Online Iterative Adaptive Dynamic Programming Approach for Solving the Zero-Sum Game for Nonlinear Continuous-Time Systems with Partially Unknown Dynamics

被引：1

作者：

Fu, Bin ^{[1
]}

Sun, Bo ^{[2
]}

Guo, Hang ^{[1
]}

Yang, Tao ^{[1
]}

Fu, Wenxing ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Shanxi, Peoples R China

[2] Delft Univ Technol, Fac Aerosp Engn, NL-2629 HS Delft, Netherlands

来源：

PROCEEDINGS OF 2022 INTERNATIONAL CONFERENCE ON AUTONOMOUS UNMANNED SYSTEMS, ICAUS 2022 | 2023年 / 1010卷

关键词：

Approximation dynamic programming; Zero-sum game; Integral reinforcement learning; Online learning; Value iteration; REINFORCEMENT; DESIGN;

D O I：

10.1007/978-981-99-0479-2_262

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The current study presents an online iterative adaptive dynamic programming approach to resolve the zero-sum game (ZSG) for nonlinear continuous-time (CT) systems containing a partially unknown dynamic. The Hamilton-Jacobian-Issacs (HJI) equation is solved along the state trajectory according to the value function approximation and the policy improvement online. Relaxed dynamic programming is utilized to ensure the algorithm's convergence. Model and costate networks were established to conduct the method. Computational simulations are performed to present the efficiency of the algorithm.

引用

页码：2833 / 2842

页数：10

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