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Mixed H2/H∞ Control for Nonlinear Stochastic Systems With Cooperative and Non-Cooperative Differential Game

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第12期69卷 4874-4878页

作者： Ming, Zhongyang Zhang, Huaguang Li, Qiaochu Tong, Xin Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China

This technical note discusses the design process of the cooperative differential game (CDG) with the mixed H2/H infinity control to obtain Pareto solutions for nonlinear stochastic systems by adaptive dynamic programming (ADP). In order to obtain Pareto solutions, a non-cooperative differential game (NCDG) for nonlinear stochastic systems is also studied. It is worth mentioning that the mixed H2/H infinity control can achieve the expected H2 performance under the condition of the H infinity continuous attenuation. Firstly, the two-player nonzero-sum differential game for stochastic systems is given to obtain Nash equilibrium. Then, on this basis, a joint cost function is established, which is derived from Pareto strategies and the corresponding Hamiltonian function. Furthermore, two critic neural networks (NNs) are used to realize Nash equilibrium and Pareto equilibrium, respectively. Finally, a detailed algorithm and a numerical example are given.

关键词： Stochastic systems adaptive dynamic programming neural networks Pareto equilibrium

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adaptive Identifier-Critic-Based Optimal Tracking Control for Nonlinear Systems With Experimental Validation

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第1期52卷 459-472页

作者： Na, Jing Lv, Yongfeng Zhang, Kaiqiang Zhao, Jun Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Yunnan Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China UK Atom Energy Author Remote Applicat Challenging Environm Abingdon OX14 3DB Oxon England

This article presents and practically validates an identifier-critic-based approximate dynamic programming (ADP) method to online address the optimal tracking control problem for nonlinear continuous-time unknown systems. The imposed assumption on precisely known system dynamics is obviated via a neural network (NN) identifier. A static control is first adopted to retain the steady-state tracking response, while an optimal control derived via the ADP method is proposed to regulate the tracking error by minimizing a cost function. A critic NN is then trained online to obtain the solution of the associated Hamilton-Jacobi-Bellman (HJB) equation. The learning of the identifier NN and critic NN is performed online simultaneously by tailoring a novel adaptation method, which can guarantee the convergence of the estimated NN weights. Consequently, the critic NN can be used to construct the optimal control policy directly, such that the actor NN used in the previous ADP schemes is avoided. Simulations are performed to verify the suggested control, and experiments on a helicopter plant are carried out to show its feasibility and improved control response.

关键词： Artificial neural networks Optimal control Convergence Nonlinear dynamical systems 1 f noise adaptive systems adaptive dynamic programming adaptive control neural network (NN) optimal control

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Event-triggered integral reinforcement learning for nonzero-sum games with asymmetric input saturation

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NEURAL NETWORKS 2022年 152卷 212-223页

作者： Xue, Shan Luo, Biao Liu, Derong Gao, Ying South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this paper, an event-triggered integral reinforcement learning (IRL) algorithm is developed for the nonzero-sum game problem with asymmetric input saturation. First, for each player, a novel non quadratic value function with a discount factor is designed, and the coupled Hamilton-Jacobi equation that does not require a complete knowledge of the game is derived by using the idea of IRL. Second, the execution of each player is based on the event-triggered mechanism. In the implementation, an adaptive dynamic programming based learning scheme using a single critic neural network (NN) is developed. Experience replay technique is introduced into the classical gradient descent method to tune the weights of the critic NN. The stability of the system and the elimination of Zeno behavior are proved. Finally, simulation experiments verify the effectiveness of the event-triggered IRL algorithm. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： adaptive dynamic programming Reinforcement learning Event-triggered mechanism Asymmetric input saturation Experience replay

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Neural-network-based control for discrete-time nonlinear systems with denial-of-service attack: The adaptive event-triggered case

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第5期32卷 2760-2779页

作者： Wang, Xueli Ding, Derui Ge, Xiaohua Han, Qing-Long Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia

This article investigates a neural network (NN)-based control problem for unknown discrete-time nonlinear systems with a denial-of-service (DoS) attack and an adaptive event-triggered mechanism (ETM). The considered DoS attacks are described by the occurrence frequency and durations and hence more general in comparison with existing stochastic models. To the addressed problem, a novel adaptive rule adjusting the triggering threshold of ETM is constructed to govern the communication schedule, and an NN-based observer is designed to identify the system dynamics where a piecewise update rule of NN weights is adopted to handle the challenge of the complex time series coming from both ETM and DoS attacks. In light of this kind of protocol- and attack-induced switched systems, a sufficient condition dependent on the occurrence frequency and durations of DoS attacks is elaborately established via the analysis of input-to-state stability. Furthermore, an iteration adaptive dynamic programming approach is proposed to handle the addressed control issue, and the boundedness is discussed to both the estimation errors of the Luenberger-type observer and the identified errors of NN weights of observer networks as well as actor-critic networks with the help of the Lyapunov theory. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.

关键词： adaptive dynamic programming adaptive event-triggered mechanism denial-of-service attacks neural networks

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Command filter and dynamic surface control technology based adaptive optimal control of uncertain nonlinear systems in strict-feedback form

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第17期32卷 9307-9331页

作者： Zhang, Tianping Wang, Shixiong Hua, Yu Xia, Meizhen Yangzhou Univ Coll Informat Engn Dept Automat Yangzhou 225127 Jiangsu Peoples R China Yangzhou Univ Coll Math Sci Yangzhou Jiangsu Peoples R China

In this article, the problem of command filter and event-triggered mechanism based adaptive neural optimal control is discussed for a class of nonlinear systems in the presence of unmodeled dynamics in strict-feedback form. The overall controller design is composed of feedforward controller and feedback controller as well as event-triggered controller design. In the first part, the feedforward controller is constructed by exploiting command filter and introducing the compensation error, and the first-order filter in the classical dynamic surface control technology is replaced by utilizing the second-order filter. In the second part, adaptive dynamic programming algorithm is used to estimate the unknown optimal index function and optimal control signal by the aid of the capability of neural networks. In the third part, based on the feedforward and feedback controllers, an adaptive event-triggered control is developed to avoid the occurrence of Zeno behavior. All the signals in the controlled system are proved to be semi-globally uniformly ultimately bounded through theoretical analysis. Two numerical examples are employed to verify the availability of the proposed method.

关键词： adaptive dynamic programming command filter dynamic surface control event-triggered control optimal control

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Learning-Based Sliding-Mode Control for Underactuated Deployment of Tethered Space Robot With Limited Input

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第3期58卷 2026-2038页

作者： Ma, Zhiqiang Huang, Panfeng Lin, Yuxin Northwestern Polytech Univ Res Ctr Intelligent Robot Sch Astronaut Xian 710072 Peoples R China Northwestern Polytech Univ Natl Key Lab Aerosp Flight Dynam Sch Astronaut Xian 710072 Peoples R China Beihang Univ Sch Aeronaut Sci & Engn Beijing 100191 Peoples R China

This article proposes a learning-based adaptive sliding-mode control for the underactuted deployment of tethered space robot with limited input. The reduced-order system on the sliding surface is optimized by the critic neural network-based nearly optimal approximation algorithm, and both the linear and nonlinear situations are taken into account. To remove the assumption that the state-dependent disturbance is vanishing, the behavior of state synchronization on the sliding surface is revealed for the first time, and the nearly optimal solution of limited input for generating stable trajectory can be approximated by the actor neural network-based adaptive dynamic programming. The hyperbolic tangent of desired input is synthesized into the optimal value function, and it can be obtained by the actor-critic neural network-based reinforcement learning algorithm solving the corresponding Hamilton-Jacobi-Bellman equation. The numerical simulation takes the numerical example of the deployment of tethered space robot to verify the effectiveness of the proposed method.

关键词： Neural networks Robots Aerospace electronics Nonlinear dynamical systems Trajectory Lyapunov methods Aerodynamics Actor-critic neural network adaptive dynamic programming deployment of tethered space robot limited input sliding-mode control underactuated system

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Neural network-based safe optimal robust control for affine nonlinear systems with unmatched disturbances q

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NEUROCOMPUTING 2022年第0期506卷 228-239页

作者： Qin, Chunbin Wang, Jinguang Zhu, Heyang Zhang, Jishi Hu, Shaolin Zhang, Dehua Henan Univ Sch Artificial Intelligence Zhenzhou 450000 Peoples R China Henan Univ Sch Software Kaifeng 475000 Peoples R China Guangdong Univ Petrochem Technol Sch Automation Maoming 525000 Peoples R China

In this paper, for the safety-critical systems with unmatched disturbances, a safe optimal robust control method based on neural network is proposed to ensure that the safety-critical system operates within its safe region and learns the optimal control strategy. The cost function which is the goal of the designers is augmented by a control barrier function (CBF) to achieve both safety and optimality. This method does not directly regard security as a constraint on the system state, but influences the cost function through a security penalty mechanism. An additional function is used to approximate the effect of the unmatched disturbances on the safety-critical systems. On the premise of satisfying the security and robustness, the neural network approximation method is used to learn the optimal control strategy. Based on Lyapunov stability theory, it is shown that the neural network-based safe optimal robust controller can guarantee all the signals of the resulting closed-loop systems to be uniformly ultimately bounded. Finally, two simulation examples are given to demonstrate the effectiveness of the proposed method.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Optimal robust control Neural network adaptive dynamic programming Safety Unmatched disturbances

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Event-triggered-based cooperative game optimal tracking control for modular robot manipulator with constrained input

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NONLINEAR dynamicS 2022年第4期109卷 2759-2779页

作者： Li, Yuanchun Wei, Chongyang An, Tianjiao Ma, Bing Dong, Bo Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China

In this paper, a cooperative game optimal tracking control method based on event-triggered mechanism for constrained input modular robot manipulators (MRMs) system is introduced. According to the joint torque feedback technique, the dynamics model of the constrained input subsystem is established and the global state space equation is derived. The control inputs of n joints in the MRM system with constrained input are taken as n participants of cooperative game, and the tracking control problem of the manipulator system is transformed into the optimal control problem based on the cooperative game. Next, a fusion function containing position and velocity errors is defined to construct the performance index function. In order to improve the control performance and robustness of the manipulator system, part of the known model information is used to devise controller, the model uncertainty is dealt by the neural network (NN) observer, and the optimal compensation control strategy is used to deal with internal disturbance such as sensor measurement error and transmission ripple due to power fluctuations, electromagnetic effects, noise and vibration. Based on the adaptive dynamic programming algorithm and event-triggered mechanism, the optimal tracking control strategy is obtained by approximately solving the event-triggered Hamilton-Jacobi-Bellman equation with the critic NN. The Lyapunov theory proves that trajectory tracking error of MRM system with constrained input is uniformly ultimately bounded. Finally, the experimental results demonstrate the effectiveness of the proposed control method.

关键词： Modular robot manipulators Constrained input Cooperative game [1] adaptive dynamic programming Event-triggered mechanism

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A composite controller for manipulator with flexible joint and link under uncertainties and disturbances

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JOURNAL OF VIBRATION AND CONTROL 2022年第9-10期28卷 1148-1164页

作者： Zhu, Jiahao Zhang, Jian Zhu, Jiangling Zeng, Lingbin Pi, Yangjun Chongqing Univ State Key Lab Mech Transmiss Room 408 Chongqing 400044 Peoples R China Chongqing Univ Sch Automobile Engn Chongqing Peoples R China Chongqing Univ Chongqing Automot Collaborat Innovat Ctr Chongqing Peoples R China Shanghai Inst Aerosp Syst Engn Shanghai Peoples R China

In this article, a composite controller is proposed for the manipulator with the flexible joint and link under uncertainties and time-varying disturbances. The dynamic of the system is developed by the Euler-Lagrange and assumed mode method, which is a nonlinear, strong coupling, and underacted system. Therefore, based on the singular perturbation theory, the dynamic is decomposed into a slow and fast subsystem. For the slow dynamic, a novel adaptive-gain super-twisting sliding mode controller is designed to guarantee joint tracking under the uncertainties and disturbances. For the fast dynamics, adaptive dynamic programming is used to deal with the uncertainty. The simulation result shows that the proposed composite controller can effectively track the trajectory and suppress the vibration simultaneously.

关键词： Flexible joint and link singular perturbation theory uncertainties and disturbances adaptive-gain super-twisting adaptive dynamic programming

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Robust Policy Iteration for Continuous-Time Linear Quadratic Regulation

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第1期67卷 504-511页

作者： Pang, Bo Bian, Tao Jiang, Zhong-Ping NYU Tandon Sch Engn Dept Elect & Comp Engn Control & Networks Lab Brooklyn NY 11201 USA Bank Amer Merrill Lynch New York NY 10036 USA

This article studies the robustness of policy iteration in the context of continuous-time infinite-horizon linear quadratic regulator (LQR) problem. It is shown that Kleinman's policy iteration algorithm is small-disturbance input-to-state stable, a property that is stronger than Sontag's local input-to-state stability but weaker than global input-to-state stability. More precisely, whenever the error in each iteration is bounded and small, the solutions of the policy iteration algorithm are also bounded and enter a small neighborhood of the optimal solution of the LQR problem. Based on this result, an off-policy data-driven policy iteration algorithm for the LQR problem is shown to be robust when the system dynamics are subject to small additive unknown bounded disturbances. The theoretical results are validated by a numerical example.

关键词： Robustness Biological system modeling Optimal control Numerical stability Heuristic algorithms Approximation algorithms Symmetric matrices adaptive dynamic programming adaptive optimal control data-driven control policy iteration reinforcement learning robustness

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