Séminaire hybride à l'Université McGill ou Zoom.

In this talk, we consider seeking (generalized) Nash equilibrium (GNE) over networks for noncooperative games. We reveal the convergence of a typical gradient-play dynamics for GNE seeking from a passivity-based perspective, and then, propose two novel dynamics by passivity-based modification on the gradient-play scheme with convergence guarantees in merely monotone regimes. Following that, resorting to passivity, we develop a unifying framework to generalize the gradient-play dynamics. In addition, we consider a noncooperative game problem played by nonlinear agents with uncertainty. We establish a two-layer framework and systematically convert this problem into a distributed output regulation problem. Under some standard assumptions, we design control laws such that outputs of agents to reach the Nash equilibrium of the game.

Bio: Weijian Li is a postdoctoral fellow in the Department of Electrical and Computer Engineering at University of Toronto. He received his PhD degree in operations research and cybernetics from the University of Science and Technology of China in Nov. 2021. He was also a joint PhD student with the Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences. His research interests include game theory, distributed optimization and network control.