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2016年第七届机电一体化与制造业国际学术会议

作者： Yi Liu Yun Zhang Sizhe Chen School of Automation Guangdong University of Technology School of Information Science and Technology Zhongkai University of Agriculture and Engineering

Home energy optimization(HEO) is an infinite horizon optimization problem,only adaptive dynamic programming(ADP) can solve infinite horizon optimization *** due to the existence of time-varying parameters,ADP can not converge to the optimal and can not be applied directly to the optimal control of home *** to the periodic characteristics of the time-varying parameters,a stochastic optimal control method is proposed in the paper based on *** influence of time-varying parameters in the iteration of ADP is eliminated by the proposed *** results show the performance of the proposed method.

关键词： adaptive dynamic programming Stochastic Control Home Energy Optimization Time-varying Parameter Smart Grid

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A New Discrete-Time Iterative adaptive dynamic programming Algorithm Based on Q-Learning 12th

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12th International Symposium on Neural Networks (ISNN)

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9783319253930;9783319253923

In this paper, a novel Q-learning based policy iteration adaptive dynamic programming (ADP) algorithm is developed to solve the optimal control problems for discrete-time nonlinear systems. The idea is to use a policy iteration ADP technique to construct the iterative control law which stabilizes the system and simultaneously minimizes the iterative Q function. Convergence property is analyzed to show that the iterative Q function is monotonically non-increasing and converges to the solution of the optimality equation. Finally, simulation results are presented to show the performance of the developed algorithm.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming Q-learning policy iteration neural networks nonlinear systems optimal control

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Optimal Cooperative Control for Residential Multi-Battery Systems Using Iterative adaptive dynamic programming 27

Optimal Cooperative Control for Residential Multi-Battery Sy...

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27th Chinese Control and Decision Conference (CCDC)

作者： Wei, Qinglai Shi, Guang Liu, Yu Guan, Qiang Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9781479970162

In this paper, an iterative adaptive dynamic programming (ADP) algorithm is developed to solve the optimal cooperative control problems for residential multi-battery systems. To avoid solving high-dimensional optimal control problems, we first constrain all the batteries at their worst performance, which transforms the multi-input optimal control problem into a single-input one. Based on the worst-performance optimal control law, the optimal cooperative control law for the residential multi-battery systems is obtained, where in each iteration, only a single-input optimization problem is implemented. Finally, numerical results are given to illustrate the performance of the developed algorithm.

关键词： adaptive critic designs adaptive dynamic programming Approximate dynamic programming Smart grid Smart home Optimal control

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Robust Tracking Control of Uncertain Nonlinear Systems Using adaptive dynamic programming 1

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22nd International Conference on Neural Information Processing (ICONIP)

作者： Yang, Xiong Liu, Derong Wei, Qinglai Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (数字)9783319265551

ISBN: (纸本)9783319265551;9783319265544

In this paper, we develop an adaptive dynamic programming-based robust tracking control for a class of continuous-time matched uncertain nonlinear systems. By selecting a discounted value function for the nominal augmented error system, we transform the robust tracking control problem into an optimal control problem. The control matrix is not required to be invertible by using the present method. Meanwhile, we employ a single critic neural network (NN) to approximate the solution of the Hamilton-Jacobi-Bellman equation. Based on the developed critic NN, we derive optimal tracking control without using policy iteration. Moreover, we prove that all signals in the closed-loop system are uniformly ultimately bounded via Lyapunov's direct method. Finally, we provide an example to show the effectiveness of the present approach.

关键词： adaptive dynamic programming Robust control Tracking control Uncertain nonlinear system

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Robust adaptive dynamic programming for a zero-sum differential game 34

Robust adaptive dynamic programming for a zero-sum different...

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34th Chinese Control Conference (CCC)

作者： Yuan, Binbin Lu, Pingli Liu, Xiangdong Bian, Tao Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China NYU Polytech Sch Engn Dept Elect & Comp Engn Brooklyn NY 11201 USA

ISBN: (纸本)9789881563897

This paper presents a novel model-free online algorithm to solve the linear continuous-time three-player zero-sum differential game problem in the presence of dynamic uncertainty. Based on robust adaptive dynamic programming (RADP) method, the saddle point control policy and disturbance policy are iteratively approximated for the game problem with unmatched uncertainties. The convergence of the proposed RADP method and the global asymptotic stability (GAS) of the closed-loop system are also analyzed. An application to a power system is adopted to illustrate the effectiveness of the derived algorithm.

关键词： adaptive dynamic programming zero-sum games H infinity control game algebraic Riccati equation

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adaptive dynamic programming for Optimal Tracking Control of Unknown Nonlinear Systems With Application to Coal Gasification

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2014年第4期11卷 1020-1036页

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, we establish a new data-based iterative optimal learning control scheme for discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) approach and apply the developed control scheme to solve a coal gasification optimal tracking control problem. According to the system data, neural networks (NNs) are used to construct the dynamics of coal gasification process, coal quality and reference control, respectively, where the mathematical model of the system is unnecessary. The approximation errors from neural network construction of the disturbance and the controls are both considered. Via system transformation, the optimal tracking control problem with approximation errors and disturbances is effectively transformed into a two-person zero-sum optimal control problem. A new iterative ADP algorithm is then developed to obtain the optimal control laws for the transformed system. Convergence property is developed to guarantee that the performance index function converges to a finite neighborhood of the optimal performance index function, and the convergence criterion is also obtained. Finally, numerical results are given to illustrate the performance of the present method.

关键词： adaptive dynamic programming coal gasification data-based control finite approximation errors neural networks optimal tracking control

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adaptive dynamic programming as a theory of sensorimotor control

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BIOLOGICAL CYBERNETICS 2014年第4期108卷 459-473页

作者： Jiang, Yu Jiang, Zhong-Ping NYU Polytech Sch Engn Dept Elect & Comp Engn Control & Networks LabMetrotech Ctr 5 Brooklyn NY 11201 USA

Many characteristics of sensorimotor control can be explained by models based on optimization and optimal control theories. However, most of the previous models assume that the central nervous system has access to the precise knowledge of the sensorimotor system and its interacting environment. This viewpoint is difficult to be justified theoretically and has not been convincingly validated by experiments. To address this problem, this paper presents a new computational mechanism for sensorimotor control from a perspective of adaptive dynamic programming (ADP), which shares some features of reinforcement learning. The ADP-based model for sensorimotor control suggests that a command signal for the human movement is derived directly from the real-time sensory data, without the need to identify the system dynamics. An iterative learning scheme based on the proposed ADP theory is developed, along with rigorous convergence analysis. Interestingly, the computational model as advocated here is able to reproduce the motor learning behavior observed in experiments where a divergent force field or velocity-dependent force field was present. In addition, this modeling strategy provides a clear way to perform stability analysis of the overall system. Hence, we conjecture that human sensorimotor systems use an ADP-type mechanism to control movements and to achieve successful adaptation to uncertainties present in the environment.

关键词： Optimal control Motor learning Endpoint stiffness adaptive dynamic programming

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adaptive dynamic programming and optimal control of nonlinear nonaffine systems

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AUTOMATICA 2014年第10期50卷 2624-2632页

作者： Bian, Tao Jiang, Yu Jiang, Zhong-Ping NYU Polytech Sch Engn Dept Elect & Comp Engn Control & Networks Lab Brooklyn NY 11201 USA

In this paper, a novel optimal control design scheme is proposed for continuous-time nonaffine nonlinear dynamic systems with unknown dynamics by adaptive dynamic programming (ADP). The proposed methodology iteratively updates the control policy online by using the state and input information without identifying the system dynamics. An ADP algorithm is developed, and can be applied to a general class of nonlinear control design problems. The convergence analysis for the designed control scheme is presented, along with rigorous stability analysis for the closed-loop system. The effectiveness of this new algorithm is illustrated by two simulation examples. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： adaptive optimal control adaptive dynamic programming Nonaffine systems

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adaptive dynamic programming-based optimal tracking control for nonlinear systems using general value iteration

Adaptive dynamic programming-based optimal tracking control ...

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IEEE Symposium on adaptive dynamic programming and Reinforcement Learning (ADPRL)

作者： Lin, Xiaofeng Ding, Qiang Kong, Weikai Song, Chunning Huang, Qingbao Guangxi Univ Sch Elect Engn Nanning Peoples R China

ISBN: (纸本)9781479945528

For the optimal tracking control problem of affine nonlinear systems, a general value iteration algorithm based on adaptive dynamic programming is proposed in this paper. By system transformation, the optimal tracking problem is converted into the optimal regulating problem for the tracking error dynamics. Then, general value iteration algorithm is developed to obtain the optimal control with convergence analysis. Considering the advantages of echo state network, we use three echo state networks with levenberg-Marquardt (LM) adjusting algorithm to approximate the system, the cost function and the control law. A simulation example is given to demonstrate the effectiveness of the presented scheme.

关键词： adaptive dynamic programming value iteration tracking control echo state network

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A Novel Iterative θ-adaptive dynamic programming for Discrete-Time Nonlinear Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2014年第4期11卷 1176-1190页

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper is concerned with a new iterative theta-adaptive dynamic programming (ADP) technique to solve optimal control problems of infinite horizon discrete-time nonlinear systems. The idea is to use an iterative ADP algorithm to obtain the iterative control law which optimizes the iterative performance index function. In the present iterative theta-ADP algorithm, the condition of initial admissible control in policy iteration algorithm is avoided. It is proved that all the iterative controls obtained in the iterative theta-ADP algorithm can stabilize the nonlinear system which means that the iterative theta-ADP algorithm is feasible for implementations both online and offline. Convergence analysis of the performance index function is presented to guarantee that the iterative performance index function will converge to the optimum monotonically. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative theta-ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the established method.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming neural networks neuro-dynamic programming nonlinear systems optimal control policy iteration reinforcement learning value iteration

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