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Data-based stable value iteration optimal control for unknown discrete-time systems with time delays

NEUROCOMPUTING 2020年第0期382卷 96-105页

作者： Ren, He Zhang, Huaguang Su, Hanguang Mu, Yunfei Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Liaoning Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China

In this study, a novel data-based stable value iteration (SVI) optimal control scheme is presented in order to tackle with the linear discrete-time (DT) system with multiple time delays. Due to the difficulty in acquiring the knowledge of system dynamics, the optimal control strategies could be computed with only history input and output database by employing an estimator on the basis of adaptive dynamic programming (ADP) technology. By analyzing features of time delay systems, a homologous equivalent notion of delay-free systems is proposed so that the optimal control policy against systems with multiple time delays could be designed indirectly. Moreover, four equivalent conditions are deduced between the two related systems. The convergence of the rising SVI algorithm with discount factor is discussed according to optimal control principles. The proposed SVI algorithm is proved to converge to optimal values with proper discount factor. In the end, two numerical examples are given, and simulation results illustrate that the presented data-based SVI method is effective. (C) 2019 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Data-based control Multiple time delays Stable value iteration Optimal control

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Event-triggered ADP control of a class of non-affine continuous-time nonlinear systems using output information

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NEUROCOMPUTING 2020年第0期378卷 304-314页

作者： Yang, Yang Xu, Chuang Yue, Dong Zhong, Xiangnan Si, Xuefeng Tan, Jie Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Univ North Texas Dept Elect Engn Denton TX 76203 USA

An event-triggered adaptive dynamic programming (ADP) approach is proposed for a class of non-affine continuous-time nonlinear systems with unknown internal states. A neural networks (NNs)-based observer is designed to reconstruct internal states of the system using output information, and then, by the estimation signals, an output feedback ADP control approach is developed in an event-triggered manner. The proposed approach samples the states and updates the control signal only when the triggered condition is violated, and critic NNs are designed to approximate the performance index. Compared with the traditional ADP one under a fixed sampling mechanism, the event-triggered control approach reduces the computation resource and transmission load in the learning process. The stability analysis of the closed-loop system is provided based on the Lyapunov's theorem. Two simulation results also verify the theoretical claims. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Event-triggered approach Observer adaptive dynamic programming Non-affine system Neural network

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adaptive periodic event-triggered control for missile-target interception system with finite-horizon convergence

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第10期42卷 1808-1822页

作者： Duan, Dandan Liu, Chunsheng Sun, Jingliang Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Jiangsu Peoples R China

In this paper, the optimal control problem for finite-time missile-target interception systems is posed in a finite-horizon two-player zero-sum (ZS) differential game framework using a periodic event-triggered (PET) scheme. To solve the optimal control problem, a time-varying Hamilton-Jacobi-Issac (HJI) equation and a time-dependent cost function are constructed to deal with finite-horizon constraints, and an event-based periodic adaptive dynamic programming (ADP) algorithm is employed to find the Nash equilibrium solution for the designed HJI equation. Comparing with the traditional continuous event-triggered (ET) scheme, the proposed PET scheme only verifies the event-triggered conditions at periodic sampling instants, which reduces resource consumption in monitoring and excludes the Zeno behavior. A single critic neural network (CNN) is used to implement the proposed event-based optimal control algorithm, which reduces approximate errors bust also simplifies structures. Further, an additional error term is added in the designed weight updating law to such that the terminal constraint is also minimized over time. By resorting to Lyapunov function approach, some sufficient conditions are derived to achieve the uniformly ultimately bounded (UUB) of the ET closed-loop system and the estimation weight error of CNN. Finally, a missile-target interception system is introduced to illustrate the efficiency of the presented methods.

关键词： Missile-target interception two-player ZS differential game finite-horizon adaptive dynamic programming periodic event-triggered strategy

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Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第15期42卷 2833-2856页

作者： Elkenawy, Ahmed El-Nagar, Ahmad M. El-Bardini, Mohammad El-Rabaie, Nabila M. Menoufia Univ Dept Ind Elect & Control Engn Menoufia Egypt

This paper proposes an observer-based adaptive control for unknown nonlinear systems using an adaptive dynamic programming (ADP) algorithm. First, a diagonal recurrent neural network (DRNN) observer is proposed to estimate the unknown dynamics of the nonlinear system states. The proposed neural network offers a simpler structure with deeper memory and guarantees the faster convergence. Second, a neural controller is constructed via ADP method using the observed states to get the optimal control. The optimal control law is determined based on the new structure of the critic network, which is performed using the DRNN. The learning algorithm for the proposed DRNN observer-based adaptive control is developed based on the Lyapunov stability theory. Simulation results and hardware-in-the-loop results indicate the robustness of the proposed ADP to respond the system uncertainties and external disturbances compared with other existing schemes.

关键词： Diagonal recurrent neural network state observer optimal control nonlinear systems adaptive dynamic programming

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Off-policy integral reinforcement learning algorithm in dealing with nonzero sum game for nonlinear distributed parameter systems

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第15期42卷 2919-2928页

作者： Ren, He Dai, Jing Zhang, Huaguang Zhang, Kun Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Liaoning Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Benefitting from the technology of integral reinforcement learning, the nonzero sum (NZS) game for distributed parameter systems is effectively solved in this paper when the information of system dynamics are unavailable. The Karhunen-Loeve decomposition (KLD) is employed to convert the partial differential equation (PDE) systems into high-order ordinary differential equation (ODE) systems. Moreover, the off-policy IRL technology is introduced to design the optimal strategies for the NZS game. To confirm that the presented algorithm will converge to the optimal value functions, the traditional adaptive dynamic programming (ADP) method is first discussed. Then, the equivalence between the traditional ADP method and the presented off-policy method is proved. For implementing the presented off-policy IRL method, actor and critic neural networks are utilized to approach the value functions and control strategies in the iteration process, individually. Finally, a numerical simulation is shown to illustrate the effectiveness of the proposal off-policy algorithm.

关键词： Integral reinforcement learning off-policy algorithm nonzero sum game distributed parameter systems adaptive dynamic programming

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Fuzzy Observer-Based dynamic Surface Control for Input-Saturated Nonlinear Systems and its Application to Missile Guidance

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IEEE ACCESS 2020年 8卷 121285-121298页

作者： Zhang, Wenguang Yi, Wenjun Nanjing Univ Sci & Technol Natl Key Lab Transient Phys Nanjing 210094 Peoples R China

In this paper, a fuzzy observer-based tracking control scheme is proposed for a class of strict-feedback uncertain nonlinear system with input saturation. To facilitate the fuzzy logic system (FLS) design, the original uncertain nonlinear system is transformed into an output feedback system firstly. Then, an adaptive state observer is constructed. In order to efficiently tackle the input saturation, an auxiliary system (AS) is introduced. Using the estimated states and AS, the dynamic surface control (DSC) strategy is developed based on backstepping method. Under the DSC scheme, the tracking control problem is reduced to optimal regulation problem of an affine system. Subsequently, an adaptive dynamic programming (ADP) based optimal control scheme is proposed. The stability of the overall system is guaranteed by Lyapunov theory. Finally, guidance simulation is made to confirm the effectiveness of the developed control method.

关键词： adaptive dynamic programming dynamic surface control adaptive fuzzy control input saturation missile guidance systems

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Finite-horizon neuro-optimal tracking control for a class of discrete-time nonlinear systems using adaptive dynamic programming approach

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NEUROCOMPUTING 2012年第1期78卷 14-22页

作者： Wang, Ding Liu, Derong Wei, Qinglai Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this paper, a finite-horizon neuro-optimal tracking control strategy for a class of discrete-time nonlinear systems is proposed. Through system transformation, the optimal tracking problem is converted into designing a finite-horizon optimal regulator for the tracking error dynamics. Then, with convergence analysis in terms of cost function and control law, the iterative adaptive dynamic programming (ADP) algorithm via heuristic dynamic programming (HDP) technique is introduced to obtain the finite-horizon optimal tracking controller which makes the cost function close to its optimal value within an epsilon-error bound. Three neural networks are used as parametric structures to implement the algorithm, which aims at approximating the cost function, the control law, and the error dynamics, respectively. Two simulation examples are included to complement the theoretical discussions. (C) 2011 Elsevier B.V. All rights reserved.

关键词： adaptive critic designs adaptive dynamic programming Approximate dynamic programming Finite-horizon optimal tracking control Learning control Neural networks Reinforcement learning

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adaptive optimal dynamic surface control of strict-feedback nonlinear systems with output constraints

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2020年第5期30卷 2059-2078页

作者： Zhang, Tianping Xu, Haoxiang Yangzhou Univ Dept Automat Coll Informat Engn Yangzhou 225127 Jiangsu Peoples R China Yangzhou Univ Coll Math Sci Yangzhou Jiangsu Peoples R China

In this paper, an adaptive optimal control strategy is proposed for a class of strict-feedback nonlinear systems with output constraints by using dynamic surface control. The controller design procedure is divided into two parts. One is the design of feedforward controller and the other is the design of optimal controller. To guarantee the satisfaction of output constraints in feedforward controller, nonlinear mapping is utilized to transform the constrained system into an unconstrained system. Neural-network based adaptive dynamic programming algorithm is employed to approximate the optimal cost function and the optimal control law. By theoretical analysis, all the signals in the closed-loop system are proved to be semi-globally uniformly ultimately bounded and the output constraints are not violated. A numerical example illustrates the effectiveness of the proposed scheme.

关键词： adaptive dynamic programming dynamic surface control neural network nonlinear mapping optimal control output constraints

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Integral Reinforcement Learning-Based Optimal Control for Nonzero-Sum Games of Multi-Player Input-Constrained Nonlinear Systems 21

Integral Reinforcement Learning-Based Optimal Control for No...

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Proceedings of the 7th International Conference on Robotics and Artificial Intelligence

作者： Qiuye Wu Bo Zhao Derong Liu School of Automation Guangdong University of Technology China School of Systems Science Beijing Normal University China

ISBN: (纸本)9781450385855

This paper investigates an integral reinforcement learning (IRL)-based optimal control scheme to solve nonzero-sum games of multi-player input-constrained nonlinear systems with unknown drift dynamics. The IRL method is introduced to obviate the identification procedure of the unknown drift dynamics. In order to achieve Nash equilibrium for each player, the simplified optimal control policy for each player is obtained by solving the coupled Hamilton-Jacobi equation with the critic neural network only. Thus, the computational resource is saved and the control structure is easy to realize. The closed-loop system is guaranteed to be uniformly ultimately bounded based on the Lyapunov stability analysis. Simulation results illustrate the effectiveness of the developed control scheme.

关键词： input constraints nonzero-sum games neural networks integral reinforcement learning adaptive dynamic programming

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A three-network architecture for on-line learning and optimization based on adaptive dynamic programming

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NEUROCOMPUTING 2012年第1期78卷 3-13页

作者： He, Haibo Ni, Zhen Fu, Jian Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China

In this paper, we propose a novel adaptive dynamic programming (ADP) architecture with three networks, an action network, a critic network, and a reference network, to develop internal goal-representation for online learning and optimization. Unlike the traditional ADP design normally with an action network and a critic network, our approach integrates the third network, a reference network, into the actor-critic design framework to automatically and adaptively build an internal reinforcement signal to facilitate learning and optimization overtime to accomplish goals. We present the detailed design architecture and its associated learning algorithm to explain how effective learning and optimization can be achieved in this new ADP architecture. Furthermore, we test the performance of our architecture both on the cart-pole balancing task and the triple-link inverted pendulum balancing task, which are the popular benchmarks in the community to demonstrate its learning and control performance over time. (C) 2011 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Online learning and control Actor-critic design Three-network architecture Multi-state optimization Goal representation Reinforcement learning

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