Distributed Nash Equilibrium Seeking Design in Network of Uncertain Linear Multi-agent Systems
Xinyu Liu, Yawei Zhang, Xinghu Wang, Haibo Ji · 2020
In this paper, we consider distributed Nash equilibrium seeking design for aggregative games over networks. The agents (players) are specified by high-order uncertain linear dynamics. By combining gradient method and distributed average consensus technique, we first construct a dynamic compensator that can generate a fictitious reference signal converging to the necessary Nash equilibrium point and convert the distributed Nash equilibrium seeking problem into a tracking problem. Then, we solve the latter tracking problem, giving rise to a two-step distributed Nash equilibrium seeking design robust against parametric uncertainties in agents.