Concurrent learning-based online approximate feedback-Nash equilibrium solution of N-player nonzero-sum differential games
Systems and Control
2017-07-25 v1 Optimization and Control
Abstract
This paper presents a concurrent learning-based actor-critic-identifier architecture to obtain an approximate feedback-Nash equilibrium solution to an infinite horizon N-player nonzero-sum differential game online, without requiring persistence of excitation (PE), for a nonlinear control-affine system. Under a condition milder than PE, uniformly ultimately bounded convergence of the developed control policies to the feedback-Nash equilibrium policies is established.
Keywords
Cite
@article{arxiv.1310.1384,
title = {Concurrent learning-based online approximate feedback-Nash equilibrium solution of N-player nonzero-sum differential games},
author = {Rushikesh Kamalapurkar and Justin Klotz and Warren E. Dixon},
journal= {arXiv preprint arXiv:1310.1384},
year = {2017}
}