Nash Equilibrium Seeking with Output Regulation
Andrew Romano · TSpace (University of Toronto) · 2018
We consider the problem of Nash equilibrium seeking (NE) for games with agents subject to exogenous signals. Using the framework of output regulation, we design dynamic gradient play feedback control laws that incorporate internal models of the exogenous signals. We start by considering linear agents with linear exosignals under full-information. We incorporate a Laplacian-based consensus algorithm to handle the case of partial information. Finally, we extend this framework to handle a class of nonlinear agents subject to nonlinear exosignals.