Neural inverse optimal control for discrete-time uncertain nonlinear systems stabilization

This paper discusses neural inverse optimal control to achieve stabilization for discrete-time uncertain nonlinear systems, and minimizing a meaningful cost functional. A neural identifier scheme is used to model the uncertain nonlinear system, and based on this neural model and the knowledge of a control Lyapunov function, then the inverse optimal controller is synthesized in order to achieve exponential stability. An example illustrates the applicability of the proposed control technique.