Robust Policy Learning Control of Nonlinear Plants With Case Studies for a Power System Application

Abstract

In view of the prevalence of dynamic uncertainties, we study the robust policy learning control of nonlinear plants in this paper. The auxiliary system and policy learning techniques are integrated to accomplish robust stabilization of mismatched nonlinear systems. First, the uncertain dynamics is handled by proper transformation, so as to construct an optimal regulation problem with respect to an augmented auxiliary system. Then, the integral policy iteration algorithm is employed for optimal control design without requiring system dynamics. The equivalence results involved in problem transformation and algorithm improvement are analyzed. After that, the actor-critic structure is adopted with least squares implementation for approximate calculation. Finally, the experimental simulation with an application to a power system is provided, which demonstrates the validity of the adaptive robust control strategy. The present policy learning algorithm does not rely on whole information of system dynamics and the established robust control technique is applicable for nonlinear plants subjected to mismatched uncertainties.