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Improved value iteration for neural-network-based stochastic optimal control design

NEURAL NETWORKS 2020年第0期124卷 280-295页

作者： Liang, Mingming Wang, Ding Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm is presented, which is called an improved value iteration ADP algorithm, to obtain the optimal policy for discrete stochastic processes. In the improved value iteration ADP algorithm, for the first time we propose a new criteria to verify whether the obtained policy is stable or not for stochastic processes. By analyzing the convergence properties of the proposed algorithm, it is shown that the iterative value functions can converge to the optimum. In addition, our algorithm allows the initial value function to be an arbitrary positive semi-definite function. Finally, two simulation examples are presented to validate the effectiveness of the developed method. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： adaptive critic designs adaptive dynamic programming Neural networks Optimal control Stochastic processes Value iteration

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Online policy iteration ADP-based attitude-tracking control for hypersonic vehicles

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AEROSPACE SCIENCE AND TECHNOLOGY 2020年 106卷 106233-106233页

作者： Han, Xiao Zheng, Zongzhun Liu, Lei Wang, Bo Cheng, Zhongtao Fan, Huijin Wang, Yongji Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Beijing Aerosp Automat Control Inst Beijing 100854 Peoples R China

An online adaptive dynamic programming (ADP) attitude-tracking controller based on policy iteration is proposed, aiming to approach the optimal control of hypersonic vehicles (HVs). The Bellman equation, known as the principal recursive dynamic programming formula, is provided to obtain the controller. In particular, the control action is generated by the ADP controller to track the attitude trajectory. In order to approach optimal control in the uncertain nonlinear HVs system, we use policy iteration to approximate the Bellman equation and build an actor-predictor-critic framework, in which the action network, state estimator and critic network are adopted to implement the policy iteration. Meanwhile, an offline learning method is provided to approach the initial value of iterative computations and improve the efficiency of online learning. The comparative simulations demonstrate the good performance of PIADP with aerodynamic parameter perturbations and random disturbances. (C) 2020 Elsevier Masson SAS. All rights reserved.

关键词： adaptive dynamic programming Policy iteration Hypersonic vehicles Neural network

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Event-triggered constrained control with DHP implementation for nonaffine discrete-time systems

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INFORMATION SCIENCES 2020年第0期519卷 110-123页

作者： Ha, Mingming Wang, Ding Liu, Derong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

This paper proposes an event-based near-optimal control algorithm for nonaffine discrete-time systems with constrained inputs. The method is derived from the dual heuristic dynamic programming (DHP) technique. The challenge caused by saturating actuators is overcome by using a nonquadratic performance index. Then, the event-based control technique is used to decrease the amount of computation. Meanwhile, the stability analysis is provided. It illustrates that the proposed event-based method can asymptotically stabilize the nonaffine systems by using the Lyapunov method. Furthermore, the stability conditions and the design process of the event-based controller are established. The event-based DHP algorithm is implemented by constructing three neural networks, namely, the model network, the critic network, and the action network. Finally, simulation studies are conducted to demonstrate the applicability and the performance of the proposed method. (C) 2020 Elsevier Inc. All rights reserved.

关键词： adaptive dynamic programming Dual heuristic dynamic programming Event-based technique Nonaffine systems Saturating actuators Stability analysis

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Robust ADP-based solution of a class of nonlinear multi-agent systems with input saturation and collision avoidance constraints

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ISA TRANSACTIONS 2020年 107卷 52-62页

作者： Khankalantary, Saeed Izadi, Iman Sheikholeslam, Farid Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

In this paper, robust tracking control for a class of nonlinear systems with external disturbance in the presence of input saturation and collision avoidance constraints is investigated. Both of these constraints are considered in a non-quadratic cost function. To obtain optimal control laws, a technique is proposed based on adaptive dynamic programming (ADP) assuming the worst case of disturbance, and without requiring any knowledge of the system dynamics. The proposed algorithm uses neural networks to estimate the optimal cost functions, control policies, and the worst case of disturbance. The Lyapunov theory is used to analyze the convergence of the estimations. Simulation results show the effectiveness of the developed ADP method. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Multi-agent systems adaptive dynamic programming Robust control Optimal control Input saturation Collision avoidance

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Disturbance observer-based optimal flight control of near space vehicle with external disturbance

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第2期42卷 272-284页

作者： Xia, Rongsheng Wu, Qingxian Shao, Shuyi Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211100 Jiangsu Peoples R China

This paper presents a disturbance observer-based robust optimal flight control strategy for near space vehicle (NSV) attitude system with external time-varying disturbance generated by an exogenous system. For the purpose of eliminating the effect of the disturbance, nonlinear disturbance observer (NDO) technique is used and the disturbance estimation error is guaranteed to be globally exponential convergence. Then, based on the disturbance estimation result and desired trajectory signal, a steady state control input is presented and the optimal tracking problem of original system with external disturbance can be converted into the optimal regulation problem of a nominal error system. Furthermore, a single network-based adaptive dynamic programming (ADP) method is applied to obtain the corresponding optimal feedback control law. Finally, all the signals in closed-loop system are proved to be uniformly ultimately bounded (UUB) and the tracking error can converge to a sufficiently small bound. Simulation tests about NSV attitude system are given to verify the effectiveness of proposed robust optimal flight control scheme.

关键词： Near space vehicles optimal attitude control nonlinear disturbance observer adaptive neural network adaptive dynamic programming

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A new robust output tracking control for discrete-time switched constrained-input systems with uncertainty via a critic-only iteration learning method

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NEUROCOMPUTING 2020年 396卷 162-171页

作者： Zhang, Kun Zhang, Huaguang Liang, Yuling Wen, Yinlei Northeastern Univ Coll Informat Sci & Engn Box 134 Shenyang 110819 Peoples R China Northeastern Univ Natl Educ Minist Key Lab Integrated Automat Proc Ind Shenyang 110004 Peoples R China

In this paper, the control objective is driving the output of a discrete-time switched constrained system with uncertainty to track a desired output of reference by an optimal manner. A new augmented switched system with discounted cost function is constructed based on the switched and reference dynamics, which converts the complex tracking problem to a stabilizing robust control optimization problem. Combining the two stage optimization and iteration learning technique, the overall optimal hybrid policy is first achieved for the constrained switched tracking control. Instead of the general critic-actor structure, only critic neural network (NN) is applied in the algorithm to simply the architecture and manner of implementation. As the main computational burden or load in iteration learning process comes from the information transmissions of tuning NNs, the designed critic-only structure can reduce computational load with less transmissions. Then the convergence of the iteration learning process is demonstrated by theorems and the tracking objective is achieved as the output tracking errors get converged to zero. Finally, the proposed robust tracking control scheme for constrained-input switched systems is applied in the simulation, and the tracking results proved the effectiveness and applicability of the designed method. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Optimal tracking control Switched system Iteration learning Constrained input adaptive dynamic programming

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Asymptotically stable critic designs for approximate optimal stabilization of nonlinear systems subject to mismatched external disturbances

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NEUROCOMPUTING 2020年 396卷 201-208页

作者： Zhao, Bo Shi, Guang Wang, Ding Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Natl Comp Network Emergency Response Tech Team Co Beijing 100029 Peoples R China

This paper addresses approximate optimal stabilization problems for nonlinear systems in the presence of mismatched external disturbances via asymptotically stable critic designs. By establishing the nonlinear disturbance observer, the corresponding information is utilized to construct the online updated cost function, which reflects the real-time disturbances, regulation and control simultaneously. With the help of the proper cost function, the Hamilton-Jacobi-Bellman equation is solved by employing a critic neural network, whose weight vector is guaranteed to be asymptotically stable with nested tuning laws. The approximate optimal control is derived to guarantee the closed-loop system to be ultimately uniformly bounded based on the Lyapunov stability theorem. The effectiveness of the developed stabilization scheme is verified via simulations of two numerical examples. (C) 2019 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming adaptive critic designs Mismatched external disturbances Disturbance observer Optimal control Neural networks Nested tuning law

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Off-policy synchronous iteration IRL method for multi-player zero-sum games with input constraints

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NEUROCOMPUTING 2020年第0期378卷 413-421页

作者： Ren, He Zhang, Huaguang Mu, Yunfei Duan, Jie 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 paper, a novel synchronous off-policy method is given to solve multi-player zero-sum (ZS) game under the condition that the knowledge of system data are completely unknown, the actuators of controls are constrained and the disturbances are bounded simultaneously. The cost functions are built by non-quadratic functions to reflect the constrained properties of inputs. The integral reinforcement learning (IRL) technology is employed to solve Hamilton-Jacobi-Bellman equation, so that the system dynamics are not necessary anymore. The obtained value function is proved to converge to the optimal game values. And the equivalent of traditional policy iteration (PI) algorithm and the proposed algorithm is given in solving the multi-player ZS game with constrained inputs. Three neural networks in this paper are utilized, the critic neural network (CNN) to approach the cost function, the action neural network (ANN) to approach the control policies and the disturbance neural networks (DNN) to approach the disturbances are utilized. Finally, a simulation example is given to demonstrate the convergence and performance of the proposed algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Zero-sum game Integral reinforcement learning Off-policy Constrained optimal control

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Neural network based integral sliding mode optimal flight control of near space hypersonic vehicle

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NEUROCOMPUTING 2020年第0期379卷 41-52页

作者： Xia, Rongsheng Chen, Mou Wu, Qingxian Wang, Yuhui Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211100 Peoples R China

In this paper, based on the integral sliding mode method and adaptive dynamic programming (ADP) algorithm, a robust optimal tracking control scheme is presented for near space hypersonic vehicle (NSHV) system in the presence of unknown modeling error, external disturbance, and input saturation. Firstly, combining neural network, auxiliary system and integral sliding mode methods, an adaptive integral sliding mode control (AISMC) law is designed to guarantee system trajectories tend to a defined integral sliding surface and the effects of modeling uncertainty, external disturbance, and control input saturation are eliminated. Then, the robust optimal tracking control problem of original system is converted into the optimal control problem of a nominal system, and an ADP method with single critic network is utilized to acquire the corresponding optimal controller. Furthermore, Lyapunov analysis method shows that the overall control input which contains AISMC law and optimal controller can ensure all the signals in closed-loop system are stable in the sense of uniform ultimate boundedness (UUB). Finally, simulation results about attitude flight control of NSHV are given to verify the effectiveness of the proposed control scheme. (C) 2019 Published by Elsevier B.V.

关键词： Near space hypersonic vehicle Integral sliding mode control Optimal tracking control adaptive dynamic programming Auxiliary system method Neural network

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Balancing Value Iteration and Policy Iteration for Discrete-Time Control

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第11期50卷 3948-3958页

作者： Luo, Biao Yang, Yin Wu, Huai-Ning Huang, Tingwen Cent South Univ Sch Automat Changsha 410083 Peoples R China Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar Beihang Univ Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China Texas A&M Univ Qatar Dept Sci Doha Qatar

The optimal control problem of discrete-time nonlinear systems depends on the solution of the Bellman equation. In this paper, an adaptive reinforcement learning (RL) method is developed to solve the complex Bellman equation, which balances value iteration (VI) and policy iteration (PI). By adding a balance parameter, an adaptive RL integrates VI and PI together, which accelerates VI and avoids the need of an initial admissible control. The convergence of the adaptive RL is proved by showing that it converges to the Bellman equation. Subsequently, the adaptive RL is realized by using the neural network (NN) approximation for value function and a least-squares scheme is developed for updating NN weights. Then, the convergence of NN-based adaptive RL is proved with considering NN approximation error. To further improve its performance, an adaptive rule is developed for tuning balance parameter in adaptive RL iteration by iteration. Finally, the effectiveness of the adaptive RL is validated with simulation studies.

关键词： Convergence Optimal control Mathematical model Artificial neural networks adaptive systems Nonlinear systems Reinforcement learning adaptive dynamic programming Bellman equation discrete-time neural network (NN) optimal control

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