Discrete-time inverse optimal neural control for synchronous generators

This paper presents a robust inverse optimal neural control approach for stabilization of discrete-time uncertain nonlinear systems, which simultaneously minimizes a meaningful cost functional. A neural identifier scheme is used to model the uncertain system, and based on this neural model and an appropriate control Lyapunov function, then the robust inverse optimal neural controller is synthesized. Applicability of the proposed scheme is illustrated via simulation results for a synchronous generator model.