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Event-triggered distributed self-learning robust tracking control for uncertain nonlinear interconnected systems

APPLIED MATHEMATICS AND COMPUTATION 2021年 395卷 125871-125871页

作者： Cui, Lili Zhang, Yong Wang, Xiaowei Xie, Xiangpeng Shenyang Normal Univ Shenyang Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

In this paper, a novel event-triggered self-learning robust tracking control scheme for a class of nonlinear interconnected systems with uncertain interconnections is developed based on adaptive dynamic programming (ADP). Initially, the robust tracking control problem of interconnected systems is transformed to the robust stabilization one of augmented interconnected systems. To address the uncertain interconnections as well as optimize the sample intervals, a group of auxiliary subsystems, each with a novel discounted cost function, are introduced, which enables the robust stabilization problem to be further converted to a group of two-player zero-sum differential games. Next, an event-triggered ADP algorithm is proposed to obtain the saddle point solutions, based on which the distributed event-triggered robust tracking control policies for the overall system are established. Specifically, the proposed event-triggered mechanism is asynchronous and distributed where the sample intervals are optimized to further reduce the computational burden and save the communication resources. Moreover, the tracking errors and the approximation errors of critic network weights are demonstrated to be uniformly ultimately bounded by using the Lyapunov approach. Finally, two simulation examples are provided to verify the effectiveness of the proposed method. (c) 2020 Elsevier Inc. All rights reserved.

关键词： adaptive dynamic programming Tracking control Differential games Event-triggered Uncertain nonlinear interconnected systems

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Event-Triggered Optimal Safety Control for Nonlinear Safety-Critical Systems with Disturbance

Event-Triggered Optimal Safety Control for Nonlinear Safety-...

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第34届中国控制与决策会议

作者： Heyang Zhu Jinguang Wang Jishi Zhang Dehua Zhang Qiyang Xiao Chunbin Qin School of Artificial Intelligence Henan University School of Software Henan University

In this paper,the event-triggered optimal safety control method is proposed to solve the zero-sum game problem of nonlinear safety-critical systems with ***,we transform the safety-critical system with safety constraints into an equivalent system without safety constraints by using the barrier ***,for relieving the computation pressure and saving communication cost,the event-triggered mechanism is introduced,and a safe eventtriggered condition is presented,meanwhile,the Zeno behavior is *** addition,only a critic neural network(NN) is used to implement the proposed *** the learning process,the past data and current data are used to relax the persistence of excitation(PE) *** to the Lyapunov theory,it can be proved that the states and the weight estimation error of critic NN are uniformly ultimately bounded(UUB).Finally,a simulation example shows the effectiveness of the proposed method.

关键词： adaptive dynamic programming Barrier function Event-triggered control Neural network Safety

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

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

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第34届中国控制与决策会议

作者： Fuyu Zhao Liang Zhao College of Electrical Engineering and Automation Shandong University of Science and Technology Institute of Complexity Science Qingdao UniversityShandong Key Laboratory of Industrial Control Technology

In this paper,a robust policy iteration(PI) based adaptive optimal control approach is proposed for large-scale *** parametric and dynamic uncertainties are coped with by using the robust adaptive dynamic programming and small-gain *** on the robust policy iteration,the learned control policy is composed of the measurement state and *** 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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A Neural Policy Learning Method for Robust Stabilization of Uncertain Nonlinear Systems

A Neural Policy Learning Method for Robust Stabilization of ...

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Chinese Automation Congress (CAC)

作者： Xu, Xin Wang, Ding Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9781728176871

In this paper, a neural-network-based policy learning method is established to solve robust stabilization for a class of continuous-time nonlinear systems with both internal dynamic uncertainties and input matrix uncertainties. First, the robust stabilization problem is converted to an optimal control problem by choosing an appropriate cast function and proving system stability. Then, in order to solve the Hamilton-Jacobi-Bellman equation, a policy iteration algorithm is employed by constructing and training a critic neural network. The approximate optimal control policy can be obtained by this algorithm, and the solution of the robust stabilization can he derived as well. Finally, a numerical example and an experimental simulation are provided to verify the availability of the proposed strategy.

关键词： Neural critic learning intelligent control adaptive dynamic programming robust stabilization uncertain nonlinear systems

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Data-Based Nonaffine Optimal Tracking Control Using Iterative DHP Approach 21st

Data-Based Nonaffine Optimal Tracking Control Using Iterativ...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

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 Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

In this paper, a data-based optimal tracking control approach is developed by involving the iterative dual heuristic dynamic programming algorithm for nonaffine systems. In order to gain the steady control corresponding to the desired trajectory, a novel strategy is established with regard to the unknown system function. Then, according to the iterative adaptive dynamic programming algorithm, the updating formula of the costate function and the new optimal control policy for unknown nonaffine systems are provided to solve the optimal tracking control problem. Moreover, three neural networks are used to facilitate the implementation of the proposed algorithm. In order to improve the accuracy of the steady control corresponding to the desired trajectory, we employ a model network to directly approximate the unknown system function instead of the error dynamics. Finally, the effectiveness of the proposed method is demonstrated through a simulation example. Copyright (C) 2020 The Authors.

关键词： adaptive dynamic programming data-based optimal tracking control iterative dual heuristic dynamic programming neural network

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Generalized value iteration for discounted optimal control with stability analysis

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SYSTEMS & CONTROL LETTERS 2021年 147卷 104847-104847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 Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this work, the generalized value iteration with a discount factor is developed for optimal control of discrete-time nonlinear systems, which is initialized with a positive definite value function rather than zero. The convergence analysis of the discounted value function sequence is provided. The condition for the discount factor is given to guarantee the stability of the controlled plants. With this operation, the iterative control policy that asymptotically stabilizes the closed-loop system can be determined. The introduction of a discount factor has eased some conditions that the system dynamics and the initialization of the generalized value iteration need to fulfill. It is not required that the initial control policy is stabilizing. In the iteration process, under some conditions, if the system is asymptotically stable at the current iteration, then it can be guaranteed that the iterative control policies after this current iteration step also are stabilizing. It is convenient and practical to evaluate the asymptotic stability of the closed-loop system using the iterative control policy. A numerical example with physical background is carried out to validate the present results. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Reinforcement learning adaptive dynamic programming Generalized value iteration Admissible control policy Nonlinear systems Discounted optimal control

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Optimise transient control against DoS attacks on ESS by input convex neural networks in a game

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2021年 28卷

作者： Sun, Jian Li, Penghua Wang, Chunye Southwest Univ Sch Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Post & Telecommun Coll Automat Chongqing 400065 Peoples R China

For improving the transient control of power grids, an alternative fast acting feature is offered by the vast application of energy storage systems (ESS). However, Denial of Service (DoS) attacks can interfere with ESS, significantly hindering the performance of the transient control. In the present study, a framework of a non-zero sum difference game between a transient controller and a DoS attacker was established to determine an optimal control strategy of ESS in DoS attacks. In order to solve the aforementioned game, an input convex neural networks (ICNNs) based adaptive dynamic programming (ADP) scheme was proposed. Regarding the DoS attacker and the transient controller, two long term strategy utility functions are approximated by ICNNs, thereby ensuring the existence of the Nash equilibrium (NE) of the game without requiring a small sampling period or linearity of power system models. Through neural training to reach the NE from the framework of the game, an approximated optimal controller is derived. The simulations of the 9-bus and 30-bus system experiments validate the effectiveness of the proposed transient control scheme against DoS attacks. Further, increased performance of the ICNNs based ADP in multiple sampling periods and the nonlinearity of the power grids are illustrated. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Transient control adaptive dynamic programming Non-zero-sum game Input convex neural networks

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A real-time tracking controller for piezoelectric actuators based on reinforcement learning and inverse compensation

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SUSTAINABLE CITIES AND SOCIETY 2021年 69卷 102822-102822页

作者： Qin, Shijie Cheng, Long Chinese Acad Sci Inst Automat State Key Lab Control & Management Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

Nanotechnology is a promising technology and has been widely applied for sustainable smart cities. As the fundamental devices for nanotechnology, piezoelectric actuators (PEAs) have gained wide attention in precision manufacturing because of the advantages of rapid response, large mechanical force and high resolution. However, the inherent nonlinearities of PEAs hinder wide applications for nano-positioning and high-precision manipulation. To eliminate these nonlinearities, various control methods have been proposed, while the optimal control of PEAs is considered rarely. Inspired by the reinforcement learning, adaptive dynamic programming (ADP) is proposed to solve the optimal tracking control problem of PEAs. In this paper, a controller based on reinforcement learning and inverse compensation is designed for the tracking control of PEAs. The experiments on the PEA platform are designed to verify the effectiveness of the proposed method. Comparisons with some representative controllers have demonstrated that the proposed controller has a better control performance.

关键词： Piezoelectric actuators Real-time tracking control Reinforcement learning adaptive dynamic programming Hysteresis compensation

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A neuro-optimal control method of modular robot manipulators based on nonzero-sum game strategy

A neuro-optimal control method of modular robot manipulators...

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Chinese Automation Congress (CAC)

作者： Dong, Bo Chen, Jingchen Wang, Yuexi Ma, Bing Liu, Guangjun Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun Jilin Peoples R China Ryerson Univ Dept Aerosp Engn Toronto ON Canada

ISBN: (纸本)9781728176871

This paper presents a nonzero-sum strategy-based nenro-optimal control method for modular robot manipulators (MRMs). Based on joint torque feedback (JTF) technique, the dynamic model of the manipulator systems is described as an integration of joint subsystems. A local dynamic information-based robust compensator is designed to engage the model uncertainty compensation, and then, the optimal tracking control problem of an MRM system is transformed into an n-player nonzero-sum game issue of multiple joint subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a cost function approzimator is developed for solving the Hamilton-Jacobi (W) equation, thus facilitating the feasible derivation of the neuro-optimal control policy. Finally, experiment results illustrated the effectiveness of the developed control method.

关键词： modular robot manipulators adaptive dynamic programming nonzero-sum differential game optimal control

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Optimal trajectory tracking control based on reinforcement learning for the deployment process of space tether system 6

Optimal trajectory tracking control based on reinforcement l...

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6th Conference on Advances in Control and Optimization of dynamical Systems (ACODS)

作者： Feng, Yiting Wang, Changqing Li, Aijun Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

Space tether system has a wide application prospect in space mission. Due to the characteristics of strong non-linearity and under-actuation, as well as the interference of complex space environment, it is difficult to model the tethered system accurately. Hence, the controller based on the parameters of the system model will cause large errors in the process of control. In this paper, an adaptive dynamic programming algorithm based on reinforcement learning theory is adopted. By training two Back Propagation (BP) neural networks, namely critic neural network (NN) and actor NN, the perfounance index function and control law of the system approach approximate optimal values respectively. The controller design is independent of the system model, so model-free control of the system is realized by implementing this control method. First, assuming that the out-of-plane motion of the system is stable, the optimal deployment trajectory of the tethered system is obtained by parameter optimization based on Nelder-Mead method. The optimal trajectory is taken as the nominal trajectory and the trajectory tracking is carried out by reinforcement learning controller. The simulation results show that the reinforcement learning algorithm has a good control effect on the in-plane trajectory tracking of the tethered system, which proves the feasibility and robustness of the control method. (C) 2020, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： space tether system deployment trajectory tracking neural network adaptive dynamic programming reinforcement learning

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