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41st Chinese Control Conference (CCC)

作者： Chu, Kun Peng, Zhinan Zhang, Zhiquan Huang, Rui Shi, Kecheng Cheng, Hong Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9789887581536

In this paper, a novel identifier-critic-based event-triggered control method is proposed to solve the H8 control problem of unknown nonlinear systems. First, system identification is proposed to reconstruct the completely unknown system dynamics, and neural networks (NNs) based critic learning framework is established to online approximated cost function and obtain corresponding optimal control law. It is highlighted that new NNs weights adaption rules based on a parameter estimation method are designed for the identifier-critic framework. In the meantime, a static event-triggered mechanism is integrated into the proposed identifier-critic architecture to improve communication and computation efficiency. The stability of the control system and the convergence of NN weights are both theoretically proved under the proposed control method. Simulation studies are provided to demonstrate the effectiveness of the proposed H-infinity control method.

关键词： Optimal control nonlinear systems adaptive dynamic programming event-triggered mechanism

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adaptive Optimal Control for Large-Scale Systems based on Robust Policy Iteration 34

Adaptive Optimal Control for Large-Scale Systems based on Ro...

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34th Chinese Control and Decision Conference (CCDC)

作者： Zhao, Fuyu Zhao, Liang Shandong Univ Sci Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Qingdao Univ Inst Complex Sci Shandong Key Lab Ind Control Technol Qingdao 266071 Peoples R China

ISBN: (纸本)9781665478960

In this paper, a robust policy iteration (PI) based adaptive optimal control approach is proposed for large-scale systems. The parametric and dynamic uncertainties are coped with by using the robust adaptive dynamic programming and small-gain theory. Based on the robust policy iteration, the learned control policy is composed of the measurement state and input. The closed-loop stability and the convergence of the proposed algorithms are analyzed by using Lyapunov stability theory and small-gain techniques. A practical example of multimachine power systems with governor controllers is given to demonstrate the effectiveness of the proposed method.

关键词： Optimal control adaptive dynamic programming robust policy iteration large-scale systems

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adaptive Critic Design-Based Robust Cooperative Tracking Control for Nonlinear Multi-Agent Systems With Disturbances

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IEEE ACCESS 2021年 9卷 34383-34394页

作者： Qu, Qiuxia Sun, Liangliang Li, Zhigang Shenyang Jianzhu Univ Sch Informat & Control Engn Shenyang 110168 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110169 Peoples R China

In this paper, a novel robust cooperative tracking control algorithm is proposed for nonlinear multi-agent graphical games with disturbances based on adaptive dynamic programming approach. The robust cooperative tracking control policy is obtained by multiplying the coupling gains to the interactive Nash solution of nominal multi-agent systems. The consensus error dynamics and cost function for each node depend only on the information of itself and its neighbors. Through Lyapunov approach, the robust stability conditions are derived to guarantee all the followers synchronize to the leader. A cooperative policy iteration algorithm is utilized to approximatly solve the coupled Hamilton-Jacobi-Isaacs equations, and only critic neural network is employed to approximate the value function and control policy for each node. A novel network weight tuning laws is proposed to guarantee uniform ultimate boundedness of closed-loop systems. Simulation results are utilized to demonstrate the effectiveness of the theoretical results.

关键词： Multi-agent systems Games Topology Mathematical model Synchronization Differential games Cost function adaptive dynamic programming leader-following consensus problem multi-agent systems neural network

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Event-driven adaptive near-optimal tracking control of the robot in aircraft skin inspection

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2021年第7期31卷 2593-2613页

作者： Wu, Xuewei Wang, Congqing Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China

In this article, we discuss a near-optimal tracking control problem (NOTCP) of robots used for inspecting aircraft skin with partially unknown systems, unmeasurable states, unknown disturbances, and unknown output delay. A novel observer based on an augmented neural network is designed to overcome the unknown disturbances, unknown output delay, and unknown internal states. An augmented system state, composed of the tracking error and reference system state, is proposed to introduce a new nonquadratic discounted performance function for the NOTCP. Due to the complexity in solving the Hamilton-Jacobi-Bellman equation, an online policy iteration is presented under the adaptive dynamic programming (ADP) framework. Unlike the traditional ADP, the event-driven algorithm updates the control input only when the event is triggered, which reduces the computational cost and transmission load. Both the control policy and the observer are updated according to the developed triggering condition. Convergence to a near-optimal control solution and the stability analysis of the proposed algorithm are shown through the Lyapunov candidate function for both the continuous and jump dynamics. The performance of the proposed algorithm is demonstrated by simulation.

关键词： adaptive dynamic programming augmented neural network observer event driven nonlinear-partially unknown systems online learning and control

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Observer-based event-triggered control for zero-sum games of input constrained multi-player nonlinear systems

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NEURAL NETWORKS 2021年 144卷 101-112页

作者： Zhang, Shunchao Zhao, Bo Liu, Derong Zhang, Yongwei Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

In this paper, an event-triggered control (ETC) method is investigated to solve zero-sum game (ZSG) problems of unknown multi-player continuous-time nonlinear systems with input constraints by using adaptive dynamic programming (ADP). To relax the requirement of system dynamics, a neural network (NN) observer is constructed to identify the dynamics of multi-player system via the input and output data. Then, the event-triggered Hamilton-Jacobi-Isaacs (HJI) equation of the ZSG can be solved by constructing a critic NN, and the approximated optimal control law and the worst disturbance law can be obtained directly. A triggering scheme which determines the updating time instants of the control law and the disturbance law is developed. Thus, the proposed ADP-based ETC method cannot only reduce the computational burden, but also save communication resource and bandwidths. Furthermore, we prove that the signals of the closed-loop system and the approximate errors of the critic NN weights are uniformly ultimately bounded by using Lyapunov's direct method, and the Zeno behavior is excluded. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed ETC scheme. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： adaptive dynamic programming Multi-player zero-sum games Input constraints Event-triggered control Neural networks

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Data-Based Optimal Tracking of Autonomous Nonlinear Switching Systems

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 227-238页

作者： Xiaofeng Li Lu Dong Changyin Sun the School of Automation Southeast UniversityNanjing 210096 the Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China the College of Electronics and Information Engineering Tongji UniversityShanghai 201804China

In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching *** system state is forced to track the reference signal by minimizing the performance ***,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled *** linear-in-parameter(LIP)neural network is taken as the value function *** the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable ***,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is ***,the effectiveness of the algorithm is demonstrated with three simulation examples.

关键词： adaptive dynamic programming approximation error data-based control Q-learning switching system

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Model-free intelligent critic design with error analysis for neural tracking control

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NEUROCOMPUTING 2024年 572卷

作者： Gao, Ning Wang, Ding Zhao, Mingming Hu, Lingzhi Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China

The core of the optimal tracking control problem for nonlinear systems is how to ensure that the controlled system tracks the desired trajectory. The utility functions in previous studies have different properties which affect the final tracking effect of the intelligent critic algorithm. In this paper, we introduce a novel utility function and propose a Q -function based policy iteration algorithm to eliminate the final tracking error. In addition, neural networks are used as function approximator to approximate the performance index and control policy. Considering the impact of the approximation error on the tracking performance, an approximation error bound for each iteration of the novel Q -function is established. Under the given conditions, the approximation Q -function converges to the finite neighborhood of the optimal value. Moreover, it is proved that weight estimation errors of neural networks are uniformly ultimately bounded. Finally, the effectiveness of the algorithm is verified by the simulation example.

关键词： adaptive dynamic programming Approximation errors Model-free control Optimal tracking control Policy iteration Neural networks

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Linear quadratic stochastic optimal control with state- and control-dependent noises: A deterministic data approach

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NEUROCOMPUTING 2024年 575卷

作者： Zhang, Heng Yan, Zhiguo Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Qilu Univ Technol Shandong Acad Sci Sch Informat & Automat Jinan 250353 Peoples R China

This paper investigates an infinite -horizon linear quadratic stochastic optimal control (LQSOC) problem with state- and control -dependent noises, in which two system matrices are unknown. First, a deterministic system is introduced and a relationship among its system trajectory, its control and the matrices to be solved is formulated. Subsequently, based on the deterministic system, an adaptive dynamic programming (ADP) algorithm is established to tackle the LQSOC problem. Then, the convergence analysis is presented by proving the equivalence between the proposed algorithm and an existing policy iteration algorithm. Finally, the effectiveness of the obtained algorithm is verified by a perturbed turbocharged diesel engine optimal control design example.

关键词： Linear quadratic Stochastic optimal control State-and control-dependent noises adaptive dynamic programming

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Fault-tolerant tracking control for nonlinear systems with multiplicative actuator faults in view of zero-sum differential games

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NEUROCOMPUTING 2024年 567卷

作者： Tan, Cong Lin, Mingduo Xia, Hongbing Zhao, Bo Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China

In view of zero-sum differential games, this paper addresses the fault-tolerant tracking control (FTTC) problem for nonlinear systems with multiplicative actuator faults. By augmenting the nominal system, the trajectory tracking problem is transformed into an optimal regulation problem. A fault observer is designed to estimate the multiplicative actuator fault factor. In order to better reflect system performance, a comprehensive performance index function including the trajectory tracking error, the control input, and the multiplicative actuator fault is constructed. By considering the control input and the multiplicative actuator fault as two players in the zero-sum differential game (ZSDG), the FTTC problem is thus transformed into a ZSDG problem. Then, a fault-tolerant control based on ZSDG is designed by solving the Hamilton-Jacobi-Isaacs (HJI) equation. Since the HJI equation is difficult to solve, a critic neural network is constructed to obtain its approximation, which constitutes the Nash equilibrium of the ZSDG. The closed-loop faulty system is guaranteed to be uniformly ultimately bounded via the Lyapunov's direct method. The effectiveness of the developed FTTC method is demonstrated by two simulation examples.

关键词： adaptive dynamic programming Reinforcement learning Zero-sum differential games Fault-tolerant control Multiplicative faults

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Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第9期35卷 096205-096205页

作者： Dong, Bo Ding, Zhendong An, Tianjiao Cui, Yiming Zhu, Xinye Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China

Under an event-triggered mechanism, a non-zero-sum (NZS) game optimal tracking control method for modular robot manipulator (MRM) systems with input constraints is proposed using the adaptive dynamic programming (ADP) method based on integral reinforcement learning (IRL). First, a dynamic model of the MRM system is developed based on joint torque feedback technology, consisting of an n-joint subsystem related to interconnected dynamic coupling (IDC). Second, we design a robust compensation controller to handle the known model term and an optimal compensation controller to deal with the uncertainty term caused by the IDC and friction, respectively. In addition, a nonlinear disturbance observer is established to dispose of the negative effects caused by the uncertain sensor output disturbance. Third, based on differential game theory, we transform the optimal tracking control problem of the MRM system into an n-player NZS game problem. Then, the IRL-based ADP method is adopted, which relaxes the need for system partial unknown dynamic information, and only a critic neural network is used to solve the coupled Hamilton-Jacobi equation, so as to obtain the optimal control policy. Then, using Lyapunov theory, the tracking error of the MRM system is demonstrated to be uniformly ultimately bounded. Finally, the effectiveness and superiority of the proposed algorithm are verified through experiments.

关键词： modular robot manipulators non-zero-sum game adaptive dynamic programming input constraint and event-triggered mechanism

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