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

Conference paper (2023)

Authors

Bin Fu Northwestern Polytechnical University
Hang Guo Northwestern Polytechnical University
Tao Yang Northwestern Polytechnical University
Wenxing Fu Northwestern Polytechnical University
Copyright: © 2023 Bin Fu, B. Sun, Hang Guo, Tao Yang, Wenxing Fu
DOI: https://doi.org/10.1007/978-981-99-0479-2_262
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Published Date

2023

Language

English

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Abstract

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.

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