Towards Closed-loop Stability of Nonlinear Receding Horizon Games
For researchers in game theory and control, this work extends stability guarantees from model predictive control to receding horizon games, addressing a known gap.
The paper proves sufficient conditions for practical asymptotic convergence of closed-loop trajectories in nonlinear receding horizon games without terminal ingredients, and shows that the region of attraction shrinks exponentially with horizon length.
We analyze Receding Horizon Games without any MPC-like terminal ingredients. We show that recursive feasibility can be inferred from the turnpike phenomenon under mild assumptions. Moreover, we prove sufficient conditions for practical asymptotic convergence of the closed-loop trajectories, and we discuss how the gap towards practical asymptotic stability may be closed. We use numerical examples to show that the closed-loop region of attraction around the steady-state GNE shrinks exponentially with the horizon length, a behavior previously known only for model predictive control. Further, we apply a linear end penalty and demonstrate in numerical simulations that it suppresses the leaving arc and ensures asymptotic convergence to the steady-state GNE.