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A System-Level Approach to Generalized Feedback Nash Equilibrium Seeking in Partially Observed Games
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This work proposes an algorithm for seeking generalized feedback Nash equilibria (GFNEs) in noncooperative dynamic games. The focus is on cyber-physical systems with dynamics that are linear, stochastic, potentially unstable, and partially observed. We employ system-level synthesis (SLS) to reformulate the problem as the search for an equilibrium profile of closed-loop responses to noise, which can then be used to reconstruct a stabilizing output-feedback policy. Under this setup, we leverage monotone operator theory to design a GFNE-seeking algorithm capable of enforcing closed-loop stability, operational constraints, and communication constraints onto the control policies. This algorithm is amenable to numerical implementation, and we provide conditions for its convergence. We demonstrate our approach in a simulated experiment on the noncooperative stabilization of a decentralized power grid.
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