Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data
Zhu, Yuanheng1; Zhao, Dongbin1,2; Li, Xiangjun3
2017-03-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号28期号:3页码:714-725
文章类型Article
摘要H-infinity control is a powerful method to solve the disturbance attenuation problems that occur in some control systems. The design of such controllers relies on solving the zero-sum game (ZSG). But in practical applications, the exact dynamics is mostly unknown. Identification of dynamics also produces errors that are detrimental to the control performance. To overcome this problem, an iterative adaptive dynamic programming algorithm is proposed in this paper to solve the continuous-time, unknown nonlinear ZSG with only online data. A model-free approach to the Hamilton-Jacobi-Isaacs equation is developed based on the policy iteration method. Control and disturbance policies and value are approximated by neural networks (NNs) under the critic-actor-disturber structure. The NN weights are solved by the least-squares method. According to the theoretical analysis, our algorithm is equivalent to a Gauss-Newton method solving an optimization problem, and it converges uniformly to the optimal solution. The online data can also be used repeatedly, which is highly efficient. Simulation results demonstrate its feasibility to solve the unknown nonlinear ZSG. When compared with other algorithms, it saves a significant amount of online measurement time.
关键词Adaptive Dynamic Programming (Adp) H-infinity Control Policy Iteration (Pi) Zero-sum Game (Zsg)
WOS标题词Science & Technology ; Technology
DOI10.1109/TNNLS.2016.2561300
关键词[WOS]H-INFINITY CONTROL ; STATE-FEEDBACK CONTROL ; DISCRETE-TIME-SYSTEMS ; POLICY UPDATE ALGORITHM ; LEARNING ALGORITHM ; CRITIC DESIGNS ; CONTROL LAWS ; APPROXIMATION ; EQUATIONS
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61273136 ; Beijing Nova Program(Z141101001814094) ; Science and Technology Foundation of State Grid Corporation of China(DG71-14-032) ; 61573353 ; 61533017)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000395980500020
引用统计
被引频次:21[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/14403
专题复杂系统管理与控制国家重点实验室_深度强化学习
作者单位1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.China Elect Power Res Inst, State Key Lab Control & Operat Renewable Energy &, Beijing 100192, Peoples R China
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Zhu, Yuanheng,Zhao, Dongbin,Li, Xiangjun. Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(3):714-725.
APA Zhu, Yuanheng,Zhao, Dongbin,&Li, Xiangjun.(2017).Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(3),714-725.
MLA Zhu, Yuanheng,et al."Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.3(2017):714-725.
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