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第33届中国控制与决策会议

作者： Ruyue Yang Ding Wang Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing Institute of Artificial Intelligence Beijing University of Technology

To solve the optimal control problem of the nitrate-nitrogen concentration in the wastewater treatment plant(WWTP),a model-free off-policy iterative adaptive dynamic programming algorithm using online data is *** the actor-critic structure,the developed algorithm approximates the optimal Q-function by minimizing the temporal difference and improves the control law through the policy gradient *** networks are utilized in the proposed ***,the experience replay buffer is involved in the off-policy iteration of neural ***,simulation examples for a nonlinear system and WWTP are presented to verify the effectiveness of the proposed method.

关键词： adaptive dynamic programming Model-Free Off-Policy Learning Wastewater Treatment Plant

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On-orbit Reconfiguration Using adaptive dynamic programming for Multi-mission-constrained Spacecraft Attitude Control System

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2019年第4期17卷 822-835页

作者： Cheng, Yue-Hua Jiang, Bin Li, Huan Han, Xiao-dong Nanjing Univ Aeronaut & Astronaut Coll Astronaut Engn Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Jiangsu Peoples R China China Acad Space Technol Inst Telecommun Satellite Beijing 100094 Peoples R China

For the on-orbit reconfiguration problem of spacecraft attitude control systems under multi-mission constraints, the idea of a reinforcement-learning algorithm is adopted, and an adaptive dynamic programming algorithm for on-orbit reconfiguration decision-making that is based on a dual optimization index is proposed. Two optimization objectives, total mission reward and total control cost (energy consumption), are defined to obtain the optimal reconfiguration policy of the spacecraft attitude control system reconfiguration, and the on-orbit reconfiguration model for multi-mission constraints is established. Then, based on the Bellman optimality principle, the optimal reconfiguration policy formulated by the discrete HJB equation is obtained. Since the HJB equation is difficult to solve accurately, a method of bi-objective adaptive dynamic programming is proposed to obtain the optimal reconfiguration policy. This method constructs a mission network and an energy network. The method then adopts a Q-learning-based algorithm to train the networks to estimate the values of total mission reward and total control cost to achieve the on-orbit optimal reconfiguration decision under multi-mission constraints. Simulation results for different cases demonstrate the validity and rationality of the proposed method.

关键词： adaptive dynamic programming attitude control system multi-mission constraints on-orbit reconfiguration reinforcement learning

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Robust Optimal Control of Wave Energy Converters Based on adaptive dynamic programming

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2019年第2期10卷 961-970页

作者： Na, Jing Li, Guang Wang, Bin Herrmann, Guido Zhang, Siyuan Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Yunnan Peoples R China Queen Mary Univ London Sch Engn & Mat Sci London E1 4NS England Univ Bristol Dept Mech Engn Bristol BS8 1TH Avon England

This paper presents a robust adaptive optimal control strategy for wave energy converters (WECs). We first propose a new estimator in a simple form to address modeling uncertainties and formulate the control of WECs as an optimal control problem. Then, a novel energy maximization control strategy is developed based on the concept of adaptive dynamic programming (ADP), where a critic neural network (NN) is used to approximate the time-dependent optimal cost value. To achieve guaranteed convergence, a recently proposed adaptive law based on the parameter estimation error is further tailored to update the weights of critic NN online. Consequently, the critic NN output, e.g., the costate, can he used to compute the optimal feedback control. The proposed robust ADP WEC control method is not only effective in handling dynamic uncertainties, but also computationally efficient with a very fast online convergence rate for the weights of the critic NN (less than 20 seconds for irregular sea waves as demonstrated in the simulations). These advantages significantly enhance the real-time applicability of the proposed method. Simulation results show that this approach is robust to model uncertainties and has significantly reduced computational costs for implementation.

关键词： Wave energy converter adaptive optimal control adaptive dynamic programming uncertainty estimator

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Nonlinear trajectory-tracking control for autonomous underwater vehicle based on iterative adaptive dynamic programming

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第3期37卷 4205-4215页

作者： Che, Gaofeng Liu, Lijun Yu, Zhen Xiamen Univ Sch Aerosp Engn Xiamen 361102 Fujian Peoples R China

In this paper, an iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle. Two iteration procedures are used in the method, which are the i-iteration and the *** i-iteration aims to obtain iterative trajectory-tracking control laws and the j-iteration aims to obtain iterative value functions in the i-iteration. The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. The policy iteration algorithm is the interacting policy and value iteration algorithms. The neural networks are used to realize the proposed algorithm and the convergence and optimality properties of the proposed algorithm are analysed. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm.

关键词： adaptive dynamic programming autonomous underwater vehicle discrete-time neural network trajectory-tracking control

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adaptive dynamic programming and Cooperative Output Regulation of Discrete-time Multi-agent Systems

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2018年第5期16卷 2273-2281页

作者： Gao, Weinan Liu, Yiyang Odekunle, Adedapo Yu, Yunjun Lu, Pingli Georgia Southern Univ Allen E Paulson Coll Engn & Comp Dept Elect & Comp Engn Statesboro GA 30460 USA Chinese Acad Sci Shenyang Inst Automat Beijing 110016 Peoples R China Nanchang Univ Informat Engn Sch Dept Elect & Automat Engn Nanchang 330029 Jiangxi Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

In this paper, a novel data-driven control solution to cooperative output regulation problems is proposed for a class of discrete-time multi-agent systems. Different from existing solutions to cooperative output regulation problems, the dynamics of all the followers are presumed unknown. Based on the combination of the internal model principle and the value iteration technique, a distributed suboptimal controller is learned by means of online input-state data collected from system trajectories. Notably, the developed learning algorithm does not rely on a priori knowledge of a stabilizing control policy. Rigorous theoretical analysis guarantees the convergence of the algorithm and the stability of the closed-loop system. Numerical results validate the effectiveness of the proposed control methodology.

关键词： adaptive dynamic programming cooperative control multi-agent systems output regulation value iteration

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adaptive dynamic programming-based stabilization of nonlinear systems with unknown actuator saturation

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NONLINEAR dynamicS 2018年第4期93卷 2089-2103页

作者： Zhao, Bo Jia, Lihao Xia, Hongbing Li, Yuanchun Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Brain Inspired Intelligence Inst Automat Beijing 100190 Peoples R China Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China

This paper addresses the stabilizing control problem for nonlinear systems subject to unknown actuator saturation by using adaptive dynamic programming algorithm. The control strategy is composed of an online nominal optimal control and a neural network (NN)-based feed-forward saturation compensator. For nominal systems without actuator saturation, a critic NN is established to deal with the Hamilton-Jacobi-Bellman equation. Thus, the online approximate nominal optimal control policy can be obtained without action NN. Then, the unknown actuator saturation, which is considered as saturation nonlinearity by simple transformation, is compensated by employing a NN-based feed-forward control loop. The stability of the closed-loop nonlinear system is analyzed to be ultimately uniformly bounded via Lyapunov's direct method. Finally, the effectiveness of the presented control method is demonstrated by two simulation examples.

关键词： adaptive dynamic programming Unknown actuator saturation Continuous-time nonlinear systems Stabilizing control Neural networks

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adaptive dynamic programming for security of networked control systems with actuator saturation

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INFORMATION SCIENCES 2018年 460卷 51-64页

作者： Yang, Hongjiu Li, Ying Yuan, Huanhuan Liu, Zhixin Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

In this paper, an optimal control problem is investigated for a networked control system with actuator saturation under denial-of-service attacks. An intrusion detection system is introduced to defend denial-of-service attacks in the networked control system. Game theory is used to describe interaction between denial-of-service attacks and the intrusion detection system. A performance index function is presented to deal with actuator saturation by an iterative adaptive dynamic programming algorithm. Back-propagation neural networks are used to realize the iterative adaptive dynamic programming algorithm. Two numerical simulations are given to demonstrate effectiveness of the proposed method. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Networked control systems Denial-of-Service attacks Actuator saturation adaptive dynamic programming Neural networks

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Output feedback adaptive dynamic programming for linear differential zero-sum games

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AUTOMATICA 2020年 122卷 109272-109272页

作者： Rizvi, Syed Ali Asad Lin, Zongli Brown, Charles L. Univ Virginia Charles L Brown Dept Elect & Comp Engn POB 400743 Charlottesville VA 22904 USA

This paper addresses the problem of finding optimal output feedback strategies for solving linear differential zero-sum games using a model-free approach based on adaptive dynamic programming (ADP). In contrast to their discrete-time counterparts, differential games involve continuous-time dynamics and existing ADP approaches to their solutions require access to full measurement of the internal state. This difficulty is due to the fact that direct translation of the discrete-time output feedback ADP results requires derivatives of the input and output measurements, which is generally prohibitive in practice. This work aims to overcome this difficulty and presents a new embedded filtering based observer approach towards designing output feedback ADP algorithms for solving the differential zero-sum game problem. Two output feedback ADP algorithms based respectively on policy iteration and value iteration are developed. The proposed scheme is completely online in nature and works without requiring information of the system dynamics. In addition, this work also addresses the excitation bias problem encountered in output feedback ADP methods, which typically requires a discounting factor for its mitigation. We show that the proposed scheme is bias-free, and therefore, does not require a discounting factor. It is shown that the proposed algorithms converge to the solution obtained by solving the game algebraic Riccati equation. Two numerical examples are demonstrated to validate the proposed scheme. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： adaptive dynamic programming Approximate dynamic programming Zero-sum games Output feedback

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Online Dual-Network-Based adaptive dynamic programming for Solving Partially Unknown Multi-Player Non-Zero-Sum Games With Control Constraints

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IEEE ACCESS 2020年 8卷 182295-182306页

作者： Liu, Pengda Zhang, Huaguang Liu, Chong Su, Hanguang Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China

In this article, a novel online method for multi-player non-zero-sum (NZS) differential games of nonlinear partially unknown continuous time (CT) systems with control constraints is developed based on neural networks (NN). The issue of multi-player NZS games with saturated actuator is elaborately analyzed and the unknown dynamics model is learned by applying identifier NN. Different from using the standard identifier-actor-critic framework of adaptive dynamic programming (ADP), the proposed method uses only identifier networks and critic networks for all the players to solve the coupled Hamilton-Jacobi (HJ) equations for multi-player NZS games, which could effectively simplify the algorithm and save computing resources. Moreover, a tuning law which utilizes the gradient descent method is designed for each critic network. Meanwhile, to remove the requirement for the initial stabilizing control, a novel stability term is designed to ensure the system stability during the training phase of the critic NN. By the means of Lyapunov approach, it is proven that the system states, the critic network weight estimation errors and the obtained control are all uniformly ultimately bounded (UUB). Finally, two numerical examples are simulated to illustrate the validity of the developed method for multi-player NZS games with control constraints.

关键词： Games Heuristic algorithms Mathematical model Optimal control Artificial neural networks dynamic programming adaptive critic designs adaptive dynamic programming control constraints multi-player non-zero-sum games

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Fault tolerant tracking control for nonlinear systems with actuator failures through particle swarm optimization-based adaptive dynamic programming

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APPLIED SOFT COMPUTING 2020年第PartA期97卷 106766-106766页

作者： Liu, Xi Zhao, Bo Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

In this paper, an adaptive dynamic programming based fault tolerant tracking control (FTTC) method is proposed for nonlinear systems with actuator failures. To solve the tracking control problem, the considered system is augmented by combining the tracking error dynamics and the desired trajectory dynamics. By establishing a critic neural network, whose weight vector is updated by particle swarm optimization algorithm, the value function with discount factor is approximated to solve the Hamilton-Jacobi-Bellman equation, and the optimal tracking control law is derived. The neural network-based fault observer is established to compensate the control input online. Then, the augmented system states are guaranteed to be uniformly ultimately bounded under the developed FTTC method according to the Lyapunov stability theorem. Two simulation examples are provided to illustrate the effectiveness of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Reinforcement learning Fault tolerant control Optimal control Neural networks Particle swarm optimization

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