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

Fu, Bin; Sun, B.; Guo, Hang; Yang, Tao; Fu, Wenxing

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

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

B. Sun

Hang Guo Northwestern Polytechnical University

Tao Yang Northwestern Polytechnical University

Wenxing Fu Northwestern Polytechnical University

DOI: https://doi.org/10.1007/978-981-99-0479-2_262

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

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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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