Nash Equilibrium Seeking for High-order Multi-agent Systems with Unknown Dynamics

TL;DR

This paper develops a distributed adaptive control method for high-order multi-agent systems with unknown dynamics to find Nash equilibria in noncooperative games, extending existing results beyond single integrator models.

Contribution

It introduces a novel approach combining auxiliary game dynamics and adaptive control for high-order agents with unknown nonlinearities.

Findings

Successfully achieves Nash equilibrium seeking in high-order uncertain systems.

Demonstrates convergence of parameters under persistence of excitation.

Validated effectiveness through numerical simulations.

Abstract

In this paper, we consider a Nash equilibrium seeking problem for a class of high-order multi-agent systems with unknown dynamics. Different from existing results for single integrators, we aim to steer the outputs of this class of uncertain high-order agents to the Nash equilibrium of some noncooperative game in a distributed manner. To overcome the difficulties brought by the high-order structure, unknown nonlinearities, and the regulation requirement, we first introduce a virtual player for each agent and solve an auxiliary noncooperative game for them. Then, we develop a distributed adaptive protocol by embedding this auxiliary game dynamics into some proper tracking controller for the original agent to resolve this problem. We also discuss the parameter convergence problem under certain persistence of excitation condition. The efficacy of our algorithms is verified by numerical examples.