Recursive Computation of Static Output Feedback Stochastic Nash Games for Weakly-Coupled Large-Scale Systems
This paper discusses the infinite horizon static output feedback stochastic Nash games involving state-dependent noise in weakly coupled large-scale systems. In order to construct the strategy. the conditions for the existence of equilibria have been derived front the solutions of the sets of cross-coupled stochastic algebraic Riccati equations (CSAREs). After establishing the asymptotic structure along with the positive semidefiniteness for the solutions of CSAREs, recursive algorithm for solving CSAREs is derived. As a result, it is shown that the proposed algorithm attains the reduced-order computations and the reduction of the CPU time. As another important contribution. the uniqueness of the strategy set is proved tor the sufficiently small parameter epsilon. Finally. in order to demonstrate the efficiency of the proposed algorithm, numerical example is given.
static output feedback stochastic nash games state-dependent noise recursive algorithm