Title: Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game based on Online Data
Authors: Zhu, YH; Zhao, DB; Li, XJ
Author Full Names: Zhu, Yuanheng; Zhao, Dongbin; Li, Xiangjun
Source: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 28 (3):714-725; 10.1109/TNNLS.2016.2561300 MAR 2017
Language: English
Abstract: 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.
ISSN: 2162-237X
eISSN: 2162-2388
IDS Number: EN4MB
Unique ID: WOS:000395980500020
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Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game based on Online Data
Mar 31, 2017Author: