range of operating conditions. To validate the power of the proposed PSS, a linearized model of a simple power system consisted of a single synchronous machine connected to infinite bus bar through transmission line is simulated. The studied power system is subjected to various operating points and power system parameters changes.

Optimal pole shifting controller for interconnected power system

Energy Technology Data Exchange (ETDEWEB)

Yousef, Ali M., E-mail: drali_yousef@yahoo.co [Electrical Eng. Dept., Faculty of Engineering, Assiut University (Egypt); Kassem, Ahmed M., E-mail: kassem_ahmed53@hotmail.co [Control Technology Dep., Industrial Education College, Beni-Suef University (Egypt)

2011-05-15

Research highlights: {yields} Mathematical model represents a power system which consists of synchronous machine connected to infinite bus through transmission line. {yields} Power system stabilizer was designed based on optimal pole shifting controller. {yields} The system performances was tested through load disturbances at different operating conditions. {yields} The system performance with the proposed optimal pole shifting controller is compared with the conventional pole placement controller. {yields} The digital simulation results indicated that the proposed controller has a superior performance. -- Abstract: Power system stabilizer based on optimal pole shifting is proposed. An approach for shifting the real parts of the open-loop poles to any desired positions while preserving the imaginary parts is presented. In each step of this approach, it is required to solve a first-order or a second-order linear matrix Lyapunov equation for shifting one real pole or two complex conjugate poles, respectively. This presented method yields a solution, which is optimal with respect to a quadratic performance index. The attractive feature of this method is that it enables solutions of the complex problem to be easily found without solving any non-linear algebraic Riccati equation. The present power system stabilizer is based on Riccati equation approach. The control law depends on finding the feedback gain matrix, and then the control signal is synthesized by multiplying the state variables of the power system with determined gain matrix. The gain matrix is calculated one time only, and it works over wide range of operating conditions. To validate the power of the proposed PSS, a linearized model of a simple power system consisted of a single synchronous machine connected to infinite bus bar through transmission line is simulated. The studied power system is subjected to various operating points and power system parameters changes.

Optimal coherent control of dissipative N-level systems

International Nuclear Information System (INIS)

Jirari, H.; Poetz, W.

2005-01-01

General optimal coherent control of dissipative N-level systems in the Markovian time regime is formulated within Pointryagin's principle and the Lindblad equation. In the present paper, we study feasibility and limitations of steering of dissipative two-, three-, and four-level systems from a given initial pure or mixed state into a desired final state under the influence of an external electric field. The time evolution of the system is computed within the Lindblad equation and a conjugate gradient method is used to identify optimal control fields. The influence of both field-independent population and polarization decay on achieving the objective is investigated in systematic fashion. It is shown that, for realistic dephasing times, optimum control fields can be identified which drive the system into the target state with very high success rate and in economical fashion, even when starting from a poor initial guess. Furthermore, the optimal fields obtained give insight into the system dynamics. However, if decay rates of the system cannot be subjected to electromagnetic control, the dissipative system cannot be maintained in a specific pure or mixed state, in general

Optimal robust control strategy of a solid oxide fuel cell system

Science.gov (United States)

Wu, Xiaojuan; Gao, Danhui

2018-01-01

Optimal control can ensure system safe operation with a high efficiency. However, only a few papers discuss optimal control strategies for solid oxide fuel cell (SOFC) systems. Moreover, the existed methods ignore the impact of parameter uncertainty on system instantaneous performance. In real SOFC systems, several parameters may vary with the variation of operation conditions and can not be identified exactly, such as load current. Therefore, a robust optimal control strategy is proposed, which involves three parts: a SOFC model with parameter uncertainty, a robust optimizer and robust controllers. During the model building process, boundaries of the uncertain parameter are extracted based on Monte Carlo algorithm. To achieve the maximum efficiency, a two-space particle swarm optimization approach is employed to obtain optimal operating points, which are used as the set points of the controllers. To ensure the SOFC safe operation, two feed-forward controllers and a higher-order robust sliding mode controller are presented to control fuel utilization ratio, air excess ratio and stack temperature afterwards. The results show the proposed optimal robust control method can maintain the SOFC system safe operation with a maximum efficiency under load and uncertainty variations.

Linear systems optimal and robust control

CERN Document Server

Sinha, Alok

2007-01-01

Introduction Overview Contents of the Book State Space Description of a Linear System Transfer Function of a Single Input/Single Output (SISO) System State Space Realizations of a SISO System SISO Transfer Function from a State Space Realization Solution of State Space Equations Observability and Controllability of a SISO System Some Important Similarity Transformations Simultaneous Controllability and Observability Multiinput/Multioutput (MIMO) Systems State Space Realizations of a Transfer Function Matrix Controllability and Observability of a MIMO System Matrix-Fraction Description (MFD) MFD of a Transfer Function Matrix for the Minimal Order of a State Space Realization Controller Form Realization from a Right MFD Poles and Zeros of a MIMO Transfer Function Matrix Stability Analysis State Feedback Control and Optimization State Variable Feedback for a Single Input System Computation of State Feedback Gain Matrix for a Multiinput System State Feedback Gain Matrix for a Multi...

Optimal control systems in hydro power plants

International Nuclear Information System (INIS)

Babunski, Darko L.

2012-01-01

The aim of the research done in this work is focused on obtaining the optimal models of hydro turbine including auxiliary equipment, analysis of governors for hydro power plants and analysis and design of optimal control laws that can be easily applicable in real hydro power plants. The methodology of the research and realization of the set goals consist of the following steps: scope of the models of hydro turbine, and their modification using experimental data; verification of analyzed models and comparison of advantages and disadvantages of analyzed models, with proposal of turbine model for design of control low; analysis of proportional-integral-derivative control with fixed parameters and gain scheduling and nonlinear control; analysis of dynamic characteristics of turbine model including control and comparison of parameters of simulated system with experimental data; design of optimal control of hydro power plant considering proposed cost function and verification of optimal control law with load rejection measured data. The hydro power plant models, including model of power grid are simulated in case of island ing and restoration after breakup and load rejection with consideration of real loading and unloading of hydro power plant. Finally, simulations provide optimal values of control parameters, stability boundaries and results easily applicable to real hydro power plants. (author)

Optimal Control of Switching Linear Systems

Directory of Open Access Journals (Sweden)

Ali Benmerzouga

2004-06-01

Full Text Available A solution to the control of switching linear systems with input constraints was given in Benmerzouga (1997 for both the conventional enumeration approach and the new approach. The solution given there turned out to be not unique. The main objective in this work is to determine the optimal control sequences {Ui(k ,  i = 1,..., M ;  k = 0, 1, ...,  N -1} which transfer the system from a given initial state  X0  to a specific target state  XT  (or to be as close as possible by using the same discrete time solution obtained in Benmerzouga (1997 and minimizing a running cost-to-go function. By using the dynamic programming technique, the optimal solution is found for both approaches given in Benmerzouga (1997. The computational complexity of the modified algorithm is also given.

Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

Science.gov (United States)

Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.

2015-01-01

The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

Hierarchical Control for Optimal and Distributed Operation of Microgrid Systems

DEFF Research Database (Denmark)

Meng, Lexuan

manages the power flow with external grids, while the economic and optimal operation of MGs is not guaranteed by applying the existing schemes. Accordingly, this project dedicates to the study of real-time optimization methods for MGs, including the review of optimization algorithms, system level...... mathematical modeling, and the implementation of real-time optimization into existing hierarchical control schemes. Efficiency enhancement in DC MGs and optimal unbalance compensation in AC MGs are taken as the optimization objectives in this project. Necessary system dynamic modeling and stability analysis......, a discrete-time domain modeling method is proposed to establish an accurate system level model. Taking into account the different sampling times of real world plant, digital controller and communication devices, the system is modeled with these three parts separately, and with full consideration...

Control and System Theory, Optimization, Inverse and Ill-Posed Problems

Science.gov (United States)

1988-09-14

Justlfleatlen Distribut ion/ Availability Codes # AFOSR-87-0350 Avat’ and/or1987-1988 Dist Special *CONTROL AND SYSTEM THEORY , ~ * OPTIMIZATION, * INVERSE...considerable va- riety of research investigations within the grant areas (Control and system theory , Optimization, and Ill-posed problems]. The

Optimal control of nonlinear continuous-time systems in strict-feedback form.

Science.gov (United States)

Zargarzadeh, Hassan; Dierks, Travis; Jagannathan, Sarangapani

2015-10-01

This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.

Multiobjective Optimization Design of a Fractional Order PID Controller for a Gun Control System

Directory of Open Access Journals (Sweden)

Qiang Gao

2013-01-01

Full Text Available Motion control of gun barrels is an ongoing topic for the development of gun control equipments possessing excellent performances. In this paper, a typical fractional order PID control strategy is employed for the gun control system. To obtain optimal parameters of the controller, a multiobjective optimization scheme is developed from the loop-shaping perspective. To solve the specified nonlinear optimization problem, a novel Pareto optimal solution based multiobjective differential evolution algorithm is proposed. To enhance the convergent rate of the optimization process, an opposition based learning method is embedded in the chaotic population initialization process. To enhance the robustness of the algorithm for different problems, an adapting scheme of the mutation operation is further employed. With assistance of the evolutionary algorithm, the optimal solution for the specified problem is selected. The numerical simulation results show that the control system can rapidly follow the demand signal with high accuracy and high robustness, demonstrating the efficiency of the proposed controller parameter tuning method.

Optimizing a mobile robot control system using GPU acceleration

Science.gov (United States)

Tuck, Nat; McGuinness, Michael; Martin, Fred

2012-01-01

This paper describes our attempt to optimize a robot control program for the Intelligent Ground Vehicle Competition (IGVC) by running computationally intensive portions of the system on a commodity graphics processing unit (GPU). The IGVC Autonomous Challenge requires a control program that performs a number of different computationally intensive tasks ranging from computer vision to path planning. For the 2011 competition our Robot Operating System (ROS) based control system would not run comfortably on the multicore CPU on our custom robot platform. The process of profiling the ROS control program and selecting appropriate modules for porting to run on a GPU is described. A GPU-targeting compiler, Bacon, is used to speed up development and help optimize the ported modules. The impact of the ported modules on overall performance is discussed. We conclude that GPU optimization can free a significant amount of CPU resources with minimal effort for expensive user-written code, but that replacing heavily-optimized library functions is more difficult, and a much less efficient use of time.

Optimized controllers for enhancing dynamic performance of PV interface system

Directory of Open Access Journals (Sweden)

Mahmoud A. Attia

2018-05-01

Full Text Available The dynamic performance of PV interface system can be improved by optimizing the gains of the Proportional–Integral (PI controller. In this work, gravitational search algorithm and harmony search algorithm are utilized to optimal tuning of PI controller gains. Performance comparison between the PV system with optimized PI gains utilizing different techniques are carried out. Finally, the dynamic behavior of the system is studied under hypothetical sudden variations in irradiance. The examination of the proposed techniques for optimal tuning of PI gains is conducted using MATLAB/SIMULINK software package. The main contribution of this work is investigating the dynamic performance of PV interfacing system with application of gravitational search algorithm and harmony search algorithm for optimal PI parameters tuning. Keywords: Photovoltaic power systems, Gravitational search algorithm, Harmony search algorithm, Genetic algorithm, Artificial intelligence

Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty

Science.gov (United States)

Nguyen, Nhan T.

2012-01-01

This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.

Optimal Acquisition and Inventory Control for a Remanufacturing System

Directory of Open Access Journals (Sweden)

Zhigang Jiang

2013-01-01

Full Text Available Optimal acquisition and inventory control can often make the difference between successful and unsuccessful remanufacturing. However, there is a greater degree of uncertainty and complexity in a remanufacturing system, which leads to a critical need for planning and control models designed to deal with this added uncertainty and complexity. In this paper, a method for optimal acquisition and inventory control of a remanufacturing system is presented. The method considers three inventories, one for returned item and the other for serviceable and recoverable items. Taking the holding cost for returns, recoverable and remanufactured products, remanufacturing cost, disposal cost, and the loss caused by backlog into account, the optimal inventory control model is established to minimize the total costs. Finally, a numerical example is provided to illustrate the proposed methods.

The Optimal Steering Control System using Imperialist Competitive Algorithm on Vehicles with Steer-by-Wire System

Directory of Open Access Journals (Sweden)

F. Hunaini

2015-03-01

Full Text Available Steer-by-wire is the electrical steering systems on vehicles that are expected with the development of an optimal control system can improve the dynamic performance of the vehicle. This paper aims to optimize the control systems, namely Fuzzy Logic Control (FLC and the Proportional, Integral and Derivative (PID control on the vehicle steering system using Imperialist Competitive Algorithm (ICA. The control systems are built in a cascade, FLC to suppress errors in the lateral motion and the PID control to minimize the error in the yaw motion of the vehicle. FLC is built has two inputs (error and delta error and single output. Each input and output consists of three Membership Function (MF in the form of a triangular for language term "zero" and two trapezoidal for language term "negative" and "positive". In order to work optimally, each MF optimized using ICA to get the position and width of the most appropriate. Likewise, in the PID control, the constant at each Proportional, Integral and Derivative control also optimized using ICA, so there are six parameters of the control system are simultaneously optimized by ICA. Simulations performed on vehicle models with 10 Degree Of Freedom (DOF, the plant input using the variables of steering that expressed in the desired trajectory, and the plant outputs are lateral and yaw motion. The simulation results showed that the FLC-PID control system optimized by using ICA can maintain the movement of vehicle according to the desired trajectory with lower error and higher speed limits than optimized with Particle Swarm Optimization (PSO.

Optimization criteria for control and instrumentation systems in nuclear power plants

International Nuclear Information System (INIS)

Gonzalez, A.J.

1978-01-01

The system of dose limitation recently recommended by the International Commission on Radiation Protection includes, as a base for deciding what is reasonably achievable in dose reduction, the optimization of radioprotection systems. This paper, after compiling relevant points in the new system, discusses the application of optimization to control and instrumentation of radioprotection systems in nuclear power plants. Furthermore, an extension of the optimization criterion to nuclear safety systems is also presented and its application to control and instrumentation is discussed; systems including majority logics are particularly scrutinized. Finally, eventual regulatory implications are described. (author)

Optimization of Thermal Object Nonlinear Control Systems by Energy Efficiency Criterion.

Science.gov (United States)

Velichkin, Vladimir A.; Zavyalov, Vladimir A.

2018-03-01

This article presents the results of thermal object functioning control analysis (heat exchanger, dryer, heat treatment chamber, etc.). The results were used to determine a mathematical model of the generalized thermal control object. The appropriate optimality criterion was chosen to make the control more energy-efficient. The mathematical programming task was formulated based on the chosen optimality criterion, control object mathematical model and technological constraints. The “maximum energy efficiency” criterion helped avoid solving a system of nonlinear differential equations and solve the formulated problem of mathematical programming in an analytical way. It should be noted that in the case under review the search for optimal control and optimal trajectory reduces to solving an algebraic system of equations. In addition, it is shown that the optimal trajectory does not depend on the dynamic characteristics of the control object.

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

Time Optimal Control Laws for Bilinear Systems

Directory of Open Access Journals (Sweden)

Salim Bichiou

2018-01-01

Full Text Available The aim of this paper is to determine the feedforward and state feedback suboptimal time control for a subset of bilinear systems, namely, the control sequence and reaching time. This paper proposes a method that uses Block pulse functions as an orthogonal base. The bilinear system is projected along that base. The mathematical integration is transformed into a product of matrices. An algebraic system of equations is obtained. This system together with specified constraints is treated as an optimization problem. The parameters to determine are the final time, the control sequence, and the states trajectories. The obtained results via the newly proposed method are compared to known analytical solutions.

Optimal control of Formula One car energy recovery systems

Science.gov (United States)

Limebeer, D. J. N.; Perantoni, G.; Rao, A. V.

2014-10-01

The utility of orthogonal collocation methods in the solution of optimal control problems relating to Formula One racing is demonstrated. These methods can be used to optimise driver controls such as the steering, braking and throttle usage, and to optimise vehicle parameters such as the aerodynamic down force and mass distributions. Of particular interest is the optimal usage of energy recovery systems (ERSs). Contemporary kinetic energy recovery systems are studied and compared with future hybrid kinetic and thermal/heat ERSs known as ERS-K and ERS-H, respectively. It is demonstrated that these systems, when properly controlled, can produce contemporary lap time using approximately two-thirds of the fuel required by earlier generation (2013 and prior) vehicles.

Notions of local controllability and optimal feedforward control for quantum systems

International Nuclear Information System (INIS)

Chakrabarti, Raj

2011-01-01

Local controllability is an essential concept for regulation and control of time-varying nonlinear dynamical systems; in the classical control logic it is at the foundation of neighboring optimal feedback and feedforward control. We introduce notions of local controllability suited to feedforward control of classical input disturbances in bilinear quantum systems evolving on projective spaces and Lie groups. Tests for local controllability based on a Gramian matrix analogous to the nonlinear local controllability Gramian, which allow assessment of which trajectories can be regulated by perturbative feedforward in the presence of classical input noise, are presented. These notions explicitly incorporate system bilinearity and the geometry of quantum states into the definition of local controllability of quantum systems. Associated feedforward strategies are described.

Notions of local controllability and optimal feedforward control for quantum systems

Energy Technology Data Exchange (ETDEWEB)

Chakrabarti, Raj, E-mail: rchakra@purdue.edu [School of Chemical Engineering, Purdue University, West Lafayette, IN 47907 (United States)

2011-05-06

Local controllability is an essential concept for regulation and control of time-varying nonlinear dynamical systems; in the classical control logic it is at the foundation of neighboring optimal feedback and feedforward control. We introduce notions of local controllability suited to feedforward control of classical input disturbances in bilinear quantum systems evolving on projective spaces and Lie groups. Tests for local controllability based on a Gramian matrix analogous to the nonlinear local controllability Gramian, which allow assessment of which trajectories can be regulated by perturbative feedforward in the presence of classical input noise, are presented. These notions explicitly incorporate system bilinearity and the geometry of quantum states into the definition of local controllability of quantum systems. Associated feedforward strategies are described.

Sub-optimal control of fuzzy linear dynamical systems under granular differentiability concept.

Science.gov (United States)

Mazandarani, Mehran; Pariz, Naser

2018-05-01

This paper deals with sub-optimal control of a fuzzy linear dynamical system. The aim is to keep the state variables of the fuzzy linear dynamical system close to zero in an optimal manner. In the fuzzy dynamical system, the fuzzy derivative is considered as the granular derivative; and all the coefficients and initial conditions can be uncertain. The criterion for assessing the optimality is regarded as a granular integral whose integrand is a quadratic function of the state variables and control inputs. Using the relative-distance-measure (RDM) fuzzy interval arithmetic and calculus of variations, the optimal control law is presented as the fuzzy state variables feedback. Since the optimal feedback gains are obtained as fuzzy functions, they need to be defuzzified. This will result in the sub-optimal control law. This paper also sheds light on the restrictions imposed by the approaches which are based on fuzzy standard interval arithmetic (FSIA), and use strongly generalized Hukuhara and generalized Hukuhara differentiability concepts for obtaining the optimal control law. The granular eigenvalues notion is also defined. Using an RLC circuit mathematical model, it is shown that, due to their unnatural behavior in the modeling phenomenon, the FSIA-based approaches may obtain some eigenvalues sets that might be different from the inherent eigenvalues set of the fuzzy dynamical system. This is, however, not the case with the approach proposed in this study. The notions of granular controllability and granular stabilizability of the fuzzy linear dynamical system are also presented in this paper. Moreover, a sub-optimal control for regulating a Boeing 747 in longitudinal direction with uncertain initial conditions and parameters is gained. In addition, an uncertain suspension system of one of the four wheels of a bus is regulated using the sub-optimal control introduced in this paper. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Decentralized Optimization for a Novel Control Structure of HVAC System

Directory of Open Access Journals (Sweden)

Shiqiang Wang

2016-01-01

Full Text Available A decentralized control structure is introduced into the heating, ventilation, and air conditioning (HVAC system to solve the high maintenance and labor cost problem in actual engineering. Based on this new control system, a decentralized optimization method is presented for sensor fault repair and optimal group control of HVAC equipment. Convergence property of the novel method is theoretically analyzed considering both convex and nonconvex systems with constraints. In this decentralized control system, traditional device is fitted with a control chip such that it becomes a smart device. The smart device can communicate and operate collaboratively with the other devices to accomplish some designated tasks. The effectiveness of the presented method is verified by simulations and hardware tests.

Output Feedback Adaptive Control of Non-Minimum Phase Systems Using Optimal Control Modification

Science.gov (United States)

Nguyen, Nhan; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

2018-01-01

This paper describes output feedback adaptive control approaches for non-minimum phase SISO systems with relative degree 1 and non-strictly positive real (SPR) MIMO systems with uniform relative degree 1 using the optimal control modification method. It is well-known that the standard model-reference adaptive control (MRAC) cannot be used to control non-SPR plants to track an ideal SPR reference model. Due to the ideal property of asymptotic tracking, MRAC attempts an unstable pole-zero cancellation which results in unbounded signals for non-minimum phase SISO systems. The optimal control modification can be used to prevent the unstable pole-zero cancellation which results in a stable adaptation of non-minimum phase SISO systems. However, the tracking performance using this approach could suffer if the unstable zero is located far away from the imaginary axis. The tracking performance can be recovered by using an observer-based output feedback adaptive control approach which uses a Luenberger observer design to estimate the state information of the plant. Instead of explicitly specifying an ideal SPR reference model, the reference model is established from the linear quadratic optimal control to account for the non-minimum phase behavior of the plant. With this non-minimum phase reference model, the observer-based output feedback adaptive control can maintain stability as well as tracking performance. However, in the presence of the mismatch between the SPR reference model and the non-minimum phase plant, the standard MRAC results in unbounded signals, whereas a stable adaptation can be achieved with the optimal control modification. An application of output feedback adaptive control for a flexible wing aircraft illustrates the approaches.

Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

Science.gov (United States)

Zhang, Huaipin; Yue, Dong; Zhao, Wei; Hu, Songlin; Dou, Chunxia; Huaipin Zhang; Dong Yue; Wei Zhao; Songlin Hu; Chunxia Dou; Hu, Songlin; Zhang, Huaipin; Dou, Chunxia; Yue, Dong; Zhao, Wei

2018-06-01

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

Particle swarm optimization based PID controller tuning for level control of two tank system

Science.gov (United States)

Vincent, Anju K.; Nersisson, Ruban

2017-11-01

Automatic control plays a vital role in industrial operation. In process industries, in order to have an improved and stable control system, we need a robust tuning method. In this paper Particle Swarm Optimization (PSO) based algorithm is proposed for the optimization of a PID controller for level control process. A two tank system is considered. Initially a PID controller is designed using an Internal Model Control (IMC). The results are compared with the PSO based controller setting. The performance of the controller is compared and analyzed by time domain specification. In order to validate the robustness of PID controller, disturbance is imposed. The system is simulated using MATLAB. The results show that the proposed method provides better controller performance.

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

Complex optimization of radiometric control and measurement systems

International Nuclear Information System (INIS)

Onishchenko, A.M.

1995-01-01

Fundamentals of a new approach to increase in the accuracy of radiometric systems of control and measurements are presented in succession. Block diagram of the new concept of radiometric system optimization is provided. The approach involving radical increase in accuracy and envisages ascertaining of controlled parameter by the totality of two intelligence signals closely correlated with each other. The new concept makes use of system analysis as a unified one-piece object, permitting euristic synthesis of the system. 4 refs., 3 figs

Optimal control for parabolic-hyperbolic system with time delay

International Nuclear Information System (INIS)

Kowalewski, A.

1985-07-01

In this paper we consider an optimal control problem for a system described by a linear partial differential equation of the parabolic-hyperbolic type with time delay in the state. The right-hand side of this equation and the initial conditions are not continuous functions usually, but they are measurable functions belonging to L 2 or Lsup(infinity) spaces. Therefore, the solution of this equation is given by a certain Sobolev space. The time delay in the state is constant, but it can be also a function of time. The control time T is fixed in our problem. Making use of the Milutin-Dubovicki theorem, necessary and sufficient conditions of optimality with the quadratic performance functional and constrained control are derived for the Dirichlet problem. The flow chart of the algorithm which can be used in the numerical solving of certain optimization problems for distributed systems is also presented. (author)

Dynamics of underactuated multibody systems modeling, control and optimal design

CERN Document Server

Seifried, Robert

2014-01-01

Underactuated multibody systems are intriguing mechatronic systems, as they possess fewer control inputs than degrees of freedom. Some examples are modern light-weight flexible robots and articulated manipulators with passive joints. This book investigates such underactuated multibody systems from an integrated perspective. This includes all major steps from the modeling of rigid and flexible multibody systems, through nonlinear control theory, to optimal system design. The underlying theories and techniques from these different fields are presented using a self-contained and unified approach and notation system. Subsequently, the book focuses on applications to large multibody systems with multiple degrees of freedom, which require a combination of symbolical and numerical procedures. Finally, an integrated, optimization-based design procedure is proposed, whereby both structural and control design are considered concurrently. Each chapter is supplemented by illustrated examples.

Thermodynamic framework for discrete optimal control in multiphase flow systems

Science.gov (United States)

Sieniutycz, Stanislaw

1999-08-01

Bellman's method of dynamic programming is used to synthesize diverse optimization approaches to active (work producing) and inactive (entropy generating) multiphase flow systems. Thermal machines, optimally controlled unit operations, nonlinear heat conduction, spontaneous relaxation processes, and self-propagating wave fronts are all shown to satisfy a discrete Hamilton-Jacobi-Bellman equation and a corresponding discrete optimization algorithm of Pontryagin's type, with the maximum principle for a Hamiltonian. The extremal structures are always canonical. A common unifying criterion is set for all considered systems, which is the criterion of a minimum generated entropy. It is shown that constraints can modify the entropy functionals in a different way for each group of the processes considered; thus the resulting structures of these functionals may differ significantly. Practical conclusions are formulated regarding the energy savings and energy policy in optimally controlled systems.

Control Systems with Normalized and Covariance Adaptation by Optimal Control Modification

Science.gov (United States)

Nguyen, Nhan T. (Inventor); Burken, John J. (Inventor); Hanson, Curtis E. (Inventor)

2016-01-01

Disclosed is a novel adaptive control method and system called optimal control modification with normalization and covariance adjustment. The invention addresses specifically to current challenges with adaptive control in these areas: 1) persistent excitation, 2) complex nonlinear input-output mapping, 3) large inputs and persistent learning, and 4) the lack of stability analysis tools for certification. The invention has been subject to many simulations and flight testing. The results substantiate the effectiveness of the invention and demonstrate the technical feasibility for use in modern aircraft flight control systems.

Distributed Cooperative Optimal Control for Multiagent Systems on Directed Graphs: An Inverse Optimal Approach.

Science.gov (United States)

Zhang, Huaguang; Feng, Tao; Yang, Guang-Hong; Liang, Hongjing

2015-07-01

In this paper, the inverse optimal approach is employed to design distributed consensus protocols that guarantee consensus and global optimality with respect to some quadratic performance indexes for identical linear systems on a directed graph. The inverse optimal theory is developed by introducing the notion of partial stability. As a result, the necessary and sufficient conditions for inverse optimality are proposed. By means of the developed inverse optimal theory, the necessary and sufficient conditions are established for globally optimal cooperative control problems on directed graphs. Basic optimal cooperative design procedures are given based on asymptotic properties of the resulting optimal distributed consensus protocols, and the multiagent systems can reach desired consensus performance (convergence rate and damping rate) asymptotically. Finally, two examples are given to illustrate the effectiveness of the proposed methods.

CALCULATION METHODS OF OPTIMAL ADJUSTMENT OF CONTROL SYSTEM THROUGH DISTURBANCE CHANNEL

Directory of Open Access Journals (Sweden)

I. M. Golinko

2014-01-01

Full Text Available In the process of automatic control system debugging the great attention is paid to determining formulas’ parameters of optimal dynamic adjustment of regulators, taking into account the dynamics of Objects control. In most cases the known formulas are oriented on design of automatic control system through channel “input-output definition”. But practically in all continuous processes the main task of all regulators is stabilization of output parameters. The Methods of parameters calculation for dynamic adjustment of regulations were developed. These methods allow to optimize the analog and digital regulators, taking into account minimization of regulated influences. There were suggested to use the fact of detuning and maximum value of regulated influence. As the automatic control system optimization with proportional plus reset controllers on disturbance channel is an unimodal task, the main algorithm of optimization is realized by Hooke – Jeeves method. For controllers optimization through channel external disturbance there were obtained functional dependences of parameters calculations of dynamic proportional plus reset controllers from dynamic characteristics of Object control. The obtained dependences allow to improve the work of controllers (regulators of automatic control on external disturbance channel and so it allows to improve the quality of regulation of transient processes. Calculation formulas provide high accuracy and convenience in usage. In suggested method there are no nomographs and this fact expels subjectivity of investigation in determination of parameters of dynamic adjustment of proportional plus reset controllers. Functional dependences can be used for calculation of adjustment of PR controllers in a great range of change of dynamic characteristics of Objects control.

The Pealization of the Most Economical and optimized Control System

Institute of Scientific and Technical Information of China (English)

WUBin

2002-01-01

In order to plow an access to low cost automation,the method to set up the most economical and optimized control system is studied.Such a system is achieved by adopting the field bus technologies based on net connection to form the hierarchical architecture and employing genetic algorithm to intelliently optimize the parameters of the topology structure at the field execution level and the parameters of a local controller,Praxios has proved that this realization can shorten the system development cycle,improve the systtem's reliability,and achieve conspicuous social economic benefits.

Simplified model-based optimal control of VAV air-conditioning system

Energy Technology Data Exchange (ETDEWEB)

Nassif, N.; Kajl, S.; Sabourin, R. [Ecole de Technologie Superieure, Montreal, PQ (Canada). Dept. of Construction Engineering

2005-07-01

The improvement of Variable Air Volume (VAV) system performance is one of several attempts being made to minimize the high energy use associated with the operation of heating, ventilation and air conditioning (HVAC) systems. A Simplified Optimization Process (SOP) comprised of controller set point strategies and a simplified VAV model was presented in this paper. The aim of the SOP was to determine supply set points. The advantage of the SOP over previous methods was that it did not require a detailed VAV model and optimization program. In addition, the monitored data for representative local-loop control can be checked on-line, after which controller set points can be updated in order to ensure proper operation by opting for real situations with minimum energy use. The SOP was validated using existing monitoring data and a model of an existing VAV system. Energy use simulations were compared to that of the existing VAV system. At each simulation step, 3 controller set point values were proposed and studied using the VAV model in order to select a value for each point which corresponded to the best performance of the VAV system. Simplified VAV component models were presented. Strategies for controller set points were described, including zone air temperature, duct static pressure set points; chilled water supply set points and supply air temperature set points. Simplified optimization process calculations were presented. Results indicated that the SOP provided significant energy savings when applied to specific AHU systems. In a comparison with a Detailed Optimization Process (DOP), the SOP was capable of determining set points close to those obtained by the DOP. However, it was noted that the controller set points determined by the SOP need a certain amount of time to reach optimal values when outdoor conditions or thermal loads are significantly changed. It was suggested that this disadvantage could be overcome by the use of a dynamic incremental value, which

Accelerator optimization using a network control and acquisition system

International Nuclear Information System (INIS)

Geddes, Cameron G.R.; Catravas, P.E.; Faure, Jerome; Toth, Csaba; Tilborg, J. van; Leemans, Wim P.

2002-01-01

Accelerator optimization requires detailed study of many parameters, indicating the need for remote control and automated data acquisition systems. A control and data acquisition system based on a network of commodity PCs and applications with standards based inter-application communication is being built for the l'OASIS accelerator facility. This system allows synchronous acquisition of data at high (> 1 Hz) rates and remote control of the accelerator at low cost, allowing detailed study of the acceleration process

Optimal Control of Heterogeneous Systems with Endogenous Domain of Heterogeneity

International Nuclear Information System (INIS)

Belyakov, Anton O.; Tsachev, Tsvetomir; Veliov, Vladimir M.

2011-01-01

The paper deals with optimal control of heterogeneous systems, that is, families of controlled ODEs parameterized by a parameter running over a domain called domain of heterogeneity. The main novelty in the paper is that the domain of heterogeneity is endogenous: it may depend on the control and on the state of the system. This extension is crucial for several economic applications and turns out to rise interesting mathematical problems. A necessary optimality condition is derived, where one of the adjoint variables satisfies a differential inclusion (instead of equation) and the maximization of the Hamiltonian takes the form of “min-max”. As a consequence, a Pontryagin-type maximum principle is obtained under certain regularity conditions for the optimal control. A formula for the derivative of the objective function with respect to the control from L ∞ is presented together with a sufficient condition for its existence. A stylized economic example is investigated analytically and numerically.

Optimal feedback control of the forced van der Pol system

International Nuclear Information System (INIS)

Chagas, T.P.; Toledo, B.A.; Rempel, E.L.; Chian, A.C.-L.; Valdivia, J.A.

2012-01-01

A simple feedback control strategy for chaotic systems is investigated using the forced van der Pol system as an example. The strategy regards chaos control as an optimization problem, where the maximum magnitude Floquet multiplier of a target unstable periodic orbit (UPO) is used as a cost function that needs to be minimized. Thus, the method obtains the optimal control gain in terms of the stability of the target UPO. This strategy was recently proposed for the proportional feedback control (PFC) method. Here, it is extended to the highly popular delayed feedback control (DFC) method. Since the DFC method treats the system as a delay-differential equation whose phase space is infinite-dimensional, the characteristic multipliers are found through a truncation in the number of delayed states. Control of a target UPO is achieved for several values of the forcing amplitude. We compare the DFC and PFC methods in terms of stability of the controlled orbit, steady state error and control effort.

Optimizing data access in the LAMPF control system

International Nuclear Information System (INIS)

Schaller, S.C.; Corley, J.K.; Rose, P.A.

1985-01-01

The LAMPF control system data access software offers considerable power and flexibility to application programs through symbolic device naming and an emphasis on hardware independence. This paper discusses optimizations aimed at improving the performance of the data access software while retaining these capabilities. The only aspects of the optimizations visible to the application programs are ''vector devices'' and ''aggregate devices.'' A vector device accesses a set of hardware related data items through a single device name. Aggregate devices allow run-time optimization of references to groups of unrelated devices. Optimizations not visible on the application level include careful handling of: network message traffic; the sharing of global resources; and storage allocation

Distributed computer control system for reactor optimization

International Nuclear Information System (INIS)

Williams, A.H.

1983-01-01

At the Oldbury power station a prototype distributed computer control system has been installed. This system is designed to support research and development into improved reactor temperature control methods. This work will lead to the development and demonstration of new optimal control systems for improvement of plant efficiency and increase of generated output. The system can collect plant data from special test instrumentation connected to dedicated scanners and from the station's existing data processing system. The system can also, via distributed microprocessor-based interface units, make adjustments to the desired reactor channel gas exit temperatures. The existing control equipment will then adjust the height of control rods to maintain operation at these temperatures. The design of the distributed system is based on extensive experience with distributed systems for direct digital control, operator display and plant monitoring. The paper describes various aspects of this system, with particular emphasis on: (1) the hierarchal system structure; (2) the modular construction of the system to facilitate installation, commissioning and testing, and to reduce maintenance to module replacement; (3) the integration of the system into the station's existing data processing system; (4) distributed microprocessor-based interfaces to the reactor controls, with extensive security facilities implemented by hardware and software; (5) data transfer using point-to-point and bussed data links; (6) man-machine communication based on VDUs with computer input push-buttons and touch-sensitive screens; and (7) the use of a software system supporting a high-level engineer-orientated programming language, at all levels in the system, together with comprehensive data link management

Optimization and optimal control in automotive systems

CERN Document Server

Kolmanovsky, Ilya; Steinbuch, Maarten; Re, Luigi

2014-01-01

This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier  approaches, based on some degree of heuristics, to the use of  more and more common systematic methods. Even systematic methods can be developed and applied in a large number of forms so the text collects contributions from across the theory, methods and real-world automotive applications of optimization. Greater fuel economy, significant reductions in permissible emissions, new drivability requirements and the generally increasing complexity of automotive systems are among the criteria that the contributing authors set themselves to meet. In many cases multiple and often conflicting requirements give rise to multi-objective constrained optimization problems which are also considered. Some of these problems fall into the domain of the traditional multi-disciplinary optimization applie...

Weather and Climate Manipulation as an Optimal Control for Adaptive Dynamical Systems

Directory of Open Access Journals (Sweden)

Sergei A. Soldatenko

2017-01-01

Full Text Available The weather and climate manipulation is examined as an optimal control problem for the earth climate system, which is considered as a complex adaptive dynamical system. Weather and climate manipulations are actually amorphous operations. Since their objectives are usually formulated vaguely, the expected results are fairly unpredictable and uncertain. However, weather and climate modification is a purposeful process and, therefore, we can formulate operations to manipulate weather and climate as the optimization problem within the framework of the optimal control theory. The complexity of the earth’s climate system is discussed and illustrated using the simplified low-order coupled chaotic dynamical system. The necessary conditions of optimality are derived for the large-scale atmospheric dynamics. This confirms that even a relatively simplified control problem for the atmospheric dynamics requires significant efforts to obtain the solution.

CLFs-based optimization control for a class of constrained visual servoing systems.

Science.gov (United States)

Song, Xiulan; Miaomiao, Fu

2017-03-01

In this paper, we use the control Lyapunov function (CLF) technique to present an optimized visual servo control method for constrained eye-in-hand robot visual servoing systems. With the knowledge of camera intrinsic parameters and depth of target changes, visual servo control laws (i.e. translation speed) with adjustable parameters are derived by image point features and some known CLF of the visual servoing system. The Fibonacci method is employed to online compute the optimal value of those adjustable parameters, which yields an optimized control law to satisfy constraints of the visual servoing system. The Lyapunov's theorem and the properties of CLF are used to establish stability of the constrained visual servoing system in the closed-loop with the optimized control law. One merit of the presented method is that there is no requirement of online calculating the pseudo-inverse of the image Jacobian's matrix and the homography matrix. Simulation and experimental results illustrated the effectiveness of the method proposed here. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal Control of Solar Heating System

KAUST Repository

Huang, Bin-Juine

2017-02-21

Forced-circulation solar heating system has been widely used in process and domestic heating applications. Additional pumping power is required to circulate the water through the collectors to absorb the solar energy. The present study intends to develop a maximum-power point tracking control (MPPT) to obtain the minimum pumping power consumption at an optimal heat collection. The net heat energy gain Qnet (= Qs − Wp/ηe) was found to be the cost function for MPPT. The step-up-step-down controller was used in the feedback design of MPPT. The field test results show that the pumping power is 89 W at Qs = 13.7 kW and IT = 892 W/m2. A very high electrical COP of the solar heating system (Qs/Wp = 153.8) is obtained.

Quasivelocities and Optimal Control for underactuated Mechanical Systems

International Nuclear Information System (INIS)

Colombo, L.; Martin de Diego, D.

2010-01-01

This paper is concerned with the application of the theory of quasivelocities for optimal control for underactuated mechanical systems. Using this theory, we convert the original problem in a variational second-order lagrangian system subjected to constraints. The equations of motion are geometrically derived using an adaptation of the classical Skinner and Rusk formalism.

Energy evaluation of optimal control strategies for central VWV chiller systems

International Nuclear Information System (INIS)

Jin Xinqiao; Du Zhimin; Xiao Xiaokun

2007-01-01

Under various conditions, the actual load of the heating, ventilation and air conditioning (HVAC) systems is less than it is originally designed in most operation periods. To save energy and to optimize the controls for chilling systems, the performance of variable water volume (VWV) systems and characteristics of control systems are analyzed, and three strategies are presented and tested based on simulation in this paper. Energy evaluation for the three strategies shows that they can save energy to some extent, and there is potential remained. To minimize the energy consumption of chilling system, the setpoints of controls of supply chilled water temperature and supply head of secondary pump should be optimized simultaneously

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.)

Control parameter optimization for AP1000 reactor using Particle Swarm Optimization

International Nuclear Information System (INIS)

Wang, Pengfei; Wan, Jiashuang; Luo, Run; Zhao, Fuyu; Wei, Xinyu

2016-01-01

Highlights: • The PSO algorithm is applied for control parameter optimization of AP1000 reactor. • Key parameters of the MSHIM control system are optimized. • Optimization results are evaluated though simulations and quantitative analysis. - Abstract: The advanced mechanical shim (MSHIM) core control strategy is implemented in the AP1000 reactor for core reactivity and axial power distribution control simultaneously. The MSHIM core control system can provide superior reactor control capabilities via automatic rod control only. This enables the AP1000 to perform power change operations automatically without the soluble boron concentration adjustments. In this paper, the Particle Swarm Optimization (PSO) algorithm has been applied for the parameter optimization of the MSHIM control system to acquire better reactor control performance for AP1000. System requirements such as power control performance, control bank movement and AO control constraints are reflected in the objective function. Dynamic simulations are performed based on an AP1000 reactor simulation platform in each iteration of the optimization process to calculate the fitness values of particles in the swarm. The simulation platform is developed in Matlab/Simulink environment with implementation of a nodal core model and the MSHIM control strategy. Based on the simulation platform, the typical 10% step load decrease transient from 100% to 90% full power is simulated and the objective function used for control parameter tuning is directly incorporated in the simulation results. With successful implementation of the PSO algorithm in the control parameter optimization of AP1000 reactor, four key parameters of the MSHIM control system are optimized. It has been demonstrated by the calculation results that the optimized MSHIM control system parameters can improve the reactor power control capability and reduce the control rod movement without compromising AO control. Therefore, the PSO based optimization

Optimization and Control of Bilinear Systems Theory, Algorithms, and Applications

CERN Document Server

Pardalos, Panos M

2008-01-01

Covers developments in bilinear systems theory Focuses on the control of open physical processes functioning in a non-equilibrium mode Emphasis is on three primary disciplines: modern differential geometry, control of dynamical systems, and optimization theory Includes applications to the fields of quantum and molecular computing, control of physical processes, biophysics, superconducting magnetism, and physical information science

Soft Computing Optimizer For Intelligent Control Systems Design: The Structure And Applications

Directory of Open Access Journals (Sweden)

Sergey A. Panfilov

2003-10-01

Full Text Available Soft Computing Optimizer (SCO as a new software tool for design of robust intelligent control systems is described. It is based on the hybrid methodology of soft computing and stochastic simulation. It uses as an input the measured or simulated data about the modeled system. SCO is used to design an optimal fuzzy inference system, which approximates a random behavior of control object with the certain accuracy. The task of the fuzzy inference system construction is reduced to the subtasks such as forming of the linguistic variables for each input and output variable, creation of rule data base, optimization of rule data base and refinement of the parameters of the membership functions. Each task by the corresponding genetic algorithm (with an appropriate fitness function is solved. The result of SCO application is the design of Knowledge Base of a Fuzzy Controller, which contains the value information about developed fuzzy inference system. Such value information can be downloaded into the actual fuzzy controller to perform online fuzzy control. Simulations results of robust fuzzy control of nonlinear dynamic systems and experimental results of application on automotive semi-active suspension control are demonstrated.

Mathematical modelling and quality indices optimization of automatic control systems of reactor facility

International Nuclear Information System (INIS)

Severin, V.P.

2007-01-01

The mathematical modeling of automatic control systems of reactor facility WWER-1000 with various regulator types is considered. The linear and nonlinear models of neutron power control systems of nuclear reactor WWER-1000 with various group numbers of delayed neutrons are designed. The results of optimization of direct quality indexes of neutron power control systems of nuclear reactor WWER-1000 are designed. The identification and optimization of level control systems with various regulator types of steam generator are executed

Overlapping quadratic optimal control of linear time-varying commutative systems

Czech Academy of Sciences Publication Activity Database

Bakule, Lubomír; Rodellar, J.; Rossell, J. M.

2002-01-01

Roč. 40, č. 5 (2002), s. 1611-1627 ISSN 0363-0129 R&D Projects: GA AV ČR IAA2075802 Institutional research plan: CEZ:AV0Z1075907 Keywords : overlapping * optimal control * linear time-varying systems Subject RIV: BC - Control Systems Theory Impact factor: 1.441, year: 2002

Design of an Optimal Preview Controller for Linear Discrete-Time Descriptor Noncausal Multirate Systems

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Mengjuan Cao

2014-01-01

Full Text Available The linear discrete-time descriptor noncausal multirate system is considered for the presentation of a new design approach for optimal preview control. First, according to the characteristics of causal controllability and causal observability, the descriptor noncausal system is constructed into a descriptor causal closed-loop system. Second, by using the characteristics of the causal system and elementary transformation, the descriptor causal closed-loop system is transformed into a normal system. Then, taking advantage of the discrete lifting technique, the normal multirate system is converted to a single-rate system. By making use of the standard preview control method, we construct the descriptor augmented error system. The quadratic performance index for the multirate system is given, which can be changed into one for the single-rate system. In addition, a new single-rate system is obtained, the optimal control law of which is given. Returning to the original system, the optimal preview controller for linear discrete-time descriptor noncausal multirate systems is derived. The stabilizability and detectability of the lifted single-rate system are discussed in detail. The optimal preview control design techniques are illustrated by simulation results for a simple example.

Optimal control

CERN Document Server

Aschepkov, Leonid T; Kim, Taekyun; Agarwal, Ravi P

2016-01-01

This book is based on lectures from a one-year course at the Far Eastern Federal University (Vladivostok, Russia) as well as on workshops on optimal control offered to students at various mathematical departments at the university level. The main themes of the theory of linear and nonlinear systems are considered, including the basic problem of establishing the necessary and sufficient conditions of optimal processes. In the first part of the course, the theory of linear control systems is constructed on the basis of the separation theorem and the concept of a reachability set. The authors prove the closure of a reachability set in the class of piecewise continuous controls, and the problems of controllability, observability, identification, performance and terminal control are also considered. The second part of the course is devoted to nonlinear control systems. Using the method of variations and the Lagrange multipliers rule of nonlinear problems, the authors prove the Pontryagin maximum principle for prob...

Evolutionary Computing for Intelligent Power System Optimization and Control

DEFF Research Database (Denmark)

This new book focuses on how evolutionary computing techniques benefit engineering research and development tasks by converting practical problems of growing complexities into simple formulations, thus largely reducing development efforts. This book begins with an overview of the optimization the...... theory and modern evolutionary computing techniques, and goes on to cover specific applications of evolutionary computing to power system optimization and control problems....

Applied optimal control theory of distributed systems

CERN Document Server

Lurie, K A

1993-01-01

This book represents an extended and substantially revised version of my earlierbook, Optimal Control in Problems ofMathematical Physics,originally published in Russian in 1975. About 60% of the text has been completely revised and major additions have been included which have produced a practically new text. My aim was to modernize the presentation but also to preserve the original results, some of which are little known to a Western reader. The idea of composites, which is the core of the modern theory of optimization, was initiated in the early seventies. The reader will find here its implementation in the problem of optimal conductivity distribution in an MHD-generatorchannel flow.Sincethen it has emergedinto an extensive theory which is undergoing a continuous development. The book does not pretend to be a textbook, neither does it offer a systematic presentation of the theory. Rather, it reflects a concept which I consider as fundamental in the modern approach to optimization of dis­ tributed systems. ...

Chaos synchronization and chaotization of complex chaotic systems in series form by optimal control

International Nuclear Information System (INIS)

Ge Zhengming; Yang, C.-H.

2009-01-01

By the method of quadratic optimum control, a quadratic optimal regulator is used for synchronizing two complex chaotic systems in series form. By this method the least error with less control energy is achieved, and the optimization on both energy and error is realized synthetically. The simulation results of two Quantum-CNN chaos systems in series form prove the effectiveness of this method. Finally, chaotization of the system is given by optimal control.

Optimal strategy analysis based on robust predictive control for inventory system with random demand

Science.gov (United States)

Saputra, Aditya; Widowati, Sutrisno

2017-12-01

In this paper, the optimal strategy for a single product single supplier inventory system with random demand is analyzed by using robust predictive control with additive random parameter. We formulate the dynamical system of this system as a linear state space with additive random parameter. To determine and analyze the optimal strategy for the given inventory system, we use robust predictive control approach which gives the optimal strategy i.e. the optimal product volume that should be purchased from the supplier for each time period so that the expected cost is minimal. A numerical simulation is performed with some generated random inventory data. We simulate in MATLAB software where the inventory level must be controlled as close as possible to a set point decided by us. From the results, robust predictive control model provides the optimal strategy i.e. the optimal product volume that should be purchased and the inventory level was followed the given set point.

Digital control system of a steam generator water level by LQG optimal method

International Nuclear Information System (INIS)

Lee, Yoon Joon

1993-01-01

A digital control system for the steam generator water level control is developed using LQG optimal design method. To describe the more realistic situaton, a feedwater valve actuator is assumed to be of the first order lagger and is included in the overall control system. By composing the digital control circuit in such a way that the overall control system consists of two sub-systems of feedwater station and feedback loop digital controller, the design procedure is divided into two independent steps. The feedwater station system is described in the error dynamics of an ordinary regulator system. The optimal gains are obtained by LQ method which imposes the constraints of the feedwater valve motion as well as on the output deviations. Developed also is a Kalman observer on account of the flow measurement uncertainty at low power. Then a digital controller on the feedback loop is designed so that the system maintains the same stability margins for all power ranges. The simulation results show thst the optimal digital system has a good control characteristics despite the adverse dynamics of a steam generator at low power. (Author)

PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems

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Arturo Y. Jaen-Cuellar

2013-09-01

Full Text Available Performance improvement is the main goal of the study of PID control and much research has been conducted for this purpose. The PID filter is implemented in almost all industrial processes because of its well-known beneficial features. In general, the whole system's performance strongly depends on the controller's efficiency and hence the tuning process plays a key role in the system's behaviour. In this work, the servo systems will be analysed, specifically the positioning control systems. Among the existent tuning methods, the Gain-Phase Margin method based on Frequency Response analysis is the most adequate for controller tuning in positioning control systems. Nevertheless, this method can be improved by integrating an optimization technique. The novelty of this work is the development of a new methodology for PID control tuning by coupling the Gain-Phase Margin method with the Genetic Algorithms in which the micro-population concept and adaptive mutation probability are applied. Simulations using a positioning system model in MATLAB and experimental tests in two CNC machines and an industrial robot are carried out in order to show the effectiveness of the proposal. The obtained results are compared with both the classical Gain-Phase Margin tuning and with a recent PID controller optimization using Genetic Algorithms based on real codification. The three methodologies are implemented using software.

Event-Triggered Distributed Approximate Optimal State and Output Control of Affine Nonlinear Interconnected Systems.

Science.gov (United States)

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-06-08

This paper presents an approximate optimal distributed control scheme for a known interconnected system composed of input affine nonlinear subsystems using event-triggered state and output feedback via a novel hybrid learning scheme. First, the cost function for the overall system is redefined as the sum of cost functions of individual subsystems. A distributed optimal control policy for the interconnected system is developed using the optimal value function of each subsystem. To generate the optimal control policy, forward-in-time, neural networks are employed to reconstruct the unknown optimal value function at each subsystem online. In order to retain the advantages of event-triggered feedback for an adaptive optimal controller, a novel hybrid learning scheme is proposed to reduce the convergence time for the learning algorithm. The development is based on the observation that, in the event-triggered feedback, the sampling instants are dynamic and results in variable interevent time. To relax the requirement of entire state measurements, an extended nonlinear observer is designed at each subsystem to recover the system internal states from the measurable feedback. Using a Lyapunov-based analysis, it is demonstrated that the system states and the observer errors remain locally uniformly ultimately bounded and the control policy converges to a neighborhood of the optimal policy. Simulation results are presented to demonstrate the performance of the developed controller.

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...

Time-optimal control of infinite order distributed parabolic systems involving time lags

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G.M. Bahaa

2014-06-01

Full Text Available A time-optimal control problem for linear infinite order distributed parabolic systems involving constant time lags appear both in the state equation and in the boundary condition is presented. Some particular properties of the optimal control are discussed.

On Optimal Feedback Control for Stationary Linear Systems

International Nuclear Information System (INIS)

Russell, David L.

2010-01-01

We study linear-quadratic optimal control problems for finite dimensional stationary linear systems AX+BU=Z with output Y=CX+DU from the viewpoint of linear feedback solution. We interpret solutions in relation to system robustness with respect to disturbances Z and relate them to nonlinear matrix equations of Riccati type and eigenvalue-eigenvector problems for the corresponding Hamiltonian system. Examples are included along with an indication of extensions to continuous, i.e., infinite dimensional, systems, primarily of elliptic type.

An optimization strategy for the control of small capacity heat pump integrated air-conditioning system

International Nuclear Information System (INIS)

Gao, Jiajia; Huang, Gongsheng; Xu, Xinhua

2016-01-01

Highlights: • An optimization strategy for a small-scale air-conditioning system is developed. • The optimization strategy aims at optimizing the overall system energy consumption. • The strategy may guarantee the robust control of the space air temperature. • The performance of the optimization strategy was tested on a simulation platform. - Abstract: This paper studies the optimization of a small-scale central air-conditioning system, in which the cooling is provided by a ground source heat pump (GSHP) equipped with an on/off capacity control. The optimization strategy aims to optimize the overall system energy consumption and simultaneously guarantee the robustness of the space air temperature control without violating the allowed GSHP maximum start-ups number per hour specified by customers. The set-point of the chilled water return temperature and the width of the water temperature control band are used as the decision variables for the optimization. The performance of the proposed strategy was tested on a simulation platform. Results show that the optimization strategy can save the energy consumption by 9.59% in a typical spring day and 2.97% in a typical summer day. Meanwhile it is able to enhance the space air temperature control robustness when compared with a basic control strategy without optimization.

ADEX optimized adaptive controllers and systems from research to industrial practice

CERN Document Server

Martín-Sánchez, Juan M

2015-01-01

This book is a didactic explanation of the developments of predictive, adaptive predictive and optimized adaptive control, including the latest methodology of adaptive predictive expert (ADEX) control, and their practical applications. It is focused on the stability perspective, used in the introduction of these methodologies, and is divided into six parts, with exercises and real-time simulations provided for the reader as appropriate. ADEX Optimized Adaptive Controllers and Systems begins with the conceptual and intuitive knowledge of the technology and derives the stability conditions to be verified by the driver block and the adaptive mechanism of the optimized adaptive controller to guarantee achievement of desired control performance. The second and third parts are centered on the design of the driver block and adaptive mechanism, which verify these stability conditions. The authors then proceed to detail the stability theory that supports predictive, adaptive predictive and optimized adaptive control m...

Optimal Force Control of Vibro-Impact Systems for Autonomous Drilling Applications

Science.gov (United States)

Aldrich, Jack B.; Okon, Avi B.

2012-01-01

The need to maintain optimal energy efficiency is critical during the drilling operations performed on future and current planetary rover missions (see figure). Specifically, this innovation seeks to solve the following problem. Given a spring-loaded percussive drill driven by a voice-coil motor, one needs to determine the optimal input voltage waveform (periodic function) and the optimal hammering period that minimizes the dissipated energy, while ensuring that the hammer-to-rock impacts are made with sufficient (user-defined) impact velocity (or impact energy). To solve this problem, it was first observed that when voice-coil-actuated percussive drills are driven at high power, it is of paramount importance to ensure that the electrical current of the device remains in phase with the velocity of the hammer. Otherwise, negative work is performed and the drill experiences a loss of performance (i.e., reduced impact energy) and an increase in Joule heating (i.e., reduction in energy efficiency). This observation has motivated many drilling products to incorporate the standard bang-bang control approach for driving their percussive drills. However, the bang-bang control approach is significantly less efficient than the optimal energy-efficient control approach solved herein. To obtain this solution, the standard tools of classical optimal control theory were applied. It is worth noting that these tools inherently require the solution of a two-point boundary value problem (TPBVP), i.e., a system of differential equations where half the equations have unknown boundary conditions. Typically, the TPBVP is impossible to solve analytically for high-dimensional dynamic systems. However, for the case of the spring-loaded vibro-impactor, this approach yields the exact optimal control solution as the sum of four analytic functions whose coefficients are determined using a simple, easy-to-implement algorithm. Once the optimal control waveform is determined, it can be used

Energy Center Structure Optimization by using Smart Technologies in Process Control System

Science.gov (United States)

Shilkina, Svetlana V.

2018-03-01

The article deals with practical application of fuzzy logic methods in process control systems. A control object - agroindustrial greenhouse complex, which includes its own energy center - is considered. The paper analyzes object power supply options taking into account connection to external power grids and/or installation of own power generating equipment with various layouts. The main problem of a greenhouse facility basic process is extremely uneven power consumption, which forces to purchase redundant generating equipment idling most of the time, which quite negatively affects project profitability. Energy center structure optimization is largely based on solving the object process control system construction issue. To cut investor’s costs it was proposed to optimize power consumption by building an energy-saving production control system based on a fuzzy logic controller. The developed algorithm of automated process control system functioning ensured more even electric and thermal energy consumption, allowed to propose construction of the object energy center with a smaller number of units due to their more even utilization. As a result, it is shown how practical use of microclimate parameters fuzzy control system during object functioning leads to optimization of agroindustrial complex energy facility structure, which contributes to a significant reduction in object construction and operation costs.

On robust control of uncertain chaotic systems: a sliding-mode synthesis via chaotic optimization

International Nuclear Information System (INIS)

Lu Zhao; Shieh Leangsan; Chen GuanRong

2003-01-01

This paper presents a novel Lyapunov-based control approach which utilizes a Lyapunov function of the nominal plant for robust tracking control of general multi-input uncertain nonlinear systems. The difficulty of constructing a control Lyapunov function is alleviated by means of predefining an optimal sliding mode. The conventional schemes for constructing sliding modes of nonlinear systems stipulate that the system of interest is canonical-transformable or feedback-linearizable. An innovative approach that exploits a chaotic optimizing algorithm is developed thereby obtaining the optimal sliding manifold for the control purpose. Simulations on the uncertain chaotic Chen's system illustrate the effectiveness of the proposed approach

Optimal control of dissipative nonlinear dynamical systems with triggers of coupled singularities

International Nuclear Information System (INIS)

Hedrih, K

2008-01-01

This paper analyses the controllability of motion of nonconservative nonlinear dynamical systems in which triggers of coupled singularities exist or appear. It is shown that the phase plane method is useful for the analysis of nonlinear dynamics of nonconservative systems with one degree of freedom of control strategies and also shows the way it can be used for controlling the relative motion in rheonomic systems having equivalent scleronomic conservative or nonconservative system For the system with one generalized coordinate described by nonlinear differential equation of nonlinear dynamics with trigger of coupled singularities, the functions of system potential energy and conservative force must satisfy some conditions defined by a Theorem on the existence of a trigger of coupled singularities and the separatrix in the form of 'an open a spiral form' of number eight. Task of the defined dynamical nonconservative system optimal control is: by using controlling force acting to the system, transfer initial state of the nonlinear dynamics of the system into the final state of the nonlinear dynamics in the minimal time for that optimal control task

Optimal control of dissipative nonlinear dynamical systems with triggers of coupled singularities

Science.gov (United States)

Stevanović Hedrih, K.

2008-02-01

This paper analyses the controllability of motion of nonconservative nonlinear dynamical systems in which triggers of coupled singularities exist or appear. It is shown that the phase plane method is useful for the analysis of nonlinear dynamics of nonconservative systems with one degree of freedom of control strategies and also shows the way it can be used for controlling the relative motion in rheonomic systems having equivalent scleronomic conservative or nonconservative system For the system with one generalized coordinate described by nonlinear differential equation of nonlinear dynamics with trigger of coupled singularities, the functions of system potential energy and conservative force must satisfy some conditions defined by a Theorem on the existence of a trigger of coupled singularities and the separatrix in the form of "an open a spiral form" of number eight. Task of the defined dynamical nonconservative system optimal control is: by using controlling force acting to the system, transfer initial state of the nonlinear dynamics of the system into the final state of the nonlinear dynamics in the minimal time for that optimal control task

Fractional Order Controller Designing with Firefly Algorithm and Parameter Optimization for Hydroturbine Governing System

Directory of Open Access Journals (Sweden)

Li Junyi

2015-01-01

Full Text Available A fractional order PID (FOPID controller, which is suitable for control system designing for being insensitive to the variation in system parameter, is proposed for hydroturbine governing system in the paper. The simultaneous optimization for several parameters of controller, that is, Ki, Kd, Kp, λ, and μ, is done by a recently developed metaheuristic nature-inspired algorithm, namely, the firefly algorithm (FA, for the first time, where the selecting, moving, attractiveness behavior between fireflies and updating of brightness, and decision range are studied in detail to simulate the optimization process. Investigation clearly reveals the advantages of the FOPID controller over the integer controllers in terms of reduced oscillations and settling time. The present work also explores the superiority of FA based optimization technique in finding optimal parameters of the controller. Further, convergence characteristics of the FA are compared with optimum integer order PID (IOPID controller to justify its efficiency. What is more, analysis confirms the robustness of FOPID controller under isolated load operation conditions.

Ship Lock Control System Optimization using GA, PSO and ABC: A Comparative Review

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Željko Kanović

2014-02-01

Full Text Available This paper presents the comparison of some well-known global optimization techniques in optimization of an expert system controlling a ship locking process. The purpose of the comparison is to find the best algorithm for optimization of membership function parameters of fuzzy expert system for the ship lock control. Optimization was conducted in order to achieve better results in local distribution of ship arrivals, i.e. shorter waiting times for ships and less empty lockages. Particle swarm optimization, artificial bee colony optimization and genetic algorithm were compared. The results shown in this paper confirmed that all these procedures show similar results and provide overall improvement of ship lock operation performance, which speaks in favour of their application in similar transportation problem optimization.

Optimization Control of Bidirectional Cascaded DC-AC Converter Systems

DEFF Research Database (Denmark)

Tian, Yanjun

in bidirectional cascaded converter. This research work analyses the control strategies based on the topology of dual active bridges converter cascaded with a three phase inverter. It firstly proposed a dc link voltage and active power coordinative control method for this cascaded topology, and it can reduce dc....... The connections of the renewable energy sources to the power system are mostly through the power electronic converters. Moreover, for high controllability and flexibility, power electronic devices are gradually acting as the interface between different networks in power systems, promoting conventional power...... the bidirectional power flow in the distribution level of power systems. Therefore direct contact of converters introduces significant uncertainties to power system, especially for the stability and reliability. This dissertation studies the optimization control of the two stages directly connected converters...

Optimal gravitational search algorithm for automatic generation control of interconnected power systems

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Rabindra Kumar Sahu

2014-09-01

Full Text Available An attempt is made for the effective application of Gravitational Search Algorithm (GSA to optimize PI/PIDF controller parameters in Automatic Generation Control (AGC of interconnected power systems. Initially, comparison of several conventional objective functions reveals that ITAE yields better system performance. Then, the parameters of GSA technique are properly tuned and the GSA control parameters are proposed. The superiority of the proposed approach is demonstrated by comparing the results of some recently published techniques such as Differential Evolution (DE, Bacteria Foraging Optimization Algorithm (BFOA and Genetic Algorithm (GA. Additionally, sensitivity analysis is carried out that demonstrates the robustness of the optimized controller parameters to wide variations in operating loading condition and time constants of speed governor, turbine, tie-line power. Finally, the proposed approach is extended to a more realistic power system model by considering the physical constraints such as reheat turbine, Generation Rate Constraint (GRC and Governor Dead Band nonlinearity.

A Study on the Analysis and Optimal Control of Nonlinear Systems via Walsh Function

Energy Technology Data Exchange (ETDEWEB)

Kim, Jin Tae; Kim, Tai Hoon; Ahn, Doo Soo [Sungkyunkwan University (Korea); Lee, Myung Kyu [Kyungsung University (Korea)

2000-07-01

This paper presents the new adaptive optimal scheme for the nonlinear systems, which is based on the Picard's iterative approximation and fast Walsh transform. It is well known that the Walsh function approach method is very difficult to apply for the analysis and optimal control of nonlinear systems. However, these problems can be easily solved by the improvement of the previous adaptive optimal scheme. The proposes method is easily applicable to the analysis and optimal control of nonlinear systems. (author). 15 refs., 6 figs., 1 tab.

Optimal control of a variable spin speed CMG system for space vehicles. [Control Moment Gyros

Science.gov (United States)

Liu, T. C.; Chubb, W. B.; Seltzer, S. M.; Thompson, Z.

1973-01-01

Many future NASA programs require very high accurate pointing stability. These pointing requirements are well beyond anything attempted to date. This paper suggests a control system which has the capability of meeting these requirements. An optimal control law for the suggested system is specified. However, since no direct method of solution is known for this complicated system, a computation technique using successive approximations is used to develop the required solution. The method of calculus of variations is applied for estimating the changes of index of performance as well as those constraints of inequality of state variables and terminal conditions. Thus, an algorithm is obtained by the steepest descent method and/or conjugate gradient method. Numerical examples are given to show the optimal controls.

Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay.

Science.gov (United States)

Pan, Indranil; Das, Saptarshi; Gupta, Amitava

2011-01-01

An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal fault-tolerant control strategy of a solid oxide fuel cell system

Science.gov (United States)

Wu, Xiaojuan; Gao, Danhui

2017-10-01

For solid oxide fuel cell (SOFC) development, load tracking, heat management, air excess ratio constraint, high efficiency, low cost and fault diagnosis are six key issues. However, no literature studies the control techniques combining optimization and fault diagnosis for the SOFC system. An optimal fault-tolerant control strategy is presented in this paper, which involves four parts: a fault diagnosis module, a switching module, two backup optimizers and a controller loop. The fault diagnosis part is presented to identify the SOFC current fault type, and the switching module is used to select the appropriate backup optimizer based on the diagnosis result. NSGA-II and TOPSIS are employed to design the two backup optimizers under normal and air compressor fault states. PID algorithm is proposed to design the control loop, which includes a power tracking controller, an anode inlet temperature controller, a cathode inlet temperature controller and an air excess ratio controller. The simulation results show the proposed optimal fault-tolerant control method can track the power, temperature and air excess ratio at the desired values, simultaneously achieving the maximum efficiency and the minimum unit cost in the case of SOFC normal and even in the air compressor fault.

Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

Science.gov (United States)

Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

2018-06-01

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

Connection between optimal control theory and adiabatic-passage techniques in quantum systems

Science.gov (United States)

Assémat, E.; Sugny, D.

2012-08-01

This work explores the relationship between optimal control theory and adiabatic passage techniques in quantum systems. The study is based on a geometric analysis of the Hamiltonian dynamics constructed from Pontryagin's maximum principle. In a three-level quantum system, we show that the stimulated Raman adiabatic passage technique can be associated to a peculiar Hamiltonian singularity. One deduces that the adiabatic pulse is solution of the optimal control problem only for a specific cost functional. This analysis is extended to the case of a four-level quantum system.

Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach

International Nuclear Information System (INIS)

Santos Coelho, Leandro dos

2009-01-01

Despite the popularity, the tuning aspect of proportional-integral-derivative (PID) controllers is a challenge for researchers and plant operators. Various controllers tuning methodologies have been proposed in the literature such as auto-tuning, self-tuning, pattern recognition, artificial intelligence, and optimization methods. Chaotic optimization algorithms as an emergent method of global optimization have attracted much attention in engineering applications. Chaotic optimization algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from local optimum, is a promising tool for engineering applications. In this paper, a tuning method for determining the parameters of PID control for an automatic regulator voltage (AVR) system using a chaotic optimization approach based on Lozi map is proposed. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed chaotic optimization introduces chaos mapping using Lozi map chaotic sequences which increases its convergence rate and resulting precision. Simulation results are promising and show the effectiveness of the proposed approach. Numerical simulations based on proposed PID control of an AVR system for nominal system parameters and step reference voltage input demonstrate the good performance of chaotic optimization.

Emergency strategy optimization for the environmental control system in manned spacecraft

Science.gov (United States)

Li, Guoxiang; Pang, Liping; Liu, Meng; Fang, Yufeng; Zhang, Helin

2018-02-01

It is very important for a manned environmental control system (ECS) to be able to reconfigure its operation strategy in emergency conditions. In this article, a multi-objective optimization is established to design the optimal emergency strategy for an ECS in an insufficient power supply condition. The maximum ECS lifetime and the minimum power consumption are chosen as the optimization objectives. Some adjustable key variables are chosen as the optimization variables, which finally represent the reconfigured emergency strategy. The non-dominated sorting genetic algorithm-II is adopted to solve this multi-objective optimization problem. Optimization processes are conducted at four different carbon dioxide partial pressure control levels. The study results show that the Pareto-optimal frontiers obtained from this multi-objective optimization can represent the relationship between the lifetime and the power consumption of the ECS. Hence, the preferred emergency operation strategy can be recommended for situations when there is suddenly insufficient power.

Optimality based repetitive controller design for track-following servo system of optical disk drives.

Science.gov (United States)

Chen, Wentao; Zhang, Weidong

2009-10-01

In an optical disk drive servo system, to attenuate the external periodic disturbances induced by inevitable disk eccentricity, repetitive control has been used successfully. The performance of a repetitive controller greatly depends on the bandwidth of the low-pass filter included in the repetitive controller. However, owing to the plant uncertainty and system stability, it is difficult to maximize the bandwidth of the low-pass filter. In this paper, we propose an optimality based repetitive controller design method for the track-following servo system with norm-bounded uncertainties. By embedding a lead compensator in the repetitive controller, both the system gain at periodic signal's harmonics and the bandwidth of the low-pass filter are greatly increased. The optimal values of the repetitive controller's parameters are obtained by solving two optimization problems. Simulation and experimental results are provided to illustrate the effectiveness of the proposed method.

Adaptive critic designs for optimal control of uncertain nonlinear systems with unmatched interconnections.

Science.gov (United States)

Yang, Xiong; He, Haibo

2018-05-26

In this paper, we develop a novel optimal control strategy for a class of uncertain nonlinear systems with unmatched interconnections. To begin with, we present a stabilizing feedback controller for the interconnected nonlinear systems by modifying an array of optimal control laws of auxiliary subsystems. We also prove that this feedback controller ensures a specified cost function to achieve optimality. Then, under the framework of adaptive critic designs, we use critic networks to solve the Hamilton-Jacobi-Bellman equations associated with auxiliary subsystem optimal control laws. The critic network weights are tuned through the gradient descent method combined with an additional stabilizing term. By using the newly established weight tuning rules, we no longer need the initial admissible control condition. In addition, we demonstrate that all signals in the closed-loop auxiliary subsystems are stable in the sense of uniform ultimate boundedness by using classic Lyapunov techniques. Finally, we provide an interconnected nonlinear plant to validate the present control scheme. Copyright © 2018 Elsevier Ltd. All rights reserved.

Design of Simulation Product for Stability of Electric Power System Using Power System Stabilizer and Optimal Control

Science.gov (United States)

Junaidi, Agus; Hamid, K. Abdul

2018-03-01

This paper will discuss the use of optimal control and Power System Stabilizer (PSS) in improving the oscillation of electric power system. Oscillations in the electric power system can occur due to the sudden release of the load (Switcing-Off). The oscillation of an unstable system for a long time causes the equipment to work in an interruption. To overcome this problem, a control device is required that can work effectively in repairing the oscillation. The power system is modeled from the Single Machine Infinite Bus Model (SMIB). The state space equation is used to mathematically model SMIB. SMIB system which is a plant will be formed togetherness state variables (State-Space), using riccati equation then determined the optimal gain as controller plant. Plant is also controlled by Power Stabilizer System using phase compensation method. Using Matlab Software based simulation will be observed response of rotor speed change and rotor angle change for each of the two controlling methods. Simulation results using the Simulink-MATLAB 6.1 software will compare the analysis of the plant state in Open loop state and use the controller. The simulation response shows that the optimal control and PSS can improve the stability of the power system in terms of acceleration to achieve settling-time and Over Shoot improvement. From the results of both methods are able to improve system performance.

Efficient solution method for optimal control of nuclear systems

International Nuclear Information System (INIS)

Naser, J.A.; Chambre, P.L.

1981-01-01

To improve the utilization of existing fuel sources, the use of optimization techniques is becoming more important. A technique for solving systems of coupled ordinary differential equations with initial, boundary, and/or intermediate conditions is given. This method has a number of inherent advantages over existing techniques as well as being efficient in terms of computer time and space requirements. An example of computing the optimal control for a spatially dependent reactor model with and without temperature feedback is given. 10 refs

Quantum demolition filtering and optimal control of unstable systems.

Science.gov (United States)

Belavkin, V P

2012-11-28

A brief account of the quantum information dynamics and dynamical programming methods for optimal control of quantum unstable systems is given to both open loop and feedback control schemes corresponding respectively to deterministic and stochastic semi-Markov dynamics of stable or unstable systems. For the quantum feedback control scheme, we exploit the separation theorem of filtering and control aspects as in the usual case of quantum stable systems with non-demolition observation. This allows us to start with the Belavkin quantum filtering equation generalized to demolition observations and derive the generalized Hamilton-Jacobi-Bellman equation using standard arguments of classical control theory. This is equivalent to a Hamilton-Jacobi equation with an extra linear dissipative term if the control is restricted to Hamiltonian terms in the filtering equation. An unstable controlled qubit is considered as an example throughout the development of the formalism. Finally, we discuss optimum observation strategies to obtain a pure quantum qubit state from a mixed one.

Optimization of motion control laws for tether crawler or elevator systems

Science.gov (United States)

Swenson, Frank R.; Von Tiesenhausen, Georg

1988-01-01

Based on the proposal of a motion control law by Lorenzini (1987), a method is developed for optimizing motion control laws for tether crawler or elevator systems in terms of the performance measures of travel time, the smoothness of acceleration and deceleration, and the maximum values of velocity and acceleration. The Lorenzini motion control law, based on powers of the hyperbolic tangent function, is modified by the addition of a constant-velocity section, and this modified function is then optimized by parameter selections to minimize the peak acceleration value for a selected travel time or to minimize travel time for the selected peak values of velocity and acceleration. It is shown that the addition of a constant-velocity segment permits further optimization of the motion control law performance.

An Optimal Control Method for Maximizing the Efficiency of Direct Drive Ocean Wave Energy Extraction System

Science.gov (United States)

Chen, Zhongxian; Yu, Haitao; Wen, Cheng

2014-01-01

The goal of direct drive ocean wave energy extraction system is to convert ocean wave energy into electricity. The problem explored in this paper is the design and optimal control for the direct drive ocean wave energy extraction system. An optimal control method based on internal model proportion integration differentiation (IM-PID) is proposed in this paper though most of ocean wave energy extraction systems are optimized by the structure, weight, and material. With this control method, the heavy speed of outer heavy buoy of the energy extraction system is in resonance with incident wave, and the system efficiency is largely improved. Validity of the proposed optimal control method is verified in both regular and irregular ocean waves, and it is shown that IM-PID control method is optimal in that it maximizes the energy conversion efficiency. In addition, the anti-interference ability of IM-PID control method has been assessed, and the results show that the IM-PID control method has good robustness, high precision, and strong anti-interference ability. PMID:25152913

An optimal control method for maximizing the efficiency of direct drive ocean wave energy extraction system.

Science.gov (United States)

Chen, Zhongxian; Yu, Haitao; Wen, Cheng

2014-01-01

The goal of direct drive ocean wave energy extraction system is to convert ocean wave energy into electricity. The problem explored in this paper is the design and optimal control for the direct drive ocean wave energy extraction system. An optimal control method based on internal model proportion integration differentiation (IM-PID) is proposed in this paper though most of ocean wave energy extraction systems are optimized by the structure, weight, and material. With this control method, the heavy speed of outer heavy buoy of the energy extraction system is in resonance with incident wave, and the system efficiency is largely improved. Validity of the proposed optimal control method is verified in both regular and irregular ocean waves, and it is shown that IM-PID control method is optimal in that it maximizes the energy conversion efficiency. In addition, the anti-interference ability of IM-PID control method has been assessed, and the results show that the IM-PID control method has good robustness, high precision, and strong anti-interference ability.

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

Science.gov (United States)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Application of Minimum-time Optimal Control System in Buck-Boost Bi-linear Converters

Directory of Open Access Journals (Sweden)

S. M. M. Shariatmadar

2017-08-01

Full Text Available In this study, the theory of minimum-time optimal control system in buck-boost bi-linear converters is described, so that output voltage regulation is carried out within minimum time. For this purpose, the Pontryagin's Minimum Principle is applied to find optimal switching level applying minimum-time optimal control rules. The results revealed that by utilizing an optimal switching level instead of classical switching patterns, output voltage regulation will be carried out within minimum time. However, transient energy index of increased overvoltage significantly reduces in order to attain minimum time optimal control in reduced output load. The laboratory results were used in order to verify numerical simulations.

Optimized hardware design for the divertor remote handling control system

Energy Technology Data Exchange (ETDEWEB)

Saarinen, Hannu [Tampere University of Technology, Korkeakoulunkatu 6, 33720 Tampere (Finland)], E-mail: hannu.saarinen@tut.fi; Tiitinen, Juha; Aha, Liisa; Muhammad, Ali; Mattila, Jouni; Siuko, Mikko; Vilenius, Matti [Tampere University of Technology, Korkeakoulunkatu 6, 33720 Tampere (Finland); Jaervenpaeae, Jorma [VTT Systems Engineering, Tekniikankatu 1, 33720 Tampere (Finland); Irving, Mike; Damiani, Carlo; Semeraro, Luigi [Fusion for Energy, Josep Pla 2, Torres Diagonal Litoral B3, 08019 Barcelona (Spain)

2009-06-15

A key ITER maintenance activity is the exchange of the divertor cassettes. One of the major focuses of the EU Remote Handling (RH) programme has been the study and development of the remote handling equipment necessary for divertor exchange. The current major step in this programme involves the construction of a full scale physical test facility, namely DTP2 (Divertor Test Platform 2), in which to demonstrate and refine the RH equipment designs for ITER using prototypes. The major objective of the DTP2 project is the proof of concept studies of various RH devices, but is also important to define principles for standardizing control hardware and methods around the ITER maintenance equipment. This paper focuses on describing the control system hardware design optimization that is taking place at DTP2. Here there will be two RH movers, namely the Cassette Multifuctional Mover (CMM), Cassette Toroidal Mover (CTM) and assisting water hydraulic force feedback manipulators (WHMAN) located aboard each Mover. The idea here is to use common Real Time Operating Systems (RTOS), measurement and control IO-cards etc. for all maintenance devices and to standardize sensors and control components as much as possible. In this paper, new optimized DTP2 control system hardware design and some initial experimentation with the new DTP2 RH control system platform are presented. The proposed new approach is able to fulfil the functional requirements for both Mover and Manipulator control systems. Since the new control system hardware design has reduced architecture there are a number of benefits compared to the old approach. The simplified hardware solution enables the use of a single software development environment and a single communication protocol. This will result in easier maintainability of the software and hardware, less dependence on trained personnel, easier training of operators and hence reduced the development costs of ITER RH.

A comparison of the economic benefits of centralized and distributed model predictive control strategies for optimal and sub-optimal mine dewatering system designs

International Nuclear Information System (INIS)

Romero, Alberto; Millar, Dean; Carvalho, Monica; Maestre, José M.; Camacho, Eduardo F.

2015-01-01

Mine dewatering can represent up to 5% of the total energy demand of a mine, and is one of the mine systems that aim to guarantee safe operating conditions. As mines go deeper, dewatering pumping heads become bigger, potentially involving several lift stages. Greater depth does not only mean greater dewatering cost, but more complex systems that require more sophisticated control systems, especially if mine operators wish to gain benefits from demand response incentives that are becoming a routine part of electricity tariffs. This work explores a two stage economic optimization procedure of an underground mine dewatering system, comprising two lifting stages, each one including a pump station and a water reservoir. First, the system design is optimized considering hourly characteristic dewatering demands for twelve days, one day representing each month of the year to account for seasonal dewatering demand variations. This design optimization minimizes the annualized cost of the system, and therefore includes the investment costs in underground reservoirs. Reservoir size, as well as an hourly pumping operation plan are calculated for specific operating environments, defined by characteristic hourly electricity prices and water inflows (seepage and water use from production activities), at best known through historical observations for the previous year. There is no guarantee that the system design will remain optimal when it faces the water inflows and market determined electricity prices of the year ahead, or subsequent years ahead, because these remain unknown at design time. Consequently, the dewatering optimized system design is adopted subsequently as part of a Model Predictive Control (MPC) strategy that adaptively maintains optimality during the operations phase. Centralized, distributed and non-centralized MPC strategies are explored. Results show that the system can be reliably controlled using any of these control strategies proposed. Under the operating

Near-optimal order-reduced control for A/C (air-conditioning) system of EVs (electric vehicles)

International Nuclear Information System (INIS)

Chiu, Chien-Chin; Tsai, Nan-Chyuan; Lin, Chun-Chi

2014-01-01

This work is aimed to investigate the regulation problem for thermal comfortableness and propose control strategies for cabin environment of EVs (electric vehicles) by constructing a reduced-scale A/C (air-conditioning) system which mainly consists of two modules: ECB (environmental control box) and AHU (air-handling unit). Temperature and humidity in the ECB can be regulated by AHU via cooling, heating, mixing air streams and adjusting speed of fans. To synthesize the near-optimal controllers, the mathematical model for the system thermodynamics is developed by employing the equivalent lumped heat capacity approach, energy/mass conservation principle and the heat transfer theories. In addition, from the clustering pattern of system eigenvalues, the thermodynamics of the interested system can evidently be characterized by two-time-scale property. That is, the studied system can be decoupled into two subsystems, slow mode and fast mode, by singular perturbation technique. As to the optimal control strategies for EVs, by taking thermal comfortableness, humidity and energy consumption all into account, a series of optimal controllers is synthesized on the base of the order-reduced thermodynamic model. The feedback control loop for the experimental test rig is examined and realized by the aid of the control system development kit dSPACE DS1104 and the commercial software MATLAB/Simulink. To sum up, the intensive computer simulations and experimental results verify that the performance of the near-optimal order-reduced control law is almost as superior as that of standard LQR (Linear-Quadratic Regulator). - Highlights: • A reduced-scale test rig for A/C (air-conditioning) system to imitate the temperature/humidity of cabin in EV (electric vehicle) is constructed. • The non-linear thermodynamic model of A/C system can be decoupled by singular perturbation technique. • The temperature/humidity in cabin is regulated to the desired values by proposed optimal

Optimized Controller Design for a 12-Pulse Voltage Source Converter Based HVDC System

Science.gov (United States)

Agarwal, Ruchi; Singh, Sanjeev

2017-12-01

The paper proposes an optimized controller design scheme for power quality improvement in 12-pulse voltage source converter based high voltage direct current system. The proposed scheme is hybrid combination of golden section search and successive linear search method. The paper aims at reduction of current sensor and optimization of controller. The voltage and current controller parameters are selected for optimization due to its impact on power quality. The proposed algorithm for controller optimizes the objective function which is composed of current harmonic distortion, power factor, and DC voltage ripples. The detailed designs and modeling of the complete system are discussed and its simulation is carried out in MATLAB-Simulink environment. The obtained results are presented to demonstrate the effectiveness of the proposed scheme under different transient conditions such as load perturbation, non-linear load condition, voltage sag condition, and tapped load fault under one phase open condition at both points-of-common coupling.

Time-optimal control with finite bandwidth

Science.gov (United States)

Hirose, M.; Cappellaro, P.

2018-04-01

Time-optimal control theory provides recipes to achieve quantum operations with high fidelity and speed, as required in quantum technologies such as quantum sensing and computation. While technical advances have achieved the ultrastrong driving regime in many physical systems, these capabilities have yet to be fully exploited for the precise control of quantum systems, as other limitations, such as the generation of higher harmonics or the finite response time of the control apparatus, prevent the implementation of theoretical time-optimal control. Here we present a method to achieve time-optimal control of qubit systems that can take advantage of fast driving beyond the rotating wave approximation. We exploit results from time-optimal control theory to design driving protocols that can be implemented with realistic, finite-bandwidth control fields, and we find a relationship between bandwidth limitations and achievable control fidelity.

Optimization of the main control room habitability system in nuclear power plant

International Nuclear Information System (INIS)

Zheng Guanghui; Zhao Xinyan

2013-01-01

This article describes the optimization of main control room habitability system in nuclear power plant. It also describes the design shortage in terms of habitability in the main control room. Through modification and optimization, habitable conditions are met for personnel staying in the emergency area of the main control room for a period of time, with an aim to take accident intervention measures smoothly and reduce the accident loss and radioactive contamination as low as possible. (authors)

Optimal decoupling controllers revisited

Czech Academy of Sciences Publication Activity Database

Kučera, Vladimír

2013-01-01

Roč. 42, č. 1 (2013), s. 1-16 ISSN 0324-8569 R&D Projects: GA TA ČR(CZ) TE01020197 Institutional support: RVO:67985556 Keywords : linear systems * fractional representations * decoupling control lers * stabilizing control lers * optimal control lers Subject RIV: BC - Control Systems Theory

Optimal control of LQR for discrete time-varying systems with input delays

Science.gov (United States)

Yin, Yue-Zhu; Yang, Zhong-Lian; Yin, Zhi-Xiang; Xu, Feng

2018-04-01

In this work, we consider the optimal control problem of linear quadratic regulation for discrete time-variant systems with single input and multiple input delays. An innovative and simple method to derive the optimal controller is given. The studied problem is first equivalently converted into a problem subject to a constraint condition. Last, with the established duality, the problem is transformed into a static mathematical optimisation problem without input delays. The optimal control input solution to minimise performance index function is derived by solving this optimisation problem with two methods. A numerical simulation example is carried out and its results show that our two approaches are both feasible and very effective.

Distributed Optimization System

Science.gov (United States)

Hurtado, John E.; Dohrmann, Clark R.; Robinett, III, Rush D.

2004-11-30

A search system and method for controlling multiple agents to optimize an objective using distributed sensing and cooperative control. The search agent can be one or more physical agents, such as a robot, and can be software agents for searching cyberspace. The objective can be: chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, explosive sources, time dependent sources, time independent sources, function surfaces, maximization points, minimization points, and optimal control of a system such as a communication system, an economy, a crane, and a multi-processor computer.

Consideration of Optimal Input on Semi-Active Shock Control System

Science.gov (United States)

Kawashima, Takeshi

In press working, unidirectional transmission of mechanical energy is expected in order to maximize the life of the dies. To realize this transmission, the author has developed a shock control system based on the sliding mode control technique. The controller makes a collision-receiving object effectively deform plastically by adjusting the force of the actuator inserted between the colliding objects, while the deformation of the colliding object is held at the necessity minimum. However, the actuator has to generate a large force corresponding to the impulsive force. Therefore, development of such an actuator is a formidable challenge. The author has proposed a semi-active shock control system in which the impulsive force is adjusted by a brake mechanism, although the system exhibits inferior performance. Thus, the author has also designed an actuator using a friction device for semi-active shock control, and proposed an active seatbelt system as an application. The effectiveness has been confirmed by a numerical simulation and model experiment. In this study, the optimal deformation change of the colliding object is theoretically examined in the case that the collision-receiving object has perfect plasticity and the colliding object has perfect elasticity. As a result, the optimal input condition is obtained so that the ratio of the maximum deformation of the collision-receiving object to the maximum deformation of the colliding object becomes the maximum. Additionally, the energy balance is examined.

Optimal control of multi-level quantum systems

Energy Technology Data Exchange (ETDEWEB)

Fisher, Robert M.

2010-12-02

This thesis is concerned with the control of quantum systems. Given a Hamiltonian model of a quantum system, we are interested in finding controls - typically shaped electromagnetic pulses - that steer the evolution of the system toward a desired target operation. For this we employ a numerical optimisation method known as the GRAPE algorithm. For particular experimental systems, we design control schemes that respect constraints of robustness and addressability, and are within the reach of the experimental hardware. A general procedure is given for specifying a Hamiltonian model of a driven N-level system and converting it to an appropriate rotating frame. This is then applied together with the numerical algorithm to design improved schemes for two different systems, where laser fields manipulate orbital and hyperfine states of Pr{sup 3+} and Rb. The generation of cluster states in Ising-coupled systems is also studied. We find that, in the ideal case, the solution of evolving only under the coupling Hamiltonian is not time-optimal. This surprising result is in contrast to the known cases for unitary gates. For a symmetrised three-qubit example, we provide a geometrical interpretation of this. Numerically optimised control schemes are then developed for a nonideal coupling topology, modelling an experimental configuration of trapped ions. Controls for the implementation of the two-qubit Deutsch and Grover algorithms are designed for a pair of {sup 13}C nuclear spins at a nitrogen vacancy center in diamond. These implementations are robust to experimental errors, and found to be reproduced with high accuracy on a VFG-150 pulse generator. We also consider two-qubit gate synthesis in a system of superconducting qubits coupled by microwave resonators known as the cavity grid. We find that the optimised schemes allow two-qubit operations to be performed between an arbitrary qubit pair on the grid with only a small time overhead, with speedups of 2-4 over the existing

Optimal control of multi-level quantum systems

International Nuclear Information System (INIS)

Fisher, Robert M.

2010-01-01

This thesis is concerned with the control of quantum systems. Given a Hamiltonian model of a quantum system, we are interested in finding controls - typically shaped electromagnetic pulses - that steer the evolution of the system toward a desired target operation. For this we employ a numerical optimisation method known as the GRAPE algorithm. For particular experimental systems, we design control schemes that respect constraints of robustness and addressability, and are within the reach of the experimental hardware. A general procedure is given for specifying a Hamiltonian model of a driven N-level system and converting it to an appropriate rotating frame. This is then applied together with the numerical algorithm to design improved schemes for two different systems, where laser fields manipulate orbital and hyperfine states of Pr 3+ and Rb. The generation of cluster states in Ising-coupled systems is also studied. We find that, in the ideal case, the solution of evolving only under the coupling Hamiltonian is not time-optimal. This surprising result is in contrast to the known cases for unitary gates. For a symmetrised three-qubit example, we provide a geometrical interpretation of this. Numerically optimised control schemes are then developed for a nonideal coupling topology, modelling an experimental configuration of trapped ions. Controls for the implementation of the two-qubit Deutsch and Grover algorithms are designed for a pair of 13 C nuclear spins at a nitrogen vacancy center in diamond. These implementations are robust to experimental errors, and found to be reproduced with high accuracy on a VFG-150 pulse generator. We also consider two-qubit gate synthesis in a system of superconducting qubits coupled by microwave resonators known as the cavity grid. We find that the optimised schemes allow two-qubit operations to be performed between an arbitrary qubit pair on the grid with only a small time overhead, with speedups of 2-4 over the existing schemes

Nonlinear optimal control theory

CERN Document Server

Berkovitz, Leonard David

2012-01-01

Nonlinear Optimal Control Theory presents a deep, wide-ranging introduction to the mathematical theory of the optimal control of processes governed by ordinary differential equations and certain types of differential equations with memory. Many examples illustrate the mathematical issues that need to be addressed when using optimal control techniques in diverse areas. Drawing on classroom-tested material from Purdue University and North Carolina State University, the book gives a unified account of bounded state problems governed by ordinary, integrodifferential, and delay systems. It also dis

Hard and soft sub-time-optimal controllers for a mechanical system with uncertain mass

DEFF Research Database (Denmark)

Kulczycki, P.; Wisniewski, Rafal; Kowalski, P.

2004-01-01

An essential limitation in using the classical optimal control has been its limited robustness to modeling inadequacies and perturbations. This paper presents conceptions of two practical control structures based on the time-optimal approach: hard and soft ones. The hard structure is defined...... by parameters selected in accordance with the rules of the statistical decision theory; however, the soft structure allows additionally to eliminate rapid changes in control values. The object is a basic mechanical system, with uncertain (also non-stationary) mass treated as a stochastic process....... The methodology proposed here is of a universal nature and may easily be applied with respect to other elements of uncertainty of time-optimal controlled mechanical systems....

Hard and soft Sub-Time-Optimal Controllers for a Mechanical System with Uncertain Mass

DEFF Research Database (Denmark)

Kulczycki, P.; Wisniewski, Rafal; Kowalski, P.

2005-01-01

An essential limitation in using the classical optimal control has been its limited robustness to modeling inadequacies and perturbations. This paper presents conceptions of two practical control structures based on the time-optimal approach: hard and soft ones. The hard structure is defined...... by parameters selected in accordance with the rules of the statistical decision theory; however, the soft structure allows additionally to eliminate rapid changes in control values. The object is a basic mechanical system, with uncertain (also non-stationary) mass treated as a stochastic process....... The methodology proposed here is of a universal nature and may easily be applied with respect to other elements of uncertainty of time-optimal controlled mechanical systems....

Centralized Stochastic Optimal Control of Complex Systems

Energy Technology Data Exchange (ETDEWEB)

Malikopoulos, Andreas [ORNL

2015-01-01

In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain using the long-run expected average cost per unit time criterion, and we show that the control policy yielding the Pareto optimal solution minimizes the average cost criterion online. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.

State transformations and Hamiltonian structures for optimal control in discrete systems

Science.gov (United States)

Sieniutycz, S.

2006-04-01

Preserving usual definition of Hamiltonian H as the scalar product of rates and generalized momenta we investigate two basic classes of discrete optimal control processes governed by the difference rather than differential equations for the state transformation. The first class, linear in the time interval θ, secures the constancy of optimal H and satisfies a discrete Hamilton-Jacobi equation. The second class, nonlinear in θ, does not assure the constancy of optimal H and satisfies only a relationship that may be regarded as an equation of Hamilton-Jacobi type. The basic question asked is if and when Hamilton's canonical structures emerge in optimal discrete systems. For a constrained discrete control, general optimization algorithms are derived that constitute powerful theoretical and computational tools when evaluating extremum properties of constrained physical systems. The mathematical basis is Bellman's method of dynamic programming (DP) and its extension in the form of the so-called Carathéodory-Boltyanski (CB) stage optimality criterion which allows a variation of the terminal state that is otherwise fixed in Bellman's method. For systems with unconstrained intervals of the holdup time θ two powerful optimization algorithms are obtained: an unconventional discrete algorithm with a constant H and its counterpart for models nonlinear in θ. We also present the time-interval-constrained extension of the second algorithm. The results are general; namely, one arrives at: discrete canonical equations of Hamilton, maximum principles, and (at the continuous limit of processes with free intervals of time) the classical Hamilton-Jacobi theory, along with basic results of variational calculus. A vast spectrum of applications and an example are briefly discussed with particular attention paid to models nonlinear in the time interval θ.

Quasicanonical structure of optimal control in constrained discrete systems

Science.gov (United States)

Sieniutycz, S.

2003-06-01

This paper considers discrete processes governed by difference rather than differential equations for the state transformation. The basic question asked is if and when Hamiltonian canonical structures are possible in optimal discrete systems. Considering constrained discrete control, general optimization algorithms are derived that constitute suitable theoretical and computational tools when evaluating extremum properties of constrained physical models. The mathematical basis of the general theory is the Bellman method of dynamic programming (DP) and its extension in the form of the so-called Carathéodory-Boltyanski (CB) stage criterion which allows a variation of the terminal state that is otherwise fixed in the Bellman's method. Two relatively unknown, powerful optimization algorithms are obtained: an unconventional discrete formalism of optimization based on a Hamiltonian for multistage systems with unconstrained intervals of holdup time, and the time interval constrained extension of the formalism. These results are general; namely, one arrives at: the discrete canonical Hamilton equations, maximum principles, and (at the continuous limit of processes with free intervals of time) the classical Hamilton-Jacobi theory along with all basic results of variational calculus. Vast spectrum of applications of the theory is briefly discussed.

Optimization and Optimal Control in Automotive Systems

NARCIS (Netherlands)

Waschl, H.; Kolmanovsky, I.V.; Steinbuch, M.; Re, del L.

2014-01-01

This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier approaches, based on some degree of heuristics, to the use of more and

Optimal control of open quantum systems: a combined surrogate hamiltonian optimal control theory approach applied to photochemistry on surfaces.

Science.gov (United States)

Asplund, Erik; Klüner, Thorsten

2012-03-28

In this paper, control of open quantum systems with emphasis on the control of surface photochemical reactions is presented. A quantum system in a condensed phase undergoes strong dissipative processes. From a theoretical viewpoint, it is important to model such processes in a rigorous way. In this work, the description of open quantum systems is realized within the surrogate hamiltonian approach [R. Baer and R. Kosloff, J. Chem. Phys. 106, 8862 (1997)]. An efficient and accurate method to find control fields is optimal control theory (OCT) [W. Zhu, J. Botina, and H. Rabitz, J. Chem. Phys. 108, 1953 (1998); Y. Ohtsuki, G. Turinici, and H. Rabitz, J. Chem. Phys. 120, 5509 (2004)]. To gain control of open quantum systems, the surrogate hamiltonian approach and OCT, with time-dependent targets, are combined. Three open quantum systems are investigated by the combined method, a harmonic oscillator immersed in an ohmic bath, CO adsorbed on a platinum surface, and NO adsorbed on a nickel oxide surface. Throughout this paper, atomic units, i.e., ℏ = m(e) = e = a(0) = 1, have been used unless otherwise stated.

Design of an optimal preview controller for linear discrete-time descriptor systems with state delay

Science.gov (United States)

Cao, Mengjuan; Liao, Fucheng

2015-04-01

In this paper, the linear discrete-time descriptor system with state delay is studied, and a design method for an optimal preview controller is proposed. First, by using the discrete lifting technique, the original system is transformed into a general descriptor system without state delay in form. Then, taking advantage of the first-order forward difference operator, we construct a descriptor augmented error system, including the state vectors of the lifted system, error vectors, and desired target signals. Rigorous mathematical proofs are given for the regularity, stabilisability, causal controllability, and causal observability of the descriptor augmented error system. Based on these, the optimal preview controller with preview feedforward compensation for the original system is obtained by using the standard optimal regulator theory of the descriptor system. The effectiveness of the proposed method is shown by numerical simulation.

Online Adaptive Optimal Control of Vehicle Active Suspension Systems Using Single-Network Approximate Dynamic Programming

Directory of Open Access Journals (Sweden)

Zhi-Jun Fu

2017-01-01

Full Text Available In view of the performance requirements (e.g., ride comfort, road holding, and suspension space limitation for vehicle suspension systems, this paper proposes an adaptive optimal control method for quarter-car active suspension system by using the approximate dynamic programming approach (ADP. Online optimal control law is obtained by using a single adaptive critic NN to approximate the solution of the Hamilton-Jacobi-Bellman (HJB equation. Stability of the closed-loop system is proved by Lyapunov theory. Compared with the classic linear quadratic regulator (LQR approach, the proposed ADP-based adaptive optimal control method demonstrates improved performance in the presence of parametric uncertainties (e.g., sprung mass and unknown road displacement. Numerical simulation results of a sedan suspension system are presented to verify the effectiveness of the proposed control strategy.

Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure.

Science.gov (United States)

Luo, Biao; Liu, Derong; Wu, Huai-Ning

2018-06-01

Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition . To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

Optimal Design of MPPT Controllers for Grid Connected Photovoltaic Array System

Science.gov (United States)

Ebrahim, M. A.; AbdelHadi, H. A.; Mahmoud, H. M.; Saied, E. M.; Salama, M. M.

2016-10-01

Integrating photovoltaic (PV) plants into electric power system exhibits challenges to power system dynamic performance. These challenges stem primarily from the natural characteristics of PV plants, which differ in some respects from the conventional plants. The most significant challenge is how to extract and regulate the maximum power from the sun. This paper presents the optimal design for the most commonly used Maximum Power Point Tracking (MPPT) techniques based on Proportional Integral tuned by Particle Swarm Optimization (PI-PSO). These suggested techniques are, (1) the incremental conductance, (2) perturb and observe, (3) fractional short circuit current and (4) fractional open circuit voltage techniques. This research work provides a comprehensive comparative study with the energy availability ratio from photovoltaic panels. The simulation results proved that the proposed controllers have an impressive tracking response. The system dynamic performance improved greatly using the proposed controllers.

A generic methodology for the optimisation of sewer systems using stochastic programming and self-optimizing control

DEFF Research Database (Denmark)

Maurico-Iglesias, Miguel; Castro, Ignacio Montero; Mollerup, Ane Loft

2015-01-01

. Such controller is aimed at keeping the system close to the optimal performance, thanks to an optimal selection of controlled variables. The definition of an optimal performance was carried out by a two-stage optimisation (stochastic and deterministic) to take into account both the overflow during the current......The design of sewer system control is a complex task given the large size of the sewer networks, the transient dynamics of the water flow and the stochastic nature of rainfall. This contribution presents a generic methodology for the design of a self-optimising controller in sewer systems...

A study on optimal control of the aero-propulsion system acceleration process under the supersonic state

Directory of Open Access Journals (Sweden)

Fengyong Sun

2017-04-01

Full Text Available In order to solve the aero-propulsion system acceleration optimal problem, the necessity of inlet control is discussed, and a fully new aero-propulsion system acceleration process control design including the inlet, engine, and nozzle is proposed in this paper. In the proposed propulsion system control scheme, the inlet, engine, and nozzle are simultaneously adjusted through the FSQP method. In order to implement the control scheme design, an aero-propulsion system component-level model is built to simulate the inlet working performance and the matching problems between the inlet and engine. Meanwhile, a stabilizing inlet control scheme is designed to solve the inlet control problems. In optimal control of the aero-propulsion system acceleration process, the inlet is an emphasized control unit in the optimal acceleration control system. Two inlet control patterns are discussed in the simulation. The simulation results prove that by taking the inlet ramp angle as an active control variable instead of being modulated passively, acceleration performance could be obviously enhanced. Acceleration objectives could be obtained with a faster acceleration time by 5%.

An optimal control method for fluid structure interaction systems via adjoint boundary pressure

Science.gov (United States)

Chirco, L.; Da Vià, R.; Manservisi, S.

2017-11-01

In recent year, in spite of the computational complexity, Fluid-structure interaction (FSI) problems have been widely studied due to their applicability in science and engineering. Fluid-structure interaction systems consist of one or more solid structures that deform by interacting with a surrounding fluid flow. FSI simulations evaluate the tensional state of the mechanical component and take into account the effects of the solid deformations on the motion of the interior fluids. The inverse FSI problem can be described as the achievement of a certain objective by changing some design parameters such as forces, boundary conditions and geometrical domain shapes. In this paper we would like to study the inverse FSI problem by using an optimal control approach. In particular we propose a pressure boundary optimal control method based on Lagrangian multipliers and adjoint variables. The objective is the minimization of a solid domain displacement matching functional obtained by finding the optimal pressure on the inlet boundary. The optimality system is derived from the first order necessary conditions by taking the Fréchet derivatives of the Lagrangian with respect to all the variables involved. The optimal solution is then obtained through a standard steepest descent algorithm applied to the optimality system. The approach presented in this work is general and could be used to assess other objective functionals and controls. In order to support the proposed approach we perform a few numerical tests where the fluid pressure on the domain inlet controls the displacement that occurs in a well defined region of the solid domain.

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

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Adel Taieb

2017-01-01

Full Text Available This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO Fuzzy Optimal Model Predictive Control (FOMPC using the Adaptive Particle Swarm Optimization (APSO algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR and Tank system, where the proposed approach provides better performances compared with other methods.

Automatic generation control application with craziness based particle swarm optimization in a thermal power system

Energy Technology Data Exchange (ETDEWEB)

Gozde, Haluk; Taplamacioglu, M. Cengiz [Gazi University, Faculty of Engineering, Department of Electrical and Electronics Engineering, 06750 Maltepe, Ankara (Turkey)

2011-01-15

In this study, a novel gain scheduling Proportional-plus-Integral (PI) control strategy is suggested for automatic generation control (AGC) of the two area thermal power system with governor dead-band nonlinearity. In this strategy, the control is evaluated as an optimization problem, and two different cost functions with tuned weight coefficients are derived in order to increase the performance of convergence to the global optima. One of the cost functions is derived through the frequency deviations of the control areas and tie-line power changes. On the other hand, the other one includes the rate of changes which can be variable depends on the time in these deviations. These weight coefficients of the cost functions are also optimized as the controller gains have been done. The craziness based particle swarm optimization (CRAZYPSO) algorithm is preferred to optimize the parameters, because of convergence superiority. At the end of the study, the performance of the control system is compared with the performance which is obtained with classical integral of the squared error (ISE) and the integral of time weighted squared error (ITSE) cost functions through transient response analysis method. The results show that the obtained optimal PI-controller improves the dynamic performance of the power system as expected as mentioned in literature. (author)

Optimization of accelerator control

International Nuclear Information System (INIS)

Vasiljev, N.D.; Mozin, I.V.; Shelekhov, V.A.; Efremov, D.V.

1992-01-01

Expensive exploitation of charged particle accelerators is inevitably concerned with requirements of effectively obtaining of the best characteristics of accelerated beams for physical experiments. One of these characteristics is intensity. Increase of intensity is hindered by a number of effects, concerned with the influence of the volume charge field on a particle motion dynamics in accelerator's chamber. However, ultimate intensity, determined by a volume charge, is almost not achieved for the most of the operating accelerators. This fact is caused by losses of particles during injection, at the initial stage of acceleration and during extraction. These losses are caused by deviations the optimal from real characteristics of the accelerating and magnetic system. This is due to a number of circumstances, including technological tolerances on structural elements of systems, influence of measuring and auxiliary equipment and beam consumers' installations, placed in the closed proximity to magnets, and instability in operation of technological systems of accelerator. Control task consists in compensation of deviations of characteristics of magnetic and electric fields by optimal selection of control actions. As for technical means, automatization of modern accelerators allows to solve optimal control problems in real time. Therefore, the report is devoted to optimal control methods and experimental results. (J.P.N.)

Development of Design Tools for the Optimization of Biologically Based Control Systems

Data.gov (United States)

National Aeronautics and Space Administration — I plan to develop software that aids in the design of biomimetic control systems by optimizing the properties of the system in order to produce the desired output....

ORACLS- OPTIMAL REGULATOR ALGORITHMS FOR THE CONTROL OF LINEAR SYSTEMS (CDC VERSION)

Science.gov (United States)

Armstrong, E. S.

1994-01-01

This control theory design package, called Optimal Regulator Algorithms for the Control of Linear Systems (ORACLS), was developed to aid in the design of controllers and optimal filters for systems which can be modeled by linear, time-invariant differential and difference equations. Optimal linear quadratic regulator theory, currently referred to as the Linear-Quadratic-Gaussian (LQG) problem, has become the most widely accepted method of determining optimal control policy. Within this theory, the infinite duration time-invariant problems, which lead to constant gain feedback control laws and constant Kalman-Bucy filter gains for reconstruction of the system state, exhibit high tractability and potential ease of implementation. A variety of new and efficient methods in the field of numerical linear algebra have been combined into the ORACLS program, which provides for the solution to time-invariant continuous or discrete LQG problems. The ORACLS package is particularly attractive to the control system designer because it provides a rigorous tool for dealing with multi-input and multi-output dynamic systems in both continuous and discrete form. The ORACLS programming system is a collection of subroutines which can be used to formulate, manipulate, and solve various LQG design problems. The ORACLS program is constructed in a manner which permits the user to maintain considerable flexibility at each operational state. This flexibility is accomplished by providing primary operations, analysis of linear time-invariant systems, and control synthesis based on LQG methodology. The input-output routines handle the reading and writing of numerical matrices, printing heading information, and accumulating output information. The basic vector-matrix operations include addition, subtraction, multiplication, equation, norm construction, tracing, transposition, scaling, juxtaposition, and construction of null and identity matrices. The analysis routines provide for the following

Modified Newton-Raphson GRAPE methods for optimal control of spin systems

International Nuclear Information System (INIS)

Goodwin, D. L.; Kuprov, Ilya

2016-01-01

Quadratic convergence throughout the active space is achieved for the gradient ascent pulse engineering (GRAPE) family of quantum optimal control algorithms. We demonstrate in this communication that the Hessian of the GRAPE fidelity functional is unusually cheap, having the same asymptotic complexity scaling as the functional itself. This leads to the possibility of using very efficient numerical optimization techniques. In particular, the Newton-Raphson method with a rational function optimization (RFO) regularized Hessian is shown in this work to require fewer system trajectory evaluations than any other algorithm in the GRAPE family. This communication describes algebraic and numerical implementation aspects (matrix exponential recycling, Hessian regularization, etc.) for the RFO Newton-Raphson version of GRAPE and reports benchmarks for common spin state control problems in magnetic resonance spectroscopy.

Modified Newton-Raphson GRAPE methods for optimal control of spin systems

Energy Technology Data Exchange (ETDEWEB)

Goodwin, D. L.; Kuprov, Ilya, E-mail: i.kuprov@soton.ac.uk [School of Chemistry, University of Southampton, Highfield Campus, Southampton SO17 1BJ (United Kingdom)

2016-05-28

Quadratic convergence throughout the active space is achieved for the gradient ascent pulse engineering (GRAPE) family of quantum optimal control algorithms. We demonstrate in this communication that the Hessian of the GRAPE fidelity functional is unusually cheap, having the same asymptotic complexity scaling as the functional itself. This leads to the possibility of using very efficient numerical optimization techniques. In particular, the Newton-Raphson method with a rational function optimization (RFO) regularized Hessian is shown in this work to require fewer system trajectory evaluations than any other algorithm in the GRAPE family. This communication describes algebraic and numerical implementation aspects (matrix exponential recycling, Hessian regularization, etc.) for the RFO Newton-Raphson version of GRAPE and reports benchmarks for common spin state control problems in magnetic resonance spectroscopy.

Pareto-optimal multi-objective design of airplane control systems

Science.gov (United States)

Schy, A. A.; Johnson, K. G.; Giesy, D. P.

1980-01-01

A constrained minimization algorithm for the computer aided design of airplane control systems to meet many requirements over a set of flight conditions is generalized using the concept of Pareto-optimization. The new algorithm yields solutions on the boundary of the achievable domain in objective space in a single run, whereas the older method required a sequence of runs to approximate such a limiting solution. However, Pareto-optimality does not guarantee a satisfactory design, since such solutions may emphasize some objectives at the expense of others. The designer must still interact with the program to obtain a well-balanced set of objectives. Using the example of a fighter lateral stability augmentation system (SAS) design over five flight conditions, several effective techniques are developed for obtaining well-balanced Pareto-optimal solutions. For comparison, one of these techniques is also used in a recently developed algorithm of Kreisselmeier and Steinhauser, which replaces the hard constraints with soft constraints, using a special penalty function. It is shown that comparable results can be obtained.

Direct Optimal Control of Duffing Dynamics

Science.gov (United States)

Oz, Hayrani; Ramsey, John K.

2002-01-01

The "direct control method" is a novel concept that is an attractive alternative and competitor to the differential-equation-based methods. The direct method is equally well applicable to nonlinear, linear, time-varying, and time-invariant systems. For all such systems, the method yields explicit closed-form control laws based on minimization of a quadratic control performance measure. We present an application of the direct method to the dynamics and optimal control of the Duffing system where the control performance measure is not restricted to a quadratic form and hence may include a quartic energy term. The results we present in this report also constitute further generalizations of our earlier work in "direct optimal control methodology." The approach is demonstrated for the optimal control of the Duffing equation with a softening nonlinear stiffness.

Control optimizations for heat recovery from CO2 refrigeration systems in supermarket

International Nuclear Information System (INIS)

Ge, Y.T.; Tassou, S.A.

2014-01-01

Highlights: • Application of supermarket energy control system model. • Heat recovery from CO 2 refrigeration system in supermarket space conditioning. • Effect of pressure controls of CO 2 refrigeration system on heat recovery potentials. • Control optimization of CO 2 refrigeration system for heat recovery in supermarket. - Abstract: A modern supermarket energy control system has a concurrent need for electricity, food refrigeration and space heating or cooling. Approximately 10% of this energy is for conventional gas-powered heating. In recent years, the use of CO 2 as a refrigerant in supermarket systems has received considerable attention due to its negligible contribution to direct greenhouse gas emissions and excellent thermophysical and heat transfer properties. CO 2 refrigeration systems also offer more compact component designs over a conventional HFC system and heat recovery potential from compressor discharge. In this paper, the heat recovery potential of an all-CO 2 cascade refrigeration system in a supermarket has been investigated using the supermarket simulation model “SuperSim” developed by the authors. It has been shown that at UK weather conditions, the heat recovery potential of CO 2 refrigeration systems can be increased by increasing the condenser/gas cooler pressure to the point where all the heat requirements are satisfied. However, the optimum level of heat recovery will vary during the year and the control system should be able to continuously optimize this level based on the relative cost of energy, i.e., gas and electricity

Optimal control of quantum dissipative dynamics: Analytic solution for cooling the three-level Λ system

International Nuclear Information System (INIS)

Sklarz, Shlomo E.; Tannor, David J.; Khaneja, Navin

2004-01-01

We study the problem of optimal control of dissipative quantum dynamics. Although under most circumstances dissipation leads to an increase in entropy (or a decrease in purity) of the system, there is an important class of problems for which dissipation with external control can decrease the entropy (or increase the purity) of the system. An important example is laser cooling. In such systems, there is an interplay of the Hamiltonian part of the dynamics, which is controllable, and the dissipative part of the dynamics, which is uncontrollable. The strategy is to control the Hamiltonian portion of the evolution in such a way that the dissipation causes the purity of the system to increase rather than decrease. The goal of this paper is to find the strategy that leads to maximal purity at the final time. Under the assumption that Hamiltonian control is complete and arbitrarily fast, we provide a general framework by which to calculate optimal cooling strategies. These assumptions lead to a great simplification, in which the control problem can be reformulated in terms of the spectrum of eigenvalues of ρ, rather than ρ itself. By combining this formulation with the Hamilton-Jacobi-Bellman theorem we are able to obtain an equation for the globally optimal cooling strategy in terms of the spectrum of the density matrix. For the three-level Λ system, we provide a complete analytic solution for the optimal cooling strategy. For this system it is found that the optimal strategy does not exploit system coherences and is a 'greedy' strategy, in which the purity is increased maximally at each instant

Optimal Design and Hybrid Control for the Electro-Hydraulic Dual-Shaking Table System

Directory of Open Access Journals (Sweden)

Lianpeng Zhang

2016-08-01

Full Text Available This paper is to develop an optimal electro-hydraulic dual-shaking table system with high waveform replication precision. The parameters of hydraulic cylinders, servo valves, hydraulic supply power and gravity balance system are designed and optimized in detail. To improve synchronization and tracking control precision, a hybrid control strategy is proposed. The cross-coupled control using a novel based on sliding mode control based on adaptive reaching law (ASMC, which can adaptively tune the parameters of sliding mode control (SMC, is proposed to reduce the synchronization error. To improve the tracking performance, the observer-based inverse control scheme combining the feed-forward inverse model controller and disturbance observer is proposed. The system model is identified applying the recursive least squares (RLS algorithm and then the feed-forward inverse controller is designed based on zero phase error tracking controller (ZPETC technique. To compensate disturbance and model errors, disturbance observer is used cooperating with the designed inverse controller. The combination of the novel ASMC cross-coupled controller and proposed observer-based inverse controller can improve the control precision noticeably. The dual-shaking table experiment system is built and various experiments are performed. The experimental results indicate that the developed system with the proposed hybrid control strategy is feasible and efficient and can reduce the tracking errors to 25% and synchronization error to 16% compared with traditional control schemes.

Optimal Control of Diesel Engines with Waste Heat Recovery System

NARCIS (Netherlands)

Willems, F.P.T.; Donkers, M.C.F.; Kupper, F.

2014-01-01

This study presents an integrated energy and emission management strategy for a Euro-VI diesel engine with Waste Heat Recovery (WHR) system. This Integrated Powertrain Control (IPC) strategy optimizes the CO2-NOx trade-off by minimizing the operational costs associated with fuel and AdBlue

Optimal control of diesel engines with waste heat recovery systems

NARCIS (Netherlands)

Willems, F.P.T.; Donkers, M.C.F.; Kupper, F.; Waschl, H.; Kolmanovsky, I.; Steinbuch, M.; Del Re, L.

2014-01-01

This study presents an integrated energy and emission management strategy for a Euro-VI diesel engine with Waste Heat Recovery (WHR) system. This Integrated Powertrain Control (IPC) strategy optimizes the CO 2 - NO x trade-off by minimizing the operational costs associated with fuel and AdBlue

Optimal sampling period of the digital control system for the nuclear power plant steam generator water level control

International Nuclear Information System (INIS)

Hur, Woo Sung; Seong, Poong Hyun

1995-01-01

A great effort has been made to improve the nuclear plant control system by use of digital technologies and a long term schedule for the control system upgrade has been prepared with an aim to implementation in the next generation nuclear plants. In case of digital control system, it is important to decide the sampling period for analysis and design of the system, because the performance and the stability of a digital control system depend on the value of the sampling period of the digital control system. There is, however, currently no systematic method used universally for determining the sampling period of the digital control system. Generally, a traditional way to select the sampling frequency is to use 20 to 30 times the bandwidth of the analog control system which has the same system configuration and parameters as the digital one. In this paper, a new method to select the sampling period is suggested which takes into account of the performance as well as the stability of the digital control system. By use of the Irving's model steam generator, the optimal sampling period of an assumptive digital control system for steam generator level control is estimated and is actually verified in the digital control simulation system for Kori-2 nuclear power plant steam generator level control. Consequently, we conclude the optimal sampling period of the digital control system for Kori-2 nuclear power plant steam generator level control is 1 second for all power ranges. 7 figs., 3 tabs., 8 refs. (Author)

Role of controllability in optimizing quantum dynamics

International Nuclear Information System (INIS)

Wu Rebing; Hsieh, Michael A.; Rabitz, Herschel

2011-01-01

This paper reveals an important role that controllability plays in the complexity of optimizing quantum control dynamics. We show that the loss of controllability generally leads to multiple locally suboptimal controls when gate fidelity in a quantum control system is maximized, which does not happen if the system is controllable. Such local suboptimal controls may attract an optimization algorithm into a local trap when a global optimal solution is sought, even if the target gate can be perfectly realized. This conclusion results from an analysis of the critical topology of the corresponding quantum control landscape, which refers to the gate fidelity objective as a functional of the control fields. For uncontrollable systems, due to SU(2) and SU(3) dynamical symmetries, the control landscape corresponding to an implementable target gate is proven to possess multiple locally optimal critical points, and its ruggedness can be further increased if the target gate is not realizable. These results imply that the optimization of quantum dynamics can be seriously impeded when operating with local search algorithms under these conditions, and thus full controllability is demanded.

Optimized Design of the SGA-WZ Strapdown Airborne Gravimeter Temperature Control System

Directory of Open Access Journals (Sweden)

Juliang Cao

2015-12-01

Full Text Available The temperature control system is one of the most important subsystems of the strapdown airborne gravimeter. Because the quartz flexible accelerometer based on springy support technology is the core sensor in the strapdown airborne gravimeter and the magnet steel in the electromagnetic force equilibrium circuits of the quartz flexible accelerometer is greatly affected by temperature, in order to guarantee the temperature control precision and minimize the effect of temperature on the gravimeter, the SGA-WZ temperature control system adopts a three-level control method. Based on the design experience of the SGA-WZ-01, the SGA-WZ-02 temperature control system came out with a further optimized design. In 1st level temperature control, thermoelectric cooler is used to conquer temperature change caused by hot weather. The experiments show that the optimized stability of 1st level temperature control is about 0.1 °C and the max cool down capability is about 10 °C. The temperature field is analyzed in the 2nd and 3rd level temperature control using the finite element analysis software ANSYS. The 2nd and 3rd level temperature control optimization scheme is based on the foundation of heat analysis. The experimental results show that static accuracy of SGA-WZ-02 reaches 0.21 mGal/24 h, with internal accuracy being 0.743 mGal/4.8 km and external accuracy being 0.37 mGal/4.8 km compared with the result of the GT-2A, whose internal precision is superior to 1 mGal/4.8 km and all of them are better than those in SGA-WZ-01.

Optimal control of open quantum systems: A combined surrogate Hamiltonian optimal control theory approach applied to photochemistry on surfaces

International Nuclear Information System (INIS)

Asplund, Erik; Kluener, Thorsten

2012-01-01

In this paper, control of open quantum systems with emphasis on the control of surface photochemical reactions is presented. A quantum system in a condensed phase undergoes strong dissipative processes. From a theoretical viewpoint, it is important to model such processes in a rigorous way. In this work, the description of open quantum systems is realized within the surrogate Hamiltonian approach [R. Baer and R. Kosloff, J. Chem. Phys. 106, 8862 (1997)]. An efficient and accurate method to find control fields is optimal control theory (OCT) [W. Zhu, J. Botina, and H. Rabitz, J. Chem. Phys. 108, 1953 (1998); Y. Ohtsuki, G. Turinici, and H. Rabitz, J. Chem. Phys. 120, 5509 (2004)]. To gain control of open quantum systems, the surrogate Hamiltonian approach and OCT, with time-dependent targets, are combined. Three open quantum systems are investigated by the combined method, a harmonic oscillator immersed in an ohmic bath, CO adsorbed on a platinum surface, and NO adsorbed on a nickel oxide surface. Throughout this paper, atomic units, i.e., (ℎ/2π)=m e =e=a 0 = 1, have been used unless otherwise stated.

Distributed Optimal Consensus Control for Nonlinear Multiagent System With Unknown Dynamic.

Science.gov (United States)

Zhang, Jilie; Zhang, Huaguang; Feng, Tao

2017-08-01

This paper focuses on the distributed optimal cooperative control for continuous-time nonlinear multiagent systems (MASs) with completely unknown dynamics via adaptive dynamic programming (ADP) technology. By introducing predesigned extra compensators, the augmented neighborhood error systems are derived, which successfully circumvents the system knowledge requirement for ADP. It is revealed that the optimal consensus protocols actually work as the solutions of the MAS differential game. Policy iteration algorithm is adopted, and it is theoretically proved that the iterative value function sequence strictly converges to the solution of the coupled Hamilton-Jacobi-Bellman equation. Based on this point, a novel online iterative scheme is proposed, which runs based on the data sampled from the augmented system and the gradient of the value function. Neural networks are employed to implement the algorithm and the weights are updated, in the least-square sense, to the ideal value, which yields approximated optimal consensus protocols. Finally, a numerical example is given to illustrate the effectiveness of the proposed scheme.

Online optimal control of variable refrigerant flow and variable air volume combined air conditioning system for energy saving

International Nuclear Information System (INIS)

Zhu, Yonghua; Jin, Xinqiao; Du, Zhimin; Fang, Xing

2015-01-01

The variable refrigerant flow (VRF) and variable air volume (VAV) combined air conditioning system can solve the problem of the VRF system in outdoor air ventilation while taking advantage of its high part load energy efficiency. Energy performance of the combined air conditioning system can also be optimized by joint control of both the VRF and the VAV parts. A model-based online optimal control strategy for the combined air conditioning system is presented. Simplified adaptive models of major components of the combined air conditioning system are firstly developed for predicting system performances. And a cost function in terms of energy consumption and thermal comfort is constructed. Genetic algorithm is used to search for the optimal control sets. The optimal control strategy is tested and evaluated through two case studies based on the simulation platform. Results show that the optimal strategy can effectively reduce energy consumption of the combined air conditioning system while maintaining acceptable thermal comfort. - Highlights: • A VRF and VAV combined system is proposed. • A model-based online optimal control strategy is proposed for the combined system. • The strategy can reduce energy consumption without sacrificing thermal comfort. • Novel simplified adaptive models are firstly developed for the VRF system

Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances.

Science.gov (United States)

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai; Zhang, Huaguang

2016-05-01

An optimal control method is developed for unknown continuous-time systems with unknown disturbances in this paper. The integral reinforcement learning (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or may be biased. For reducing the influence of unknown disturbances, a disturbances compensation controller is added. It is proven that the weight errors are uniformly ultimately bounded based on Lyapunov techniques. Convergence of the Hamiltonian function is also proven. The simulation study demonstrates the effectiveness of the proposed optimal control method for unknown systems with disturbances.

Implementation and on-sky results of an optimal wavefront controller for the MMT NGS adaptive optics system

Science.gov (United States)

Powell, Keith B.; Vaitheeswaran, Vidhya

2010-07-01

The MMT observatory has recently implemented and tested an optimal wavefront controller for the NGS adaptive optics system. Open loop atmospheric data collected at the telescope is used as the input to a MATLAB based analytical model. The model uses nonlinear constrained minimization to determine controller gains and optimize the system performance. The real-time controller performing the adaptive optics close loop operation is implemented on a dedicated high performance PC based quad core server. The controller algorithm is written in C and uses the GNU scientific library for linear algebra. Tests at the MMT confirmed the optimal controller significantly reduced the residual RMS wavefront compared with the previous controller. Significant reductions in image FWHM and increased peak intensities were obtained in J, H and K-bands. The optimal PID controller is now operating as the baseline wavefront controller for the MMT NGS-AO system.

Power flow analysis for islanded microgrid in hierarchical structure of control system using optimal control theory

Directory of Open Access Journals (Sweden)

Thang Diep Thanh

2017-12-01

Full Text Available In environmental uncertainties, the power flow problem in islanded microgrid (MG becomes complex and non-trivial. The optimal power flow (OPL problem is described in this paper by using the energy balance between the power generation and load demand. The paper also presents the hierarchical control structure which consists of primary, secondary, tertiary, and emergency controls. Clearly, optimal power flow (OPL which implements a distributed tertiary control in hierarchical control. MG consists of diesel engine generator (DEG, wind turbine generator (WTG, and photovoltaic (PV power. In the control system considered, operation planning is realized based on profiles such that the MG, load, wind and photovoltaic power must be forecasted in short-period, meanwhile the dispatch source (i.e., DEG needs to be scheduled. The aim of the control problem is to find the dispatch output power by minimizing the total cost of energy that leads to the Hamilton-Jacobi-Bellman equation. Experimental results are presented, showing the effectiveness of optimal control such that the generation allows demand profile.

Adaptive Inverse Optimal Control for Rehabilitation Robot Systems Using Actor-Critic Algorithm

Directory of Open Access Journals (Sweden)

Fancheng Meng

2014-01-01

Full Text Available The higher goal of rehabilitation robot is to aid a person to achieve a desired functional task (e.g., tracking trajectory based on assisted-as-needed principle. To this goal, a new adaptive inverse optimal hybrid control (AHC combining inverse optimal control and actor-critic learning is proposed. Specifically, an uncertain nonlinear rehabilitation robot model is firstly developed that includes human motor behavior dynamics. Then, based on this model, an open-loop error system is formed; thereafter, an inverse optimal control input is designed to minimize the cost functional and a NN-based actor-critic feedforward signal is responsible for the nonlinear dynamic part contaminated by uncertainties. Finally, the AHC controller is proven (through a Lyapunov-based stability analysis to yield a global uniformly ultimately bounded stability result, and the resulting cost functional is meaningful. Simulation and experiment on rehabilitation robot demonstrate the effectiveness of the proposed control scheme.

Cooperative control of multi-agent systems optimal and adaptive design approaches

CERN Document Server

Lewis, Frank L; Hengster-Movric, Kristian; Das, Abhijit

2014-01-01

Task complexity, communication constraints, flexibility and energy-saving concerns are all factors that may require a group of autonomous agents to work together in a cooperative manner. Applications involving such complications include mobile robots, wireless sensor networks, unmanned aerial vehicles (UAVs), spacecraft, and so on. In such networked multi-agent scenarios, the restrictions imposed by the communication graph topology can pose severe problems in the design of cooperative feedback control systems.  Cooperative control of multi-agent systems is a challenging topic for both control theorists and practitioners and has been the subject of significant recent research. Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs.  It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design.  B...

An Optimal Control Strategy for DC Bus Voltage Regulation in Photovoltaic System with Battery Energy Storage

Directory of Open Access Journals (Sweden)

Muhamad Zalani Daud

2014-01-01

Full Text Available This paper presents an evaluation of an optimal DC bus voltage regulation strategy for grid-connected photovoltaic (PV system with battery energy storage (BES. The BES is connected to the PV system DC bus using a DC/DC buck-boost converter. The converter facilitates the BES power charge/discharge to compensate for the DC bus voltage deviation during severe disturbance conditions. In this way, the regulation of DC bus voltage of the PV/BES system can be enhanced as compared to the conventional regulation that is solely based on the voltage-sourced converter (VSC. For the grid side VSC (G-VSC, two control methods, namely, the voltage-mode and current-mode controls, are applied. For control parameter optimization, the simplex optimization technique is applied for the G-VSC voltage- and current-mode controls, including the BES DC/DC buck-boost converter controllers. A new set of optimized parameters are obtained for each of the power converters for comparison purposes. The PSCAD/EMTDC-based simulation case studies are presented to evaluate the performance of the proposed optimized control scheme in comparison to the conventional methods.

An optimal control strategy for DC bus voltage regulation in photovoltaic system with battery energy storage.

Science.gov (United States)

Daud, Muhamad Zalani; Mohamed, Azah; Hannan, M A

2014-01-01

This paper presents an evaluation of an optimal DC bus voltage regulation strategy for grid-connected photovoltaic (PV) system with battery energy storage (BES). The BES is connected to the PV system DC bus using a DC/DC buck-boost converter. The converter facilitates the BES power charge/discharge to compensate for the DC bus voltage deviation during severe disturbance conditions. In this way, the regulation of DC bus voltage of the PV/BES system can be enhanced as compared to the conventional regulation that is solely based on the voltage-sourced converter (VSC). For the grid side VSC (G-VSC), two control methods, namely, the voltage-mode and current-mode controls, are applied. For control parameter optimization, the simplex optimization technique is applied for the G-VSC voltage- and current-mode controls, including the BES DC/DC buck-boost converter controllers. A new set of optimized parameters are obtained for each of the power converters for comparison purposes. The PSCAD/EMTDC-based simulation case studies are presented to evaluate the performance of the proposed optimized control scheme in comparison to the conventional methods.

NSGA-II based optimal control scheme of wind thermal power system for improvement of frequency regulation characteristics

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S. Chaine

2015-09-01

Full Text Available This work presents a methodology to optimize the controller parameters of doubly fed induction generator modeled for frequency regulation in interconnected two-area wind power integrated thermal power system. The gains of integral controller of automatic generation control loop and the proportional and derivative controllers of doubly fed induction generator inertial control loop are optimized in a coordinated manner by employing the multi-objective non-dominated sorting genetic algorithm-II. To reduce the numbers of optimization parameters, a sensitivity analysis is done to determine that the above mentioned three controller parameters are the most sensitive among the rest others. Non-dominated sorting genetic algorithm-II has depicted better efficiency of optimization compared to the linear programming, genetic algorithm, particle swarm optimization, and cuckoo search algorithm. The performance of the designed optimal controller exhibits robust performance even with the variation in penetration levels of wind energy, disturbances, parameter and operating conditions in the system.

Preparing Pseudo-Pure States in a Quadrupolar Spin System Using Optimal Control

International Nuclear Information System (INIS)

Tan Yi-Peng; Li Jun; Zhou Xian-Yi; Peng Xin-Hua; Du Jiang-Feng; Nie Xin-Fang; Chen Hong-Wei

2012-01-01

Pseudo-pure state (PPS) preparation is crucial in nuclear magnetic resonance quantum computation. There have been some methods in spin-1/2 systems and a few attempts in quadrupolar spin systems. As optimal control via gradient ascent pulses engineering (GRAPE) has been widely used in quantum information science, we apply this technique to PPS preparation in quadrupolar spin systems. This approach shows an effective and fast quantum control method for both the state preparation and the realization of quantum gates in quadrupolar systems

Optimal Active Power Control of A Wind Farm Equipped with Energy Storage System based on Distributed Model Predictive Control

DEFF Research Database (Denmark)

Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai

2016-01-01

This paper presents the Distributed Model Predictive Control (D-MPC) of a wind farm equipped with fast and short-term Energy Storage System (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm...... is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines and the ESS unit, the wind turbine mechanical loads are alleviated. With the fast gradient method, the convergence rate of the DMPC is significantly improved which leads...

Firefly algorithm optimized fuzzy PID controller for AGC of multi-area multi-source power systems with UPFC and SMES

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Pratap Chandra Pradhan

2016-03-01

Full Text Available In this paper, a Firefly Algorithm (FA optimized fuzzy PID controller is proposed for Automatic Generation Control (AGC of multi-area multi-source power system. Initially, a two area six units power system is used and the gains of the fuzzy PID controller are optimized employing FA optimization technique using an ITAE criterion. The superiority of the proposed FA optimized fuzzy PID controller has been demonstrated by comparing the results with some recently published approaches such as optimal control and Differential Evolution (DE optimized PID controller for the identical interconnected power system. Then, physical constraints such as Time Delay (TD, reheat turbine and Generation Rate Constraint (GRC are included in the system model and the superiority of FA is demonstrated by comparing the results over DE, Gravitational Search Algorithm (GSA and Genetic Algorithm (GA optimization techniques for the same interconnected power system. Additionally, a Unified Power Flow Controller (UPFC is placed in the tie-line and Superconducting Magnetic Energy Storage (SMES units are considered in both areas. Simulation results show that the system performances are improved significantly with the proposed UPFC and SMES units. Sensitivity analysis of the system is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Finally, the effectiveness of the proposed controller design is verified by considering different types of load patterns.

Optimization Controller for Mechatronic Sun Tracking System to Improve Performance

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Mustafa Engin

2013-01-01

Full Text Available An embedded system that contains hardware and software was developed for two-axis solar tracking system to improve photovoltaic panel utilization. The hardware section of the embedded system consists of a 32-bit ARM core microcontroller, motor driver circuits, a motion control unit, pyranometer, GPS receiver, and an anemometer. The real-time control algorithm enables the solar tracker to operate automatically without external control as a stand-alone system, combining the advantages of the open-loop and the closed-loop control methods. The pyranometer is employed to continuously send radiation data to the controller if the measured radiation is above the lower radiation limit the photovoltaic panel can generate power, guaranteeing the solar tracking process to be highly efficient. The anemometer is utilized in the system to ensure that the solar tracking procedure halts under high wind speed conditions to protect the entire system. Latitude, longitude, altitude, date, and real-time clock data are provided by GPS receiver. The algorithm calculates solar time using astronomical equations with GPS data and converts it to pulse-width modulated motor control signal. The overall objective of this study is to develop a control algorithm that improves performance and reliability of the two-axis solar tracker, focusing on optimization of the controller board, drive hardware, and software.

Nonfragile Guaranteed Cost Control and Optimization for Interconnected Systems of Neutral Type

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Heli Hu

2013-01-01

Full Text Available The design and optimization problems of the nonfragile guaranteed cost control are investigated for a class of interconnected systems of neutral type. A novel scheme, viewing the interconnections with time-varying delays as effective information but not disturbances, is developed to decrease the conservatism. Many techniques on decomposing and magnifying the matrices are utilized to obtain the guaranteed cost of the considered system. Also, an algorithm is proposed to solve the nonlinear problem of the interconnected matrices. Based on this algorithm, the minimization of the guaranteed cost of the considered system is obtained by optimization. Further, the state feedback control is extended to the case in which the underlying system is dependent on uncertain parameters. Finally, two numerical examples are given to illustrate the proposed method, and some comparisons are made to show the advantages of the schemes of dealing with the interconnections.

A generic methodology for the optimisation of sewer systems using stochastic programming and self-optimizing control.

Science.gov (United States)

Mauricio-Iglesias, Miguel; Montero-Castro, Ignacio; Mollerup, Ane L; Sin, Gürkan

2015-05-15

The design of sewer system control is a complex task given the large size of the sewer networks, the transient dynamics of the water flow and the stochastic nature of rainfall. This contribution presents a generic methodology for the design of a self-optimising controller in sewer systems. Such controller is aimed at keeping the system close to the optimal performance, thanks to an optimal selection of controlled variables. The definition of an optimal performance was carried out by a two-stage optimisation (stochastic and deterministic) to take into account both the overflow during the current rain event as well as the expected overflow given the probability of a future rain event. The methodology is successfully applied to design an optimising control strategy for a subcatchment area in Copenhagen. The results are promising and expected to contribute to the advance of the operation and control problem of sewer systems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimal Stochastic Control Problem for General Linear Dynamical Systems in Neuroscience

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Yan Chen

2017-01-01

Full Text Available This paper considers a d-dimensional stochastic optimization problem in neuroscience. Suppose the arm’s movement trajectory is modeled by high-order linear stochastic differential dynamic system in d-dimensional space, the optimal trajectory, velocity, and variance are explicitly obtained by using stochastic control method, which allows us to analytically establish exact relationships between various quantities. Moreover, the optimal trajectory is almost a straight line for a reaching movement; the optimal velocity bell-shaped and the optimal variance are consistent with the experimental Fitts law; that is, the longer the time of a reaching movement, the higher the accuracy of arriving at the target position, and the results can be directly applied to designing a reaching movement performed by a robotic arm in a more general environment.

Optimal control for wind turbine system via state-space method

Science.gov (United States)

Shanoob, Mudhafar L.

Renewable energy is becoming a fascinating research interest in future energy production because it is green and does not pollute nature. Wind energy is an excellent example of renewable resources that are evolving. Throughout the history of humanity, wind energy has been used. In ancient time, it was used to grind seeds, sailing etc. Nowadays, wind energy has been used to generate electrical power. Researchers have done a lot of research about using a wind source to generate electricity. As wind flow is not reliable, there is a challenge to get stable electricity out of this varying wind. This problem leads to the use of different control methods and the optimization of these methods to get a stable and reliable electrical energy. In this research, a wind turbine system is considered to study the transient and the steady-state stability; consisting of the aerodynamic system, drive train and generator. The Doubly Feed Induction Generator (DFIG) type generator is used in this thesis. The wind turbine system is connected to power system network. The grid is an infinite bus bar connected to a short transmission line and transformer. The generator is attached to the grid from the stator side. State-space method is used to model the wind turbine parts. The system is modeled and controlled using MATLAB/Simulation software. First, the current-mode control method (PVdq) with (PI) regulator is operated as a reference to find how the system reacts to an unexpected disturbance on the grid side or turbine side. The controller is operated with three scenarios of disruption: Disturbance-mechanical torque input, Step disturbance in the electrical torque reference and Fault Ride-through. In the simulation results, the time response and the transient stability of the system is a product of the disturbances that take a long time to settle. So, for this reason, Linear Quadratic Regulation (LQR) optimal control is utilized to solve this problem. The LQR method is designed based on

Optimal Control Strategies in a Two Dimensional Differential Game Using Linear Equation under a Perturbed System

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Musa Danjuma SHEHU

2008-06-01

Full Text Available This paper lays emphasis on formulation of two dimensional differential games via optimal control theory and consideration of control systems whose dynamics is described by a system of Ordinary Differential equation in the form of linear equation under the influence of two controls U(. and V(.. Base on this, strategies were constructed. Hence we determine the optimal strategy for a control say U(. under a perturbation generated by the second control V(. within a given manifold M.

The optimal operation of cooling tower systems with variable-frequency control

Science.gov (United States)

Cao, Yong; Huang, Liqing; Cui, Zhiguo; Liu, Jing

2018-02-01

This study investigates the energy performance of chiller and cooling tower systems integrated with variable-frequency control for cooling tower fans and condenser water pumps. With regard to an example chiller system serving an office building, Chiller and cooling towers models were developed to assess how different variable-frequency control methods of cooling towers fans and condenser water pumps influence the trade-off between the chiller power, pump power and fan power under various operating conditions. The matching relationship between the cooling tower fans frequency and condenser water pumps frequency at optimal energy consumption of the system is introduced to achieve optimum system performance.

Optimal Control of Complex Systems Based on Improved Dual Heuristic Dynamic Programming Algorithm

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Hui Li

2017-01-01

Full Text Available When applied to solving the data modeling and optimal control problems of complex systems, the dual heuristic dynamic programming (DHP technique, which is based on the BP neural network algorithm (BP-DHP, has difficulty in prediction accuracy, slow convergence speed, poor stability, and so forth. In this paper, a dual DHP technique based on Extreme Learning Machine (ELM algorithm (ELM-DHP was proposed. Through constructing three kinds of network structures, the paper gives the detailed realization process of the DHP technique in the ELM. The controller designed upon the ELM-DHP algorithm controlled a molecular distillation system with complex features, such as multivariability, strong coupling, and nonlinearity. Finally, the effectiveness of the algorithm is verified by the simulation that compares DHP and HDP algorithms based on ELM and BP neural network. The algorithm can also be applied to solve the data modeling and optimal control problems of similar complex systems.

Parametric optimal control of uncertain systems under an optimistic value criterion

Science.gov (United States)

Li, Bo; Zhu, Yuanguo

2018-01-01

It is well known that the optimal control of a linear quadratic model is characterized by the solution of a Riccati differential equation. In many cases, the corresponding Riccati differential equation cannot be solved exactly such that the optimal feedback control may be a complex time-oriented function. In this article, a parametric optimal control problem of an uncertain linear quadratic model under an optimistic value criterion is considered for simplifying the expression of optimal control. Based on the equation of optimality for the uncertain optimal control problem, an approximation method is presented to solve it. As an application, a two-spool turbofan engine optimal control problem is given to show the utility of the proposed model and the efficiency of the presented approximation method.

Adaptive GSA-based optimal tuning of PI controlled servo systems with reduced process parametric sensitivity, robust stability and controller robustness.

Science.gov (United States)

Precup, Radu-Emil; David, Radu-Codrut; Petriu, Emil M; Radac, Mircea-Bogdan; Preitl, Stefan

2014-11-01

This paper suggests a new generation of optimal PI controllers for a class of servo systems characterized by saturation and dead zone static nonlinearities and second-order models with an integral component. The objective functions are expressed as the integral of time multiplied by absolute error plus the weighted sum of the integrals of output sensitivity functions of the state sensitivity models with respect to two process parametric variations. The PI controller tuning conditions applied to a simplified linear process model involve a single design parameter specific to the extended symmetrical optimum (ESO) method which offers the desired tradeoff to several control system performance indices. An original back-calculation and tracking anti-windup scheme is proposed in order to prevent the integrator wind-up and to compensate for the dead zone nonlinearity of the process. The minimization of the objective functions is carried out in the framework of optimization problems with inequality constraints which guarantee the robust stability with respect to the process parametric variations and the controller robustness. An adaptive gravitational search algorithm (GSA) solves the optimization problems focused on the optimal tuning of the design parameter specific to the ESO method and of the anti-windup tracking gain. A tuning method for PI controllers is proposed as an efficient approach to the design of resilient control systems. The tuning method and the PI controllers are experimentally validated by the adaptive GSA-based tuning of PI controllers for the angular position control of a laboratory servo system.

Optimal control in thermal engineering

CERN Document Server

Badescu, Viorel

2017-01-01

This book is the first major work covering applications in thermal engineering and offering a comprehensive introduction to optimal control theory, which has applications in mechanical engineering, particularly aircraft and missile trajectory optimization. The book is organized in three parts: The first part includes a brief presentation of function optimization and variational calculus, while the second part presents a summary of the optimal control theory. Lastly, the third part describes several applications of optimal control theory in solving various thermal engineering problems. These applications are grouped in four sections: heat transfer and thermal energy storage, solar thermal engineering, heat engines and lubrication.Clearly presented and easy-to-use, it is a valuable resource for thermal engineers and thermal-system designers as well as postgraduate students.

Fuzzy control for optimal operation of complex chilling systems; Betriebsoptimierung von komplexen Kaelteanlagen mit Fuzzy-Control

Energy Technology Data Exchange (ETDEWEB)

Talebi-Daryani, R. [Fachhochschule Koeln (Germany). Lehrgebiet und Lab. fuer Regelungs- und Gebaeudeleittechnik; Luther, C. [JCI Regelungstechnik GmbH, Koeln (Germany)

1998-05-01

The optimization potentials for the operation of chilling systems within the building supervisory control systems are limited to abilities of PLC functions with their binary logic. The aim of this project is to replace inefficient PLC-solutions for the operation of chilling system by a Fuzzy control system. Optimal operation means: reducing operation time and operation costs of the system, reducing cooling energy generation- and consumption costs. Analysis of the thermal behaviour of the building and the chilling system is necessary, in order to find the current efficient cooling potentials and cooling methods during the operation. Three different Fuzzy controller have been developed with a total rule number of just 70. This realized Fuzzy control system is able to forecast the maximum cooling power of the building, but also to determine the cooling potential of the out door air. This new Fuzzy control system has been successfully commissioned, and remarkable improvement of the system behaviour is reached. Comparison of the system behaviour before and after the implementation of Fuzzy control system proved the benefits of the Fuzzy logic based operation system realized here. The system described here is a joint project between the University of applied sciences Cologne, and Johnson Controls International Cologne. The Fuzzy software tool used here (SUCO soft Fuzzy TECH 4.0), was provided by Kloeckner Moeller Bonn. (orig.) [Deutsch] Die Betriebsoptimierung von Kaelteanlagen innerhalb von Gebaeudeleitsystemen ist auf die Faehigkeiten von logischen Steuerverknuepfungen der Digitaltechnik begrenzt. In diesem Zusammenhang kann nur ein geringer Anteil der Information ueber das thermische Speicherverhalten des jeweiligen Gebaeudes herangezogen werden. Ziel des vorliegenden Projektes war es, die unzureichenden logischen Steuerverknuepfungen durch ein Fuzzy-Control-System zu ersetzen, um die Arbeitsweise der Kaelteanlage zu optimieren. Die Optimierungskriterien dieses

ORACLS- OPTIMAL REGULATOR ALGORITHMS FOR THE CONTROL OF LINEAR SYSTEMS (DEC VAX VERSION)

Science.gov (United States)

Frisch, H.

1994-01-01

This control theory design package, called Optimal Regulator Algorithms for the Control of Linear Systems (ORACLS), was developed to aid in the design of controllers and optimal filters for systems which can be modeled by linear, time-invariant differential and difference equations. Optimal linear quadratic regulator theory, currently referred to as the Linear-Quadratic-Gaussian (LQG) problem, has become the most widely accepted method of determining optimal control policy. Within this theory, the infinite duration time-invariant problems, which lead to constant gain feedback control laws and constant Kalman-Bucy filter gains for reconstruction of the system state, exhibit high tractability and potential ease of implementation. A variety of new and efficient methods in the field of numerical linear algebra have been combined into the ORACLS program, which provides for the solution to time-invariant continuous or discrete LQG problems. The ORACLS package is particularly attractive to the control system designer because it provides a rigorous tool for dealing with multi-input and multi-output dynamic systems in both continuous and discrete form. The ORACLS programming system is a collection of subroutines which can be used to formulate, manipulate, and solve various LQG design problems. The ORACLS program is constructed in a manner which permits the user to maintain considerable flexibility at each operational state. This flexibility is accomplished by providing primary operations, analysis of linear time-invariant systems, and control synthesis based on LQG methodology. The input-output routines handle the reading and writing of numerical matrices, printing heading information, and accumulating output information. The basic vector-matrix operations include addition, subtraction, multiplication, equation, norm construction, tracing, transposition, scaling, juxtaposition, and construction of null and identity matrices. The analysis routines provide for the following

Optimal control of a CSTR process

Directory of Open Access Journals (Sweden)

A. Soukkou

2008-12-01

Full Text Available Designing an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains of the Robust and Optimal Takagi Sugeno Fuzzy Controller (ROFLC. The control signal thus obtained will minimize a performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the ROFLC. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the ROFLC are compared to these found by the traditional PD controller with Genetic Optimization (PD_GO. Simulations demonstrate that the proposed ROFLC and PD_GO has successfully met the design specifications.

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

Science.gov (United States)

Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

Optimal Control of a Wind Farm Group Using the WindEx System

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Piotr Kacejko

2014-09-01

Full Text Available The aim of this paper is to present achievements obtained in implementing the framework project N R01 0021 06 in the Power System Department of Lublin University of Technology. The result of the work was “A system of optimal wind farm power control in the conditions of limited transmission capabilities of power networks”, which one of two main modules is a state estimator. The featured wind farm control system was integrated with a SCADA dispatcher system WindEx using the WebSVC service.

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

Science.gov (United States)

Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu

2016-01-01

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

Vector-valued measure and the necessary conditions for the optimal control problems of linear systems

International Nuclear Information System (INIS)

Xunjing, L.

1981-12-01

The vector-valued measure defined by the well-posed linear boundary value problems is discussed. The maximum principle of the optimal control problem with non-convex constraint is proved by using the vector-valued measure. Especially, the necessary conditions of the optimal control of elliptic systems is derived without the convexity of the control domain and the cost function. (author)

Impact of Thyristors Controlled Series Capacitor Devices and Optimal Power Flow on Power Systems

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Fatiha LAKDJA

2010-12-01

Full Text Available This paper presents an algorithm, for solving the Optimal Power Flow problem with flexible AC transmission systems (FACTS. The type of FACTS devices is used: thyristor-controlled series capacitor (TCSC. A method to determine the optimal location of thyristor controlled series compensators has been suggested. The proposed approaches have been implemented on an adapted IEEE 26 bus system. The simulation results are discussed to show the performance of the proposed algorithm and our “FACTS programmer “simulator technique, which are compared with TCSC and without TCSC.

Advanced chemistry management system to optimize BWR chemistry control

International Nuclear Information System (INIS)

Maeda, K.; Nagasawa, K.

2002-01-01

BWR plant chemistry control has close relationships among nuclear safety, component reliability, radiation field management and fuel integrity. Advanced technology is required to improve chemistry control [1,3,6,7,10,11]. Toshiba has developed TACMAN (Toshiba Advanced Chemistry Management system) to support BWR chemistry control. The TACMAN has been developed as response to utilities' years of requirements to keep plant operation safety, reliability and cost benefit. The advanced technology built into the TACMAN allows utilities to make efficient chemistry control and to keep cost benefit. TACMAN is currently being used in response to the needs for tools those plant chemists and engineers could use to optimize and identify plant chemistry conditions continuously. If an incipient condition or anomaly is detected at early stage, root causes evaluation and immediate countermeasures can be provided. Especially, the expert system brings numerous and competitive advantages not only to improve plant chemistry reliability but also to standardize and systematize know-how, empirical knowledge and technologies in BWR chemistry This paper shows detail functions of TACMAN and practical results to evaluate actual plant. (authors)

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

Science.gov (United States)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two output drone flight control system.

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.

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

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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.

Rovibrational controlled-NOT gates using optimized stimulated Raman adiabatic passage techniques and optimal control theory

International Nuclear Information System (INIS)

Sugny, D.; Bomble, L.; Ribeyre, T.; Dulieu, O.; Desouter-Lecomte, M.

2009-01-01

Implementation of quantum controlled-NOT (CNOT) gates in realistic molecular systems is studied using stimulated Raman adiabatic passage (STIRAP) techniques optimized in the time domain by genetic algorithms or coupled with optimal control theory. In the first case, with an adiabatic solution (a series of STIRAP processes) as starting point, we optimize in the time domain different parameters of the pulses to obtain a high fidelity in two realistic cases under consideration. A two-qubit CNOT gate constructed from different assignments in rovibrational states is considered in diatomic (NaCs) or polyatomic (SCCl 2 ) molecules. The difficulty of encoding logical states in pure rotational states with STIRAP processes is illustrated. In such circumstances, the gate can be implemented by optimal control theory and the STIRAP sequence can then be used as an interesting trial field. We discuss the relative merits of the two methods for rovibrational computing (structure of the control field, duration of the control, and efficiency of the optimization).

Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization algorithm

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Rabindra Kumar Sahu

2016-03-01

Full Text Available This paper presents the design and analysis of Proportional-Integral-Double Derivative (PIDD controller for Automatic Generation Control (AGC of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization (TLBO algorithm. At first, a two-area reheat thermal power system with appropriate Generation Rate Constraint (GRC is considered. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the PIDD controller. The superiority of the proposed TLBO based PIDD controller has been demonstrated by comparing the results with recently published optimization technique such as hybrid Firefly Algorithm and Pattern Search (hFA-PS, Firefly Algorithm (FA, Bacteria Foraging Optimization Algorithm (BFOA, Genetic Algorithm (GA and conventional Ziegler Nichols (ZN for the same interconnected power system. Also, the proposed approach has been extended to two-area power system with diverse sources of generation like thermal, hydro, wind and diesel units. The system model includes boiler dynamics, GRC and Governor Dead Band (GDB non-linearity. It is observed from simulation results that the performance of the proposed approach provides better dynamic responses by comparing the results with recently published in the literature. Further, the study is extended to a three unequal-area thermal power system with different controllers in each area and the results are compared with published FA optimized PID controller for the same system under study. Finally, sensitivity analysis is performed by varying the system parameters and operating load conditions in the range of ±25% from their nominal values to test the robustness.

Optimal control of population and coherence in three-level Λ systems

Science.gov (United States)

Kumar, Praveen; Malinovskaya, Svetlana A.; Malinovsky, Vladimir S.

2011-08-01

Optimal control theory (OCT) implementations for an efficient population transfer and creation of maximum coherence in a three-level system are considered. We demonstrate that the half-stimulated Raman adiabatic passage scheme for creation of the maximum Raman coherence is the optimal solution according to the OCT. We also present a comparative study of several implementations of OCT applied to the complete population transfer and creation of the maximum coherence. Performance of the conjugate gradient method, the Zhu-Rabitz method and the Krotov method has been analysed.

Optimal control of population and coherence in three-level Λ systems

International Nuclear Information System (INIS)

Kumar, Praveen; Malinovskaya, Svetlana A; Malinovsky, Vladimir S

2011-01-01

Optimal control theory (OCT) implementations for an efficient population transfer and creation of maximum coherence in a three-level system are considered. We demonstrate that the half-stimulated Raman adiabatic passage scheme for creation of the maximum Raman coherence is the optimal solution according to the OCT. We also present a comparative study of several implementations of OCT applied to the complete population transfer and creation of the maximum coherence. Performance of the conjugate gradient method, the Zhu-Rabitz method and the Krotov method has been analysed.

Model-Based Predictive Control Scheme for Cost Optimization and Balancing Services for Supermarket Refrigeration Systems

DEFF Research Database (Denmark)

Weerts, Hermanus H. M.; Shafiei, Seyed Ehsan; Stoustrup, Jakob

2014-01-01

A new formulation of model predictive control for supermarket refrigeration systems is proposed to facilitate the regulatory power services as well as energy cost optimization of such systems in the smart grid. Nonlinear dynamics existed in large-scale refrigeration plants challenges the predictive...... control design. It is however shown that taking into account the knowledge of different time scales in the dynamical subsystems makes possible a linear formulation of a centralized predictive controller. A realistic scenario of regulatory power services in the smart grid is considered and formulated...... in the same objective as of cost optimization one. A simulation benchmark validated against real data and including significant dynamics of the system are employed to show the effectiveness of the proposed control scheme....

PID Controller Design of Nonlinear System using a New Modified Particle Swarm Optimization with Time-Varying Constriction Coefficient

Directory of Open Access Journals (Sweden)

Alrijadjis .

2014-12-01

Full Text Available The proportional integral derivative (PID controllers have been widely used in most process control systems for a long time. However, it is a very important problem how to choose PID parameters, because these parameters give a great influence on the control performance. Especially, it is difficult to tune these parameters for nonlinear systems. In this paper, a new modified particle swarm optimization (PSO is presented to search for optimal PID parameters for such system. The proposed algorithm is to modify constriction coefficient which is nonlinearly decreased time-varying for improving the final accuracy and the convergence speed of PSO. To validate the control performance of the proposed method, a typical nonlinear system control, a continuous stirred tank reactor (CSTR process, is illustrated. The results testify that a new modified PSO algorithm can perform well in the nonlinear PID control system design in term of lesser overshoot, rise-time, settling-time, IAE and ISE. Keywords: PID controller, Particle Swarm Optimization (PSO,constriction factor, nonlinear system.

Multi-objective optimization of the control strategy of electric vehicle electro-hydraulic composite braking system with genetic algorithm

Directory of Open Access Journals (Sweden)

Zhang Fengjiao

2015-03-01

Full Text Available Optimization of the control strategy plays an important role in improving the performance of electric vehicles. In order to improve the braking stability and recover the braking energy, a multi-objective genetic algorithm is applied to optimize the key parameters in the control strategy of electric vehicle electro-hydraulic composite braking system. Various limitations are considered in the optimization process, and the optimization results are verified by a software simulation platform of electric vehicle regenerative braking system in typical brake conditions. The results show that optimization objectives achieved a good astringency, and the optimized control strategy can increase the brake energy recovery effectively under the condition of ensuring the braking stability.

Optimal control on hybrid ode systems with application to a tick disease model.

Science.gov (United States)

Ding, Wandi

2007-10-01

We are considering an optimal control problem for a type of hybrid system involving ordinary differential equations and a discrete time feature. One state variable has dynamics in only one season of the year and has a jump condition to obtain the initial condition for that corresponding season in the next year. The other state variable has continuous dynamics. Given a general objective functional, existence, necessary conditions and uniqueness for an optimal control are established. We apply our approach to a tick-transmitted disease model with age structure in which the tick dynamics changes seasonally while hosts have continuous dynamics. The goal is to maximize disease-free ticks and minimize infected ticks through an optimal control strategy of treatment with acaricide. Numerical examples are given to illustrate the results.

Optimization and Control of Electric Power Systems

Energy Technology Data Exchange (ETDEWEB)

Lesieutre, Bernard C. [Univ. of Wisconsin, Madison, WI (United States); Molzahn, Daniel K. [Univ. of Wisconsin, Madison, WI (United States)

2014-10-17

The analysis and optimization needs for planning and operation of the electric power system are challenging due to the scale and the form of model representations. The connected network spans the continent and the mathematical models are inherently nonlinear. Traditionally, computational limits have necessitated the use of very simplified models for grid analysis, and this has resulted in either less secure operation, or less efficient operation, or both. The research conducted in this project advances techniques for power system optimization problems that will enhance reliable and efficient operation. The results of this work appear in numerous publications and address different application problems include optimal power flow (OPF), unit commitment, demand response, reliability margins, planning, transmission expansion, as well as general tools and algorithms.

Optimization and Optimal Control

CERN Document Server

Chinchuluun, Altannar; Enkhbat, Rentsen; Tseveendorj, Ider

2010-01-01

During the last four decades there has been a remarkable development in optimization and optimal control. Due to its wide variety of applications, many scientists and researchers have paid attention to fields of optimization and optimal control. A huge number of new theoretical, algorithmic, and computational results have been observed in the last few years. This book gives the latest advances, and due to the rapid development of these fields, there are no other recent publications on the same topics. Key features: Provides a collection of selected contributions giving a state-of-the-art accou

Optimal control of distributed parameter system with incomplete information about the initial condition

International Nuclear Information System (INIS)

Kotarski, W.; Kowalewski, A.

1982-03-01

In this paper we consider an optimal control problem for a system described by a linear partial differential equation of the parabolic type with Dirichlet's boundary condition. We impose some constraints on the control. The performance functional has the integral form. The control time T is fixed. The initial condition is not given by a known function but belongs to a certain set (incomplete information about the initial state). The problem formulated in this paper describes the process of optimal heating, of which we do not have exact information about the initial temperature on the heated object. We present an example in which the set of admissible controls and one of initial conditions are given by means of the norm constraints too. The application of the well-known projective gradient method in the Hilbert space allows us to obtain the numerical solution for our optimization problem. (author)

A policy iteration approach to online optimal control of continuous-time constrained-input systems.

Science.gov (United States)

Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L

2013-09-01

This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach. Copyright © 2013 ISA. All rights reserved.

System for verifiable CT radiation dose optimization based on image quality. part II. process control system.

Science.gov (United States)

Larson, David B; Malarik, Remo J; Hall, Seth M; Podberesky, Daniel J

2013-10-01

To evaluate the effect of an automated computed tomography (CT) radiation dose optimization and process control system on the consistency of estimated image noise and size-specific dose estimates (SSDEs) of radiation in CT examinations of the chest, abdomen, and pelvis. This quality improvement project was determined not to constitute human subject research. An automated system was developed to analyze each examination immediately after completion, and to report individual axial-image-level and study-level summary data for patient size, image noise, and SSDE. The system acquired data for 4 months beginning October 1, 2011. Protocol changes were made by using parameters recommended by the prediction application, and 3 months of additional data were acquired. Preimplementation and postimplementation mean image noise and SSDE were compared by using unpaired t tests and F tests. Common-cause variation was differentiated from special-cause variation by using a statistical process control individual chart. A total of 817 CT examinations, 490 acquired before and 327 acquired after the initial protocol changes, were included in the study. Mean patient age and water-equivalent diameter were 12.0 years and 23.0 cm, respectively. The difference between actual and target noise increased from -1.4 to 0.3 HU (P process control chart identified several special causes of variation. Implementation of an automated CT radiation dose optimization system led to verifiable simultaneous decrease in image noise variation and SSDE. The automated nature of the system provides the opportunity for consistent CT radiation dose optimization on a broad scale. © RSNA, 2013.

Dynamic optimization and adaptive controller design

Science.gov (United States)

Inamdar, S. R.

2010-10-01

In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.

Optimal control theory an introduction

CERN Document Server

Kirk, Donald E

2004-01-01

Optimal control theory is the science of maximizing the returns from and minimizing the costs of the operation of physical, social, and economic processes. Geared toward upper-level undergraduates, this text introduces three aspects of optimal control theory: dynamic programming, Pontryagin's minimum principle, and numerical techniques for trajectory optimization.Chapters 1 and 2 focus on describing systems and evaluating their performances. Chapter 3 deals with dynamic programming. The calculus of variations and Pontryagin's minimum principle are the subjects of chapters 4 and 5, and chapter

Optimal control linear quadratic methods

CERN Document Server

Anderson, Brian D O

2007-01-01

This augmented edition of a respected text teaches the reader how to use linear quadratic Gaussian methods effectively for the design of control systems. It explores linear optimal control theory from an engineering viewpoint, with step-by-step explanations that show clearly how to make practical use of the material.The three-part treatment begins with the basic theory of the linear regulator/tracker for time-invariant and time-varying systems. The Hamilton-Jacobi equation is introduced using the Principle of Optimality, and the infinite-time problem is considered. The second part outlines the

Engineering applications of discrete-time optimal control

DEFF Research Database (Denmark)

Vidal, Rene Victor Valqui; Ravn, Hans V.

1990-01-01

Many problems of design and operation of engineering systems can be formulated as optimal control problems where time has been discretisized. This is also true even if 'time' is not involved in the formulation of the problem, but rather another one-dimensional parameter. This paper gives a review...... of some well-known and new results in discrete time optimal control methods applicable to practical problem solving within engineering. Emphasis is placed on dynamic programming, the classical maximum principle and generalized versions of the maximum principle for optimal control of discrete time systems...

Optimal Design of Complex Passive-Damping Systems for Vibration Control of Large Structures: An Energy-to-Peak Approach

Directory of Open Access Journals (Sweden)

Francisco Palacios-Quiñonero

2014-01-01

Full Text Available We present a new design strategy that makes it possible to synthesize decentralized output-feedback controllers by solving two successive optimization problems with linear matrix inequality (LMI constraints. In the initial LMI optimization problem, two auxiliary elements are computed: a standard state-feedback controller, which can be taken as a reference in the performance assessment, and a matrix that facilitates a proper definition of the main LMI optimization problem. Next, by solving the second optimization problem, the output-feedback controller is obtained. The proposed strategy extends recent results in static output-feedback control and can be applied to design complex passive-damping systems for vibrational control of large structures. More precisely, by taking advantages of the existing link between fully decentralized velocity-feedback controllers and passive linear dampers, advanced active feedback control strategies can be used to design complex passive-damping systems, which combine the simplicity and robustness of passive control systems with the efficiency of active feedback control. To demonstrate the effectiveness of the proposed approach, a passive-damping system for the seismic protection of a five-story building is designed with excellent results.

Optimizing pipeline transportation using a fuzzy controller

Energy Technology Data Exchange (ETDEWEB)

Aramaki, Thiago L.; Correa, Joao L. L.; Montalvoa, Antonio F. F. [National Control and Operation Center Tranpetro, Rio de Janeiro, (Brazil)

2010-07-01

The optimization of pipeline transportation is a big concern for the transporter companies. This paper is the third of a series of three articles which investigated the application of a system to simulate the human ability to operate a pipeline in an optimized way. The present paper presents the development of a proportional integral (PI) fuzzy controller, in order to optimize pipeline transportation capacity. The fuzzy adaptive PI controller system was developed and tested with a hydraulic simulator. On-field data were used from the OSBRA pipeline. The preliminary tests showed that the performance of the software simulation was satisfactory. It varied the set-point of the conventional controller within the limits of flow meters. The transport capacity of the pipe was maximize without compromising the integrity of the commodities transported. The system developed proved that it can be easily deployed as a specialist optimizing system to be added to SCADA systems.

OPTIMAL CONTROL OF A NONLINEAR COUPLED ELECTROMAGNETIC INDUCTION HEATING SYSTEM WITH POINTWISE STATE CONSTRAINTS

Directory of Open Access Journals (Sweden)

Irwin Yousept

2010-07-01

Full Text Available An optimal control problem arising in the context of 3D electromagnetic induction heating is investigated. The state equation is given by a quasilinear stationary heat equation coupled with a semilinear time harmonic eddy current equation. The temperature-dependent electrical conductivity and the presence of pointwise inequality state-constraints represent the main challenge of the paper. In the first part of the paper, the existence and regularity of the state are addressed. The second part of the paper deals with the analysis of the corresponding linearized equation. Some suffcient conditions are presented which guarantee thesolvability of the linearized system. The final part of the paper is concerned with the optimal control. The aim of the optimization is to find the optimal voltage such that a desired temperature can be achieved optimally. The corresponding first-order necessary optimality condition is presented.

A new approach of optimal control for a class of continuous-time chaotic systems by an online ADP algorithm

Science.gov (United States)

Song, Rui-Zhuo; Xiao, Wen-Dong; Wei, Qing-Lai

2014-05-01

We develop an online adaptive dynamic programming (ADP) based optimal control scheme for continuous-time chaotic systems. The idea is to use the ADP algorithm to obtain the optimal control input that makes the performance index function reach an optimum. The expression of the performance index function for the chaotic system is first presented. The online ADP algorithm is presented to achieve optimal control. In the ADP structure, neural networks are used to construct a critic network and an action network, which can obtain an approximate performance index function and the control input, respectively. It is proven that the critic parameter error dynamics and the closed-loop chaotic systems are uniformly ultimately bounded exponentially. Our simulation results illustrate the performance of the established optimal control method.

Preliminary Study on Structural Optimization with Control Variables Using Equivalent Static Loads for Spring-damper Control Systems

International Nuclear Information System (INIS)

Yoo, Nam-sun; Jung, Ui-Jin; Park, Gyung-Jin; Kim, Tai-Kyung

2014-01-01

An optimization method is proposed for the simultaneous design of structural and control systems using the equivalent static loads. In the past researches, the control parameters of such feedback gains are obtained to improve some performance in the steady-state. However, the actuators which have position and velocity feedback gains should be designed to exhibit a good performance in the time domain. In other words, the system analysis should be conducted for the transient-state in dynamic manner. In this research, a new equivalent static loads method is presented to treat the control variables as the design variables. The equivalent static loads (ESLs) set is defined as a static load set which generates the same displacement field as that from dynamic loads at a certain time. The calculated sets of ESLs are applied as multiple loading conditions in the optimization process. Several examples are solved to validate the proposed method

Distributed optimization system and method

Science.gov (United States)

Hurtado, John E.; Dohrmann, Clark R.; Robinett, III, Rush D.

2003-06-10

A search system and method for controlling multiple agents to optimize an objective using distributed sensing and cooperative control. The search agent can be one or more physical agents, such as a robot, and can be software agents for searching cyberspace. The objective can be: chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, explosive sources, time dependent sources, time independent sources, function surfaces, maximization points, minimization points, and optimal control of a system such as a communication system, an economy, a crane, and a multi-processor computer.

Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

International Nuclear Information System (INIS)

Wei Qing-Lai; Song Rui-Zhuo; Xiao Wen-Dong; Sun Qiu-Ye

2015-01-01

This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method. (paper)

Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

Science.gov (United States)

Yang, Xiong; Liu, Derong; Wang, Ding

2014-03-01

In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

Gradient Optimization for Analytic conTrols - GOAT

Science.gov (United States)

Assémat, Elie; Machnes, Shai; Tannor, David; Wilhelm-Mauch, Frank

Quantum optimal control becomes a necessary step in a number of studies in the quantum realm. Recent experimental advances showed that superconducting qubits can be controlled with an impressive accuracy. However, most of the standard optimal control algorithms are not designed to manage such high accuracy. To tackle this issue, a novel quantum optimal control algorithm have been introduced: the Gradient Optimization for Analytic conTrols (GOAT). It avoids the piecewise constant approximation of the control pulse used by standard algorithms. This allows an efficient implementation of very high accuracy optimization. It also includes a novel method to compute the gradient that provides many advantages, e.g. the absence of backpropagation or the natural route to optimize the robustness of the control pulses. This talk will present the GOAT algorithm and a few applications to transmons systems.

Robust output LQ optimal control via integral sliding modes

CERN Document Server

Fridman, Leonid; Bejarano, Francisco Javier

2014-01-01

Featuring original research from well-known experts in the field of sliding mode control, this monograph presents new design schemes for implementing LQ control solutions in situations where the output system is the only information provided about the state of the plant. This new design works under the restrictions of matched disturbances without losing its desirable features. On the cutting-edge of optimal control research, Robust Output LQ Optimal Control via Integral Sliding Modes is an excellent resource for both graduate students and professionals involved in linear systems, optimal control, observation of systems with unknown inputs, and automatization. In the theory of optimal control, the linear quadratic (LQ) optimal problem plays an important role due to its physical meaning, and its solution is easily given by an algebraic Riccati equation. This solution turns out to be restrictive, however, because of two assumptions: the system must be free from disturbances and the entire state vector must be kn...

Expert system and process optimization techniques for real-time monitoring and control of plasma processes

Science.gov (United States)

Cheng, Jie; Qian, Zhaogang; Irani, Keki B.; Etemad, Hossein; Elta, Michael E.

1991-03-01

To meet the ever-increasing demand of the rapidly-growing semiconductor manufacturing industry it is critical to have a comprehensive methodology integrating techniques for process optimization real-time monitoring and adaptive process control. To this end we have accomplished an integrated knowledge-based approach combining latest expert system technology machine learning method and traditional statistical process control (SPC) techniques. This knowledge-based approach is advantageous in that it makes it possible for the task of process optimization and adaptive control to be performed consistently and predictably. Furthermore this approach can be used to construct high-level and qualitative description of processes and thus make the process behavior easy to monitor predict and control. Two software packages RIST (Rule Induction and Statistical Testing) and KARSM (Knowledge Acquisition from Response Surface Methodology) have been developed and incorporated with two commercially available packages G2 (real-time expert system) and ULTRAMAX (a tool for sequential process optimization).

Development of Decision-Making Automated System for Optimal Placement of Physical Access Control System’s Elements

Science.gov (United States)

Danilova, Olga; Semenova, Zinaida

2018-04-01

The objective of this study is a detailed analysis of physical protection systems development for information resources. The optimization theory and decision-making mathematical apparatus is used to formulate correctly and create an algorithm of selection procedure for security systems optimal configuration considering the location of the secured object’s access point and zones. The result of this study is a software implementation scheme of decision-making system for optimal placement of the physical access control system’s elements.

The Fundamental Solution and Its Role in the Optimal Control of Infinite Dimensional Neutral Systems

International Nuclear Information System (INIS)

Liu Kai

2009-01-01

In this work, we shall consider standard optimal control problems for a class of neutral functional differential equations in Banach spaces. As the basis of a systematic theory of neutral models, the fundamental solution is constructed and a variation of constants formula of mild solutions is established. We introduce a class of neutral resolvents and show that the Laplace transform of the fundamental solution is its neutral resolvent operator. Necessary conditions in terms of the solutions of neutral adjoint systems are established to deal with the fixed time integral convex cost problem of optimality. Based on optimality conditions, the maximum principle for time varying control domain is presented. Finally, the time optimal control problem to a target set is investigated

Disturbance Error Reduction in Multivariable Optimal Control Systems

Directory of Open Access Journals (Sweden)

Ole A. Solheim

1983-01-01

Full Text Available The paper deals with the design of optimal multivariable controllers, using a modified LQR approach. All controllers discussed contain proportional feedback and, in addition, there may be feedforward, integral action or state estimation.

Extremum-Seeking Control and Applications A Numerical Optimization-Based Approach

CERN Document Server

Zhang, Chunlei

2012-01-01

Extremum seeking control tracks a varying maximum or minimum in a performance function such as a cost. It attempts to determine the optimal performance of a control system as it operates, thereby reducing downtime and the need for system analysis. Extremum Seeking Control and Applications is divided into two parts. In the first, the authors review existing analog optimization based extremum seeking control including gradient, perturbation and sliding mode based control designs. They then propose a novel numerical optimization based extremum seeking control based on optimization algorithms and state regulation. This control design is developed for simple linear time-invariant systems and then extended for a class of feedback linearizable nonlinear systems. The two main optimization algorithms – line search and trust region methods – are analyzed for robustness. Finite-time and asymptotic state regulators are put forward for linear and nonlinear systems respectively. Further design flexibility is achieved u...

Optimized design of resonant controller for stator current harmonic compensation in DFIG wind turbine systems

DEFF Research Database (Denmark)

Liu, Changjin; Chen, Wenjie; Blaabjerg, Frede

2012-01-01

This paper presents an analytical method to optimize the parameters of resonant controller which is used in a Doubly-Fed Induction Generator (DFIG). In the DFIG control system, the fundamental current loop is controlled by PI-controllers, and the stator harmonic current loop is controlled...

Pointwise second-order necessary optimality conditions and second-order sensitivity relations in optimal control

Science.gov (United States)

Frankowska, Hélène; Hoehener, Daniel

2017-06-01

This paper is devoted to pointwise second-order necessary optimality conditions for the Mayer problem arising in optimal control theory. We first show that with every optimal trajectory it is possible to associate a solution p (ṡ) of the adjoint system (as in the Pontryagin maximum principle) and a matrix solution W (ṡ) of an adjoint matrix differential equation that satisfy a second-order transversality condition and a second-order maximality condition. These conditions seem to be a natural second-order extension of the maximum principle. We then prove a Jacobson like necessary optimality condition for general control systems and measurable optimal controls that may be only ;partially singular; and may take values on the boundary of control constraints. Finally we investigate the second-order sensitivity relations along optimal trajectories involving both p (ṡ) and W (ṡ).

Optimal, real-time control--colliders

International Nuclear Information System (INIS)

Spencer, J.E.

1991-05-01

With reasonable definitions, optimal control is possible for both classical and quantal systems with new approaches called PISC(Parallel) and NISC(Neural) from analogy with RISC (Reduced Instruction Set Computing). If control equals interaction, observation and comparison to some figure of merit with interaction via external fields, then optimization comes from varying these fields to give design or operating goals. Structural stability can then give us tolerance and design constraints. But simulations use simplified models, are not in real-time and assume fixed or stationary conditions, so optimal control goes far beyond convergence rates of algorithms. It is inseparable from design and this has many implications for colliders. 12 refs., 3 figs

Optimal Control for the Degenerate Elliptic Logistic Equation

International Nuclear Information System (INIS)

Delgado, M.; Montero, J.A.; Suarez, A.

2002-01-01

We consider the optimal control of harvesting the diffusive degenerate elliptic logistic equation. Under certain assumptions, we prove the existence and uniqueness of an optimal control. Moreover, the optimality system and a characterization of the optimal control are also derived. The sub-supersolution method, the singular eigenvalue problem and differentiability with respect to the positive cone are the techniques used to obtain our results

A Feedback Optimal Control Algorithm with Optimal Measurement Time Points

Directory of Open Access Journals (Sweden)

Felix Jost

2017-02-01

Full Text Available Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning as well as minimizing a given objective (performing. We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example.

Parameter Improved Particle Swarm Optimization Based Direct-Current Vector Control Strategy for Solar PV System

Directory of Open Access Journals (Sweden)

NAMMALVAR, P.

2018-02-01

Full Text Available This paper projects Parameter Improved Particle Swarm Optimization (PIPSO based direct current vector control technology for the integration of photovoltaic array in an AC micro-grid to enhance the system performance and stability. A photovoltaic system incorporated with AC micro-grid is taken as the pursuit of research study. The test system features two power converters namely, PV side converter which consists of DC-DC boost converter with Perturbation and Observe (P&O MPPT control to reap most extreme power from the PV array, and grid side converter which consists of Grid Side-Voltage Source Converter (GS-VSC with proposed direct current vector control strategy. The gain of the proposed controller is chosen from a set of three values obtained using apriori test and tuned through the PIPSO algorithm so that the Integral of Time multiplied Absolute Error (ITAE between the actual and the desired DC link capacitor voltage reaches a minimum and allows the system to extract maximum power from PV system, whereas the existing d-q control strategy is found to perform slowly to control the DC link voltage under varying solar insolation and load fluctuations. From simulation results, it is evident that the proposed optimal control technique provides robust control and improved efficiency.

Optimization of a predictive controller of a pressurized water reactor Xenon oscillation using the particle swarm optimization algorithm

International Nuclear Information System (INIS)

Medeiros, Jose Antonio Carlos Canedo; Machado, Marcelo Dornellas; Lima, Alan Miranda M. de; Schirru, Roberto

2007-01-01

Predictive control systems are control systems that use a model of the controlled system (plant), used to predict the future behavior of the plant allowing the establishment of an anticipative control based on a future condition of the plant, and an optimizer that, considering a future time horizon of the plant output and a recent horizon of the control action, determines the controller's outputs to optimize a performance index of the controlled plant. The predictive control system does not require analytical models of the plant; the model of predictor of the plant can be learned from historical data of operation of the plant. The optimizer of the predictive controller establishes the strategy of the control: the minimization of a performance index (objective function) is done so that the present and future control actions are computed in such a way to minimize the objective function. The control strategy, implemented by the optimizer, induces the formation of an optimal control mechanism whose effect is to reduce the stabilization time, the 'overshoot' and 'undershoot', minimize the control actuation so that a compromise among those objectives is attained. The optimizer of the predictive controller is usually implemented using gradient-based algorithms. In this work we use the Particle Swarm Optimization algorithm (PSO) in the optimizer component of a predictive controller applied in the control of the xenon oscillation of a pressurized water reactor (PWR). The PSO is a stochastic optimization technique applied in several disciplines, simple and capable of providing a global optimal for high complexity problems and difficult to be optimized, providing in many cases better results than those obtained by other conventional and/or other artificial optimization techniques. (author)

Simplex sliding mode control for nonlinear uncertain systems via chaos optimization

International Nuclear Information System (INIS)

Lu, Zhao; Shieh, Leang-San; Chen, Guanrong; Coleman, Norman P.

2005-01-01

As an emerging effective approach to nonlinear robust control, simplex sliding mode control demonstrates some attractive features not possessed by the conventional sliding mode control method, from both theoretical and practical points of view. However, no systematic approach is currently available for computing the simplex control vectors in nonlinear sliding mode control. In this paper, chaos-based optimization is exploited so as to develop a systematic approach to seeking the simplex control vectors; particularly, the flexibility of simplex control is enhanced by making the simplex control vectors dependent on the Euclidean norm of the sliding vector rather than being constant, which result in both reduction of the chattering and speedup of the convergence. Computer simulation on a nonlinear uncertain system is given to illustrate the effectiveness of the proposed control method

Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP.

Science.gov (United States)

Wei, Qinglai; Song, Ruizhuo; Yan, Pengfei

2016-02-01

This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.

Model Based Control of Refrigeration Systems

DEFF Research Database (Denmark)

Larsen, Lars Finn Sloth

for automation of these procedures, that is to incorporate some "intelligence" in the control system, this project was started up. The main emphasis of this work has been on model based methods for system optimizing control in supermarket refrigeration systems. The idea of implementing a system optimizing...... control is to let an optimization procedure take over the task of operating the refrigeration system and thereby replace the role of the operator in the traditional control structure. In the context of refrigeration systems, the idea is to divide the optimizing control structure into two parts: A part...... optimizing the steady state operation "set-point optimizing control" and a part optimizing dynamic behaviour of the system "dynamical optimizing control". A novel approach for set-point optimization will be presented. The general idea is to use a prediction of the steady state, for computation of the cost...

Particle swarm optimization based fuzzy logic controller for autonomous green power energy system with hydrogen storage

International Nuclear Information System (INIS)

Safari, S.; Ardehali, M.M.; Sirizi, M.J.

2013-01-01

Highlights: ► Optimized fuzzy logic controller for a hybrid green power system is developed. ► PSO algorithm is used to optimize membership functions of controller. ► Optimized fuzzy logic controller results in lower O and M costs and LPSP. ► Optimization results in less variation of battery state of charge. - Abstract: The objective of this study is to develop an optimized fuzzy logic controller (FLC) for operating an autonomous hybrid green power system (HGPS) based on the particle swarm optimization (PSO) algorithm. An electrolyzer produces hydrogen from surplus energy generated by the wind turbine and photovoltaic array of HGPS for later use by a fuel cell. The PSO algorithm is used to optimize membership functions of the FLC. The FLC inputs are (a) net power flow and (b) batteries state of charge (SOC) and FLC output determines the time for hydrogen production or consumption. Actual data for weekly residential load, wind speed, ambient temperature, and solar irradiation are used for performance simulation and analysis of the HGPS examined. The weekly operation and maintenance (O and M) costs and the loss of power supply probability (LPSP) are considered in the optimization procedure. It is determined that FLC optimization results in (a) reduced fluctuations in batteries SOC which translates into longer life for batteries and the average SOC is increased by 6.18% and (b) less working hours for fuel cell, when the load is met by wind and PV. It is found that the optimized FLC results in lower O and M costs and LPSP by 57% and 33%, respectively, as compared to its un-optimized counterpart. In addition, a reduction of 18% in investment cost is achievable by optimal sizing and reducing the capacity of HGPS equipment.

Non-Markovianity in the optimal control of an open quantum system described by hierarchical equations of motion

Science.gov (United States)

Mangaud, E.; Puthumpally-Joseph, R.; Sugny, D.; Meier, C.; Atabek, O.; Desouter-Lecomte, M.

2018-04-01

Optimal control theory is implemented with fully converged hierarchical equations of motion (HEOM) describing the time evolution of an open system density matrix strongly coupled to the bath in a spin-boson model. The populations of the two-level sub-system are taken as control objectives; namely, their revivals or exchange when switching off the field. We, in parallel, analyze how the optimal electric field consequently modifies the information back flow from the environment through different non-Markovian witnesses. Although the control field has a dipole interaction with the central sub-system only, its indirect influence on the bath collective mode dynamics is probed through HEOM auxiliary matrices, revealing a strong correlation between control and dissipation during a non-Markovian process. A heterojunction is taken as an illustrative example for modeling in a realistic way the two-level sub-system parameters and its spectral density function leading to a non-perturbative strong coupling regime with the bath. Although, due to strong system-bath couplings, control performances remain rather modest, the most important result is a noticeable increase of the non-Markovian bath response induced by the optimally driven processes.

Hybrid Recurrent Laguerre-Orthogonal-Polynomial NN Control System Applied in V-Belt Continuously Variable Transmission System Using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Chih-Hong Lin

2015-01-01

Full Text Available Because the V-belt continuously variable transmission (CVT system driven by permanent magnet synchronous motor (PMSM has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming procedure. In order to overcome difficulties for design of the linear controllers, the hybrid recurrent Laguerre-orthogonal-polynomial neural network (NN control system which has online learning ability to respond to the system’s nonlinear and time-varying behaviors is proposed to control PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Laguerre-orthogonal-polynomial NN control system consists of an inspector control, a recurrent Laguerre-orthogonal-polynomial NN control with adaptive law, and a recouped control with estimated law. Moreover, the adaptive law of online parameters in the recurrent Laguerre-orthogonal-polynomial NN is derived using the Lyapunov stability theorem. Furthermore, the optimal learning rate of the parameters by means of modified particle swarm optimization (PSO is proposed to achieve fast convergence. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

Time-optimal control of reactor power

International Nuclear Information System (INIS)

Bernard, J.A.

1987-01-01

Control laws that permit adjustments in reactor power to be made in minimum time and without overshoot have been formulated and demonstrated. These control laws which are derived from the standard and alternate dynamic period equations, are closed-form expressions of general applicability. These laws were deduced by noting that if a system is subject to one or more operating constraints, then the time-optimal response is to move the system along these constraints. Given that nuclear reactors are subject to limitations on the allowed reactor period, a time-optimal control law would step the period from infinity to the minimum allowed value, hold the period at that value for the duration of the transient, and then step the period back to infinity. The change in reactor would therefore be accomplished in minimum time. The resulting control laws are superior to other forms of time-optimal control because they are general-purpose, closed-form expressions that are both mathematically tractable and readily implanted. Moreover, these laws include provisions for the use of feedback. The results of simulation studies and actual experiments on the 5 MWt MIT Research Reactor in which these time-optimal control laws were used successfully to adjust the reactor power are presented

Optimal control problem for the extended Fisher–Kolmogorov equation

Indian Academy of Sciences (India)

In this paper, the optimal control problem for the extended Fisher–Kolmogorov equation is studied. The optimal control under boundary condition is given, the existence of optimal solution to the equation is proved and the optimality system is established.

Study on integrated approach of Nuclear Accident Hazard Predicting, Warning, and Optimized Controlling System based on GIS

International Nuclear Information System (INIS)

Tang Lijuan; Huang Shunxiang; Wang Xinming

2012-01-01

The issue of nuclear safety becomes the attention focus of international society after the nuclear accident happened in Fukushima. Aiming at the requirements of the prevention and controlling of Nuclear Accident establishment of Nuclear Accident Hazard Predicting, Warning and optimized Controlling System (NAPWS) is a imperative project that our country and army are desiderating, which includes multiple fields of subject as nuclear physics, atmospheric science, security science, computer science and geographical information technology, etc. Multiplatform, multi-system and multi-mode are integrated effectively based on GIS, accordingly the Predicting, Warning, and Optimized Controlling technology System of Nuclear Accident Hazard is established. (authors)

GA-optimized feedforward-PID tracking control for a rugged electrohydraulic system design.

Science.gov (United States)

Sarkar, B K; Mandal, P; Saha, R; Mookherjee, S; Sanyal, D

2013-11-01

Rugged electrohydraulic systems are preferred for remote and harsh applications. Despite the low bandwidth, large deadband and flow nonlinearities in proportional valves valve and highly nonlinear friction in industry-grade cylinders that comprise rugged systems, their maintenance are much easier than very sophisticated and delicate servocontrol and servocylinder systems. With the target of making the easily maintainable system to perform comparably to a servosystem, a feedforward control has been designed here for compensating the nonlinearities. A PID feedback of the piston displacement has been employed in tandem for absorbing the unmodeled effects. All the controller parameters have been optimized by a real-coded genetic algorithm. The agreement between the achieved real-time responses for step and sinusoidal demands with those achieved by modern servosystems clearly establishes the acceptability of the controller design. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal Self-Tuning PID Controller Based on Low Power Consumption for a Server Fan Cooling System.

Science.gov (United States)

Lee, Chengming; Chen, Rongshun

2015-05-20

Recently, saving the cooling power in servers by controlling the fan speed has attracted considerable attention because of the increasing demand for high-density servers. This paper presents an optimal self-tuning proportional-integral-derivative (PID) controller, combining a PID neural network (PIDNN) with fan-power-based optimization in the transient-state temperature response in the time domain, for a server fan cooling system. Because the thermal model of the cooling system is nonlinear and complex, a server mockup system simulating a 1U rack server was constructed and a fan power model was created using a third-order nonlinear curve fit to determine the cooling power consumption by the fan speed control. PIDNN with a time domain criterion is used to tune all online and optimized PID gains. The proposed controller was validated through experiments of step response when the server operated from the low to high power state. The results show that up to 14% of a server's fan cooling power can be saved if the fan control permits a slight temperature response overshoot in the electronic components, which may provide a time-saving strategy for tuning the PID controller to control the server fan speed during low fan power consumption.

Optimal Self-Tuning PID Controller Based on Low Power Consumption for a Server Fan Cooling System

Directory of Open Access Journals (Sweden)

Chengming Lee

2015-05-01

Full Text Available Recently, saving the cooling power in servers by controlling the fan speed has attracted considerable attention because of the increasing demand for high-density servers. This paper presents an optimal self-tuning proportional-integral-derivative (PID controller, combining a PID neural network (PIDNN with fan-power-based optimization in the transient-state temperature response in the time domain, for a server fan cooling system. Because the thermal model of the cooling system is nonlinear and complex, a server mockup system simulating a 1U rack server was constructed and a fan power model was created using a third-order nonlinear curve fit to determine the cooling power consumption by the fan speed control. PIDNN with a time domain criterion is used to tune all online and optimized PID gains. The proposed controller was validated through experiments of step response when the server operated from the low to high power state. The results show that up to 14% of a server’s fan cooling power can be saved if the fan control permits a slight temperature response overshoot in the electronic components, which may provide a time-saving strategy for tuning the PID controller to control the server fan speed during low fan power consumption.

Optimal control of quantum measurement

Energy Technology Data Exchange (ETDEWEB)

Egger, Daniel; Wilhelm, Frank [Theoretical Physics, Saarland University, 66123 Saarbruecken (Germany)

2015-07-01

Pulses to steer the time evolution of quantum systems can be designed with optimal control theory. In most cases it is the coherent processes that can be controlled and one optimizes the time evolution towards a target unitary process, sometimes also in the presence of non-controllable incoherent processes. Here we show how to extend the GRAPE algorithm in the case where the incoherent processes are controllable and the target time evolution is a non-unitary quantum channel. We perform a gradient search on a fidelity measure based on Choi matrices. We illustrate our algorithm by optimizing a measurement pulse for superconducting phase qubits. We show how this technique can lead to large measurement contrast close to 99%. We also show, within the validity of our model, that this algorithm can produce short 1.4 ns pulses with 98.2% contrast.

Performance of a Nonlinear Real-Time Optimal Control System for HEVs/PHEVs during Car Following

Directory of Open Access Journals (Sweden)

Kaijiang Yu

2014-01-01

Full Text Available This paper presents a real-time optimal control approach for the energy management problem of hybrid electric vehicles (HEVs and plug-in hybrid electric vehicles (PHEVs with slope information during car following. The new features of this study are as follows. First, the proposed method can optimize the engine operating points and the driving profile simultaneously. Second, the proposed method gives the freedom of vehicle spacing between the preceding vehicle and the host vehicle. Third, using the HEV/PHEV property, the desired battery state of charge is designed according to the road slopes for better recuperation of free braking energy. Fourth, all of the vehicle operating modes engine charge, electric vehicle, motor assist and electric continuously variable transmission, and regenerative braking, can be realized using the proposed real-time optimal control approach. Computer simulation results are shown among the nonlinear real-time optimal control approach and the ADVISOR rule-based approach. The conclusion is that the nonlinear real-time optimal control approach is effective for the energy management problem of the HEV/PHEV system during car following.

Optimal Sliding Mode Controllers for Attitude Stabilization of Flexible Spacecraft

Directory of Open Access Journals (Sweden)

Chutiphon Pukdeboon

2011-01-01

Full Text Available The robust optimal attitude control problem for a flexible spacecraft is considered. Two optimal sliding mode control laws that ensure the exponential convergence of the attitude control system are developed. Integral sliding mode control (ISMC is applied to combine the first-order sliding mode with optimal control and is used to control quaternion-based spacecraft attitude manoeuvres with external disturbances and an uncertainty inertia matrix. For the optimal control part the state-dependent Riccati equation (SDRE and optimal Lyapunov techniques are employed to solve the infinite-time nonlinear optimal control problem. The second method of Lyapunov is used to guarantee the stability of the attitude control system under the action of the proposed control laws. An example of multiaxial attitude manoeuvres is presented and simulation results are included to verify the usefulness of the developed controllers.

Reference-shaping adaptive control by using gradient descent optimizers.

Directory of Open Access Journals (Sweden)

Baris Baykant Alagoz

Full Text Available This study presents a model reference adaptive control scheme based on reference-shaping approach. The proposed adaptive control structure includes two optimizer processes that perform gradient descent optimization. The first process is the control optimizer that generates appropriate control signal for tracking of the controlled system output to a reference model output. The second process is the adaptation optimizer that performs for estimation of a time-varying adaptation gain, and it contributes to improvement of control signal generation. Numerical update equations derived for adaptation gain and control signal perform gradient descent optimization in order to decrease the model mismatch errors. To reduce noise sensitivity of the system, a dead zone rule is applied to the adaptation process. Simulation examples show the performance of the proposed Reference-Shaping Adaptive Control (RSAC method for several test scenarios. An experimental study demonstrates application of method for rotor control.

Resonator reset in circuit QED by optimal control for large open quantum systems

Science.gov (United States)

Boutin, Samuel; Andersen, Christian Kraglund; Venkatraman, Jayameenakshi; Ferris, Andrew J.; Blais, Alexandre

2017-10-01

We study an implementation of the open GRAPE (gradient ascent pulse engineering) algorithm well suited for large open quantum systems. While typical implementations of optimal control algorithms for open quantum systems rely on explicit matrix exponential calculations, our implementation avoids these operations, leading to a polynomial speedup of the open GRAPE algorithm in cases of interest. This speedup, as well as the reduced memory requirements of our implementation, are illustrated by comparison to a standard implementation of open GRAPE. As a practical example, we apply this open-system optimization method to active reset of a readout resonator in circuit QED. In this problem, the shape of a microwave pulse is optimized such as to empty the cavity from measurement photons as fast as possible. Using our open GRAPE implementation, we obtain pulse shapes, leading to a reset time over 4 times faster than passive reset.

Study on collaborative optimization control of ventilation and radon reduction system based on multi-agent technology

International Nuclear Information System (INIS)

Dai Jianyong; Meng Lingcong; Zou Shuliang

2015-01-01

According to the radioactive safety features such as radon and its progeny, combined with the theory of ventilation system, structure of multi-agent system for ventilation and radon reduction system is constructed with the application of multi agent technology. The function attribute of the key agent and the connection between the nodes in the multi-agent system are analyzed to establish the distributed autonomous logic structure and negotiation mechanism of multi agent system of ventilation and radon reduction system, and thus to implement the coordination optimization control of the multi-agent system. The example analysis shows that the system structure of the multi-agent system of ventilation and reducing radon system and its collaborative mechanism can improve and optimize the radioactive pollutants control, which provides a theoretical basis and important application prospect. (authors)

Electric power system applications of optimization

CERN Document Server

Momoh, James A

2008-01-01

Introduction Structure of a Generic Electric Power System  Power System Models  Power System Control Power System Security Assessment  Power System Optimization as a Function of Time  Review of Optimization Techniques Applicable to Power Systems Electric Power System Models  Complex Power Concepts Three-Phase Systems Per Unit Representation  Synchronous Machine Modeling Reactive Capability Limits Prime Movers and Governing Systems  Automatic Gain Control Transmission Subsystems  Y-Bus Incorporating the Transformer Effect  Load Models  Available Transfer Capability  Illustrative Examples  Power

Development of a parameter optimization technique for the design of automatic control systems

Science.gov (United States)

Whitaker, P. H.

1977-01-01

Parameter optimization techniques for the design of linear automatic control systems that are applicable to both continuous and digital systems are described. The model performance index is used as the optimization criterion because of the physical insight that can be attached to it. The design emphasis is to start with the simplest system configuration that experience indicates would be practical. Design parameters are specified, and a digital computer program is used to select that set of parameter values which minimizes the performance index. The resulting design is examined, and complexity, through the use of more complex information processing or more feedback paths, is added only if performance fails to meet operational specifications. System performance specifications are assumed to be such that the desired step function time response of the system can be inferred.

Presentation of Malaria Epidemics Using Multiple Optimal Controls

Directory of Open Access Journals (Sweden)

Abid Ali Lashari

2012-01-01

Full Text Available An existing model is extended to assess the impact of some antimalaria control measures, by re-formulating the model as an optimal control problem. This paper investigates the fundamental role of three type of controls, personal protection, treatment, and mosquito reduction strategies in controlling the malaria. We work in the nonlinear optimal control framework. The existence and the uniqueness results of the solution are discussed. A characterization of the optimal control via adjoint variables is established. The optimality system is solved numerically by a competitive Gauss-Seidel-like implicit difference method. Finally, numerical simulations of the optimal control problem, using a set of reasonable parameter values, are carried out to investigate the effectiveness of the proposed control measures.

Real-time simulation requirements for study and optimization of power system controls

Energy Technology Data Exchange (ETDEWEB)

Nakra, Harbans; McCallum, David; Gagnon, Charles [Institut de Recherche d` Hydro-Quebec, Quebec, PQ (Canada); Venne, Andre; Gagnon, Julien [Hydro-Quebec, Montreal, PQ (Canada)

1994-12-31

At the time of ordering for the multi-terminal dc system linking Hydro-Quebec with New England, Hydro-Quebec also ordered functionally duplicate controls of all the converters and installed these in its real time simulation laboratory. The Hydro-Quebec ac system was also simulated in detail and the testing of the controls as thus made possible in a realistic environment. Many field tests were duplicated and many additional tests were done for correction and optimization. This paper describes some of the features of the real-time simulation carried out for this purpose. (author) 3 figs.

Design, Characterization, and Optimization of Controlled Drug Delivery System Containing Antibiotic Drug/s

Directory of Open Access Journals (Sweden)

Apurv Patel

2016-01-01

Full Text Available The objective of this work was design, characterization, and optimization of controlled drug delivery system containing antibiotic drug/s. Osmotic drug delivery system was chosen as controlled drug delivery system. The porous osmotic pump tablets were designed using Plackett-Burman and Box-Behnken factorial design to find out the best formulation. For screening of three categories of polymers, six independent variables were chosen for Plackett-Burman design. Osmotic agent sodium chloride and microcrystalline cellulose, pore forming agent sodium lauryl sulphate and sucrose, and coating agent ethyl cellulose and cellulose acetate were chosen as independent variables. Optimization of osmotic tablets was done by Box-Behnken design by selecting three independent variables. Osmotic agent sodium chloride, pore forming agent sodium lauryl sulphate, and coating agent cellulose acetate were chosen as independent variables. The result of Plackett-Burman and Box-Behnken design and ANOVA studies revealed that osmotic agent and pore former had significant effect on the drug release up to 12 hr. The observed independent variables were found to be very close to predicted values of most satisfactory formulation which demonstrates the feasibility of the optimization procedure in successful development of porous osmotic pump tablets containing antibiotic drug/s by using sodium chloride, sodium lauryl sulphate, and cellulose acetate as key excipients.

Variable Structure Disturbance Rejection Control for Nonlinear Uncertain Systems with State and Control Delays via Optimal Sliding Mode Surface Approach

Directory of Open Access Journals (Sweden)

Jing Lei

2013-01-01

Full Text Available The paper considers the problem of variable structure control for nonlinear systems with uncertainty and time delays under persistent disturbance by using the optimal sliding mode surface approach. Through functional transformation, the original time-delay system is transformed into a delay-free one. The approximating sequence method is applied to solve the nonlinear optimal sliding mode surface problem which is reduced to a linear two-point boundary value problem of approximating sequences. The optimal sliding mode surface is obtained from the convergent solutions by solving a Riccati equation, a Sylvester equation, and the state and adjoint vector differential equations of approximating sequences. Then, the variable structure disturbance rejection control is presented by adopting an exponential trending law, where the state and control memory terms are designed to compensate the state and control delays, a feedforward control term is designed to reject the disturbance, and an adjoint compensator is designed to compensate the effects generated by the nonlinearity and the uncertainty. Furthermore, an observer is constructed to make the feedforward term physically realizable, and thus the dynamical observer-based dynamical variable structure disturbance rejection control law is produced. Finally, simulations are demonstrated to verify the effectiveness of the presented controller and the simplicity of the proposed approach.

Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm

Directory of Open Access Journals (Sweden)

V. Rajinikanth

2012-01-01

Full Text Available An enhanced bacteria foraging optimization (EBFO algorithm-based Proportional + integral + derivative (PID controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.

Exploring quantum control landscapes: Topology, features, and optimization scaling

International Nuclear Information System (INIS)

Moore, Katharine W.; Rabitz, Herschel

2011-01-01

Quantum optimal control experiments and simulations have successfully manipulated the dynamics of systems ranging from atoms to biomolecules. Surprisingly, these collective works indicate that the effort (i.e., the number of algorithmic iterations) required to find an optimal control field appears to be essentially invariant to the complexity of the system. The present work explores this matter in a series of systematic optimizations of the state-to-state transition probability on model quantum systems with the number of states N ranging from 5 through 100. The optimizations occur over a landscape defined by the transition probability as a function of the control field. Previous theoretical studies on the topology of quantum control landscapes established that they should be free of suboptimal traps under reasonable physical conditions. The simulations in this work include nearly 5000 individual optimization test cases, all of which confirm this prediction by fully achieving optimal population transfer of at least 99.9% on careful attention to numerical procedures to ensure that the controls are free of constraints. Collectively, the simulation results additionally show invariance of required search effort to system dimension N. This behavior is rationalized in terms of the structural features of the underlying control landscape. The very attractive observed scaling with system complexity may be understood by considering the distance traveled on the control landscape during a search and the magnitude of the control landscape slope. Exceptions to this favorable scaling behavior can arise when the initial control field fluence is too large or when the target final state recedes from the initial state as N increases.

Robust and optimal control a two-port framework approach

CERN Document Server

Tsai, Mi-Ching

2014-01-01

A Two-port Framework for Robust and Optimal Control introduces an alternative approach to robust and optimal controller synthesis procedures for linear, time-invariant systems, based on the two-port system widespread in electrical engineering. The novel use of the two-port system in this context allows straightforward engineering-oriented solution-finding procedures to be developed, requiring no mathematics beyond linear algebra. A chain-scattering description provides a unified framework for constructing the stabilizing controller set and for synthesizing H2 optimal and H∞ sub-optimal controllers. Simple yet illustrative examples explain each step. A Two-port Framework for Robust and Optimal Control  features: ·         a hands-on, tutorial-style presentation giving the reader the opportunity to repeat the designs presented and easily to modify them for their own programs; ·         an abundance of examples illustrating the most important steps in robust and optimal design; and ·   �...

Urban Traffic Signal System Control Structural Optimization Based on Network Analysis

Directory of Open Access Journals (Sweden)

Li Wang

2013-01-01

Full Text Available Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers’ experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively.

Control Parameters Optimization Based on Co-Simulation of a Mechatronic System for an UA-Based Two-Axis Inertially Stabilized Platform

Directory of Open Access Journals (Sweden)

Xiangyang Zhou

2015-08-01

Full Text Available This paper presents a method based on co-simulation of a mechatronic system to optimize the control parameters of a two-axis inertially stabilized platform system (ISP applied in an unmanned airship (UA, by which high control performance and reliability of the ISP system are achieved. First, a three-dimensional structural model of the ISP is built by using the three-dimensional parametric CAD software SOLIDWORKS®; then, to analyze the system’s kinematic and dynamic characteristics under operating conditions, dynamics modeling is conducted by using the multi-body dynamics software ADAMS™, thus the main dynamic parameters such as displacement, velocity, acceleration and reaction curve are obtained, respectively, through simulation analysis. Then, those dynamic parameters were input into the established MATLAB® SIMULINK® controller to simulate and test the performance of the control system. By these means, the ISP control parameters are optimized. To verify the methods, experiments were carried out by applying the optimized parameters to the control system of a two-axis ISP. The results show that the co-simulation by using virtual prototyping (VP is effective to obtain optimized ISP control parameters, eventually leading to high ISP control performance.

Advanced Process Control Application and Optimization in Industrial Facilities

Directory of Open Access Journals (Sweden)

Howes S.

2015-01-01

Full Text Available This paper describes application of the new method and tool for system identification and PID tuning/advanced process control (APC optimization using the new 3G (geometric, gradient, gravity optimization method. It helps to design and implement control schemes directly inside the distributed control system (DCS or programmable logic controller (PLC. Also, the algorithm helps to identify process dynamics in closed-loop mode, optimizes controller parameters, and helps to develop adaptive control and model-based control (MBC. Application of the new 3G algorithm for designing and implementing APC schemes is presented. Optimization of primary and advanced control schemes stabilizes the process and allows the plant to run closer to process, equipment and economic constraints. This increases production rates, minimizes operating costs and improves product quality.

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

Science.gov (United States)

Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

2015-05-01

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system

International Nuclear Information System (INIS)

Attaran, Seyed Mohammad; Yusof, Rubiyah; Selamat, Hazlina

2016-01-01

Highlights: • Decoupling of a heating, ventilation, and air conditioning system is presented. • RBF models were identified by Epsilon constraint method for temperature and humidity. • Control settings derived from optimization of the decoupled model. • Epsilon constraint-RBF based on PID controller was implemented to keep thermal comfort and minimize energy. • Enhancements of controller parameters of the HVAC system are desired. - Abstract: The energy efficiency of a heating, ventilating and air conditioning (HVAC) system optimized using a radial basis function neural network (RBFNN) combined with the epsilon constraint (EC) method is reported. The new method adopts the advanced algorithm of RBFNN for the HVAC system to estimate the residual errors, increase the control signal and reduce the error results. The objective of this study is to develop and simulate the EC-RBFNN for a self tuning PID controller for a decoupled bilinear HVAC system to control the temperature and relative humidity (RH) produced by the system. A case study indicates that the EC-RBFNN algorithm has a much better accuracy than optimization PID itself and PID-RBFNN, respectively.

Optimal control and quantum simulations in superconducting quantum devices

Energy Technology Data Exchange (ETDEWEB)

Egger, Daniel J.

2014-10-31

Quantum optimal control theory is the science of steering quantum systems. In this thesis we show how to overcome the obstacles in implementing optimal control for superconducting quantum bits, a promising candidate for the creation of a quantum computer. Building such a device will require the tools of optimal control. We develop pulse shapes to solve a frequency crowding problem and create controlled-Z gates. A methodology is developed for the optimisation towards a target non-unitary process. We show how to tune-up control pulses for a generic quantum system in an automated way using a combination of open- and closed-loop optimal control. This will help scaling of quantum technologies since algorithms can calibrate control pulses far more efficiently than humans. Additionally we show how circuit QED can be brought to the novel regime of multi-mode ultrastrong coupling using a left-handed transmission line coupled to a right-handed one. We then propose to use this system as an analogue quantum simulator for the Spin-Boson model to show how dissipation arises in quantum systems.

Optimal control of a qubit in an optical cavity

International Nuclear Information System (INIS)

Deffner, Sebastian

2014-01-01

We study quantum information processing by means of optimal control theory. To this end, we analyze the damped Jaynes–Cummings model, and derive optimal control protocols that minimize the heating or energy dispersion rates, and controls that drive the system at the quantum speed limit. Special emphasis is put on analyzing the subtleties of optimal control theory for our system. In particular, it is shown how two fundamentally different approaches to the quantum speed limit can be reconciled by carefully formulating the problem. (paper)

A novel optimal coordinated control strategy for the updated robot system for single port surgery.

Science.gov (United States)

Bai, Weibang; Cao, Qixin; Leng, Chuntao; Cao, Yang; Fujie, Masakatsu G; Pan, Tiewen

2017-09-01

Research into robotic systems for single port surgery (SPS) has become widespread around the world in recent years. A new robot arm system for SPS was developed, but its positioning platform and other hardware components were not efficient. Special features of the developed surgical robot system make good teleoperation with safety and efficiency difficult. A robot arm is combined and used as new positioning platform, and the remote center motion is realized by a new method using active motion control. A new mapping strategy based on kinematics computation and a novel optimal coordinated control strategy based on real-time approaching to a defined anthropopathic criterion configuration that is referred to the customary ease state of human arms and especially the configuration of boxers' habitual preparation posture are developed. The hardware components, control architecture, control system, and mapping strategy of the robotic system has been updated. A novel optimal coordinated control strategy is proposed and tested. The new robot system can be more dexterous, intelligent, convenient and safer for preoperative positioning and intraoperative adjustment. The mapping strategy can achieve good following and representation for the slave manipulator arms. And the proposed novel control strategy can enable them to complete tasks with higher maneuverability, lower possibility of self-interference and singularity free while teleoperating. Copyright © 2017 John Wiley & Sons, Ltd.

Defending against the Advanced Persistent Threat: An Optimal Control Approach

Directory of Open Access Journals (Sweden)

Pengdeng Li

2018-01-01

Full Text Available The new cyberattack pattern of advanced persistent threat (APT has posed a serious threat to modern society. This paper addresses the APT defense problem, that is, the problem of how to effectively defend against an APT campaign. Based on a novel APT attack-defense model, the effectiveness of an APT defense strategy is quantified. Thereby, the APT defense problem is modeled as an optimal control problem, in which an optimal control stands for a most effective APT defense strategy. The existence of an optimal control is proved, and an optimality system is derived. Consequently, an optimal control can be figured out by solving the optimality system. Some examples of the optimal control are given. Finally, the influence of some factors on the effectiveness of an optimal control is examined through computer experiments. These findings help organizations to work out policies of defending against APTs.

Development and Optimization of controlled drug release ...

African Journals Online (AJOL)

The aim of this study is to develop and optimize an osmotically controlled drug delivery system of diclofenac sodium. Osmotically controlled oral drug delivery systems utilize osmotic pressure for controlled delivery of active drugs. Drug delivery from these systems, to a large extent, is independent of the physiological factors ...

Application of numerical optimization techniques to control system design for nonlinear dynamic models of aircraft

Science.gov (United States)

Lan, C. Edward; Ge, Fuying

1989-01-01

Control system design for general nonlinear flight dynamic models is considered through numerical simulation. The design is accomplished through a numerical optimizer coupled with analysis of flight dynamic equations. The general flight dynamic equations are numerically integrated and dynamic characteristics are then identified from the dynamic response. The design variables are determined iteratively by the optimizer to optimize a prescribed objective function which is related to desired dynamic characteristics. Generality of the method allows nonlinear effects to aerodynamics and dynamic coupling to be considered in the design process. To demonstrate the method, nonlinear simulation models for an F-5A and an F-16 configurations are used to design dampers to satisfy specifications on flying qualities and control systems to prevent departure. The results indicate that the present method is simple in formulation and effective in satisfying the design objectives.

Optimal control of quantum gates and suppression of decoherence in a system of interacting two-level particles

International Nuclear Information System (INIS)

Grace, Matthew; Brif, Constantin; Rabitz, Herschel; Walmsley, Ian A; Kosut, Robert L; Lidar, Daniel A

2007-01-01

Methods of optimal control are applied to a model system of interacting two-level particles (e.g., spin-half atomic nuclei or electrons or two-level atoms) to produce high-fidelity quantum gates while simultaneously negating the detrimental effect of decoherence. One set of particles functions as the quantum information processor, whose evolution is controlled by a time-dependent external field. The other particles are not directly controlled and serve as an effective environment, coupling to which is the source of decoherence. The control objective is to generate target one- and two-qubit unitary gates in the presence of strong environmentally-induced decoherence and under physically motivated restrictions on the control field. The quantum-gate fidelity, expressed in terms of a novel state-independent distance measure, is maximized with respect to the control field using combined genetic and gradient algorithms. The resulting high-fidelity gates demonstrate the feasibility of precisely guiding the quantum evolution via optimal control, even when the system complexity is exacerbated by environmental coupling. It is found that the gate duration has an important effect on the control mechanism and resulting fidelity. An analysis of the sensitivity of the gate performance to random variations in the system parameters reveals a significant degree of robustness attained by the optimal control solutions

Developments in model-based optimization and control distributed control and industrial applications

CERN Document Server

Grancharova, Alexandra; Pereira, Fernando

2015-01-01

This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and desi...

Optimized dispatch of wind farms with power control capability for power system restoration

DEFF Research Database (Denmark)

Xie, Yunyun; Liu, Changsheng; Wu, Qiuwei

2017-01-01

As the power control technology of wind farms develops, the output power of wind farms can be constant, which makes it possible for wind farms to participate in power system restoration. However, due to the uncertainty of wind energy, the actual output power can’t reach a constant dispatch power...... in all time intervals, resulting in uncertain power sags which may induce the frequency of the system being restored to go outside the security limits. Therefore, it is necessary to optimize the dispatch of wind farms participating in power system restoration. Considering that the probability...... distribution function (PDF) of transient power sags is hard to obtain, a robust optimization model is proposed in this paper, which can maximize the output power of wind farms participating in power system restoration. Simulation results demonstrate that the security constraints of the restored system can...

Optimization of feed water control for auxiliary boiler

International Nuclear Information System (INIS)

Li Lingmao

2004-01-01

This paper described the feed water control system of the auxiliary boiler steam drum in Qinshan Phase III Nuclear Power Plant, analyzed the deficiency of the original configuration, and proposed the optimized configuration. The optimized feed water control system can ensure the stable and safe operation of the auxiliary boiler, and the normal operation of the users. (author)

Stability Constrained Efficiency Optimization for Droop Controlled DC-DC Conversion System

DEFF Research Database (Denmark)

Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.

2013-01-01

implementing tertiary regulation. Moreover, system dynamic is affected when shifting VRs. Therefore, the stability is considered in optimization by constraining the eigenvalues arising from dynamic state space model of the system. Genetic algorithm is used in searching for global efficiency optimum while....... As the efficiency of each converter changes with output power, virtual resistances (VRs) are set as decision variables for adjusting power sharing proportion among converters. It is noteworthy that apart from restoring the voltage deviation, secondary control plays an important role to stabilize dc bus voltage when...

Implications of the degree of controllability of controlled plants in the sense of LQR optimal control

Science.gov (United States)

Xia, Yaping; Yin, Minghui; Zou, Yun

2018-01-01

In this paper, the relationship between the degree of controllability (DOC) of controlled plants and the corresponding quadratic optimal performance index in LQR control is investigated for the electro-hydraulic synchronising servo control systems and wind turbine systems, respectively. It is shown that for these two types of systems, the higher the DOC of a controlled plant is, the better the quadratic optimal performance index is. It implies that in some LQR controller designs, the measure of the DOC of a controlled plant can be used as an index for the optimisation of adjustable plant parameters, by which the plant can be controlled more effectively.

Optimization-Based Approaches to Control of Probabilistic Boolean Networks

Directory of Open Access Journals (Sweden)

Koichi Kobayashi

2017-02-01

Full Text Available Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs, which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.

Linear quadratic optimization for positive LTI system

Science.gov (United States)

Muhafzan, Yenti, Syafrida Wirma; Zulakmal

2017-05-01

Nowaday the linear quadratic optimization subject to positive linear time invariant (LTI) system constitute an interesting study considering it can become a mathematical model of variety of real problem whose variables have to nonnegative and trajectories generated by these variables must be nonnegative. In this paper we propose a method to generate an optimal control of linear quadratic optimization subject to positive linear time invariant (LTI) system. A sufficient condition that guarantee the existence of such optimal control is discussed.

Optimal and Autonomous Control Using Reinforcement Learning: A Survey.

Science.gov (United States)

Kiumarsi, Bahare; Vamvoudakis, Kyriakos G; Modares, Hamidreza; Lewis, Frank L

2018-06-01

This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal and control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.

Optimal control of building storage systems using both ice storage and thermal mass – Part I: Simulation environment

International Nuclear Information System (INIS)

Hajiah, Ali; Krarti, Moncef

2012-01-01

Highlights: ► A simulation environment is described to account for both passive and active thermal energy storage (TES) systems. ► Laboratory testing results have been used to validate the predictions from the simulation environment. ► Optimal control strategies for TES systems have been developed as part of the simulation environment. - Abstract: This paper presents a simulation environment that can evaluate the benefits of using simultaneously building thermal capacitance and ice storage system to reduce total operating costs including energy and demand charges while maintaining adequate occupant comfort conditions within commercial buildings. The building thermal storage is controlled through pre-cooling strategies by setting space indoor air temperatures. The ice storage system is controlled by charging the ice tank and operating the chiller during low electrical charge periods and melting the ice during on-peak periods. Optimal controls for both building thermal storage and ice storage are developed to minimize energy charges, demand charges, or combined energy and demand charges. The results obtained from the simulation environment are validated using laboratory testing for an optimal controller.

Preventive Security-Constrained Optimal Power Flow Considering UPFC Control Modes

Directory of Open Access Journals (Sweden)

Xi Wu

2017-08-01

Full Text Available The successful application of the unified power flow controller (UPFC provides a new control method for the secure and economic operation of power system. In order to make the full use of UPFC and improve the economic efficiency and static security of a power system, a preventive security-constrained power flow optimization method considering UPFC control modes is proposed in this paper. Firstly, an iterative method considering UPFC control modes is deduced for power flow calculation. Taking into account the influence of different UPFC control modes on the distribution of power flow after N-1 contingency, the optimization model is then constructed by setting a minimal system operation cost and a maximum static security margin as the objective. Based on this model, the particle swarm optimization (PSO algorithm is utilized to optimize power system operating parameters and UPFC control modes simultaneously. Finally, a standard IEEE 30-bus system is utilized to demonstrate that the proposed method fully exploits the potential of static control of UPFC and significantly increases the economic efficiency and static security of the power system.

Real-Time Optimization for use in a Control Allocation System to Recover from Pilot Induced Oscillations

Science.gov (United States)

Leonard, Michael W.

2013-01-01

Integration of the Control Allocation technique to recover from Pilot Induced Oscillations (CAPIO) System into the control system of a Short Takeoff and Landing Mobility Concept Vehicle simulation presents a challenge because the CAPIO formulation requires that constrained optimization problems be solved at the controller operating frequency. We present a solution that utilizes a modified version of the well-known L-BFGS-B solver. Despite the iterative nature of the solver, the method is seen to converge in real time with sufficient reliability to support three weeks of piloted runs at the NASA Ames Vertical Motion Simulator (VMS) facility. The results of the optimization are seen to be excellent in the vast majority of real-time frames. Deficiencies in the quality of the results in some frames are shown to be improvable with simple termination criteria adjustments, though more real-time optimization iterations would be required.

Optimization and control of a continuous polymerization reactor

Directory of Open Access Journals (Sweden)

L. A. Alvarez

2012-12-01

Full Text Available This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO, the Model Predictive Control (MPC and a Target Calculation (TC that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.

A problem of optimal control and observation for distributed homogeneous multi-agent system

Science.gov (United States)

Kruglikov, Sergey V.

2017-12-01

The paper considers the implementation of a algorithm for controlling a distributed complex of several mobile multi-robots. The concept of a unified information space of the controlling system is applied. The presented information and mathematical models of participants and obstacles, as real agents, and goals and scenarios, as virtual agents, create the base forming the algorithmic and software background for computer decision support system. The controlling scheme assumes the indirect management of the robotic team on the basis of optimal control and observation problem predicting intellectual behavior in a dynamic, hostile environment. A basic content problem is a compound cargo transportation by a group of participants in the case of a distributed control scheme in the terrain with multiple obstacles.

Optimal estimation and control in nuclear power plants

International Nuclear Information System (INIS)

Purviance, J.E.; Tylee, J.L.

1982-08-01

Optimal estimation and control theories offer the potential for more precise control and diagnosis of nuclear power plants. The important element of these theories is that a mathematical plant model is used in conjunction with the actual plant data to optimize some performance criteria. These criteria involve important plant variables and incorporate a sense of the desired plant performance. Several applications of optimal estimation and control to nuclear systems are discussed

Adaptive near-optimal neuro controller for continuous-time nonaffine nonlinear systems with constrained input.

Science.gov (United States)

Esfandiari, Kasra; Abdollahi, Farzaneh; Talebi, Heidar Ali

2017-09-01

In this paper, an identifier-critic structure is introduced to find an online near-optimal controller for continuous-time nonaffine nonlinear systems having saturated control signal. By employing two Neural Networks (NNs), the solution of Hamilton-Jacobi-Bellman (HJB) equation associated with the cost function is derived without requiring a priori knowledge about system dynamics. Weights of the identifier and critic NNs are tuned online and simultaneously such that unknown terms are approximated accurately and the control signal is kept between the saturation bounds. The convergence of NNs' weights, identification error, and system states is guaranteed using Lyapunov's direct method. Finally, simulation results are performed on two nonlinear systems to confirm the effectiveness of the proposed control strategy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Numerical aspects of optimal control of penicillin production

Czech Academy of Sciences Publication Activity Database

Pčolka, M.; Čelikovský, Sergej

2014-01-01

Roč. 37, č. 1 (2014), s. 71-81 ISSN 1615-7591 R&D Projects: GA ČR(CZ) GA13-20433S Institutional support: RVO:67985556 Keywords : Optimal control * Nonlinear systems * Fermentation process * Gradient method optimization * Antibiotics production Subject RIV: BC - Control Systems Theory Impact factor: 1.997, year: 2014 http://library.utia.cas.cz/separaty/2014/TR/celikovsky-0424718.pdf

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.

Feedback System Control Optimized Electrospinning for Fabrication of an Excellent Superhydrophobic Surface.

Science.gov (United States)

Yang, Jian; Liu, Chuangui; Wang, Boqian; Ding, Xianting

2017-10-13

Superhydrophobic surface, as a promising micro/nano material, has tremendous applications in biological and artificial investigations. The electrohydrodynamics (EHD) technique is a versatile and effective method for fabricating micro- to nanoscale fibers and particles from a variety of materials. A combination of critical parameters, such as mass fraction, ratio of N, N-Dimethylformamide (DMF) to Tetrahydrofuran (THF), inner diameter of needle, feed rate, receiving distance, applied voltage as well as temperature, during electrospinning process, to determine the morphology of the electrospun membranes, which in turn determines the superhydrophobic property of the membrane. In this study, we applied a recently developed feedback system control (FSC) scheme for rapid identification of the optimal combination of these controllable parameters to fabricate superhydrophobic surface by one-step electrospinning method without any further modification. Within five rounds of experiments by testing totally forty-six data points, FSC scheme successfully identified an optimal parameter combination that generated electrospun membranes with a static water contact angle of 160 degrees or larger. Scanning electron microscope (SEM) imaging indicates that the FSC optimized surface attains unique morphology. The optimized setup introduced here therefore serves as a one-step, straightforward, and economic approach to fabricate superhydrophobic surface with electrospinning approach.