Dynamic neural network-observer-based adaptive inverse optimal control design for unknown nonlinear systems

An observer-based adaptive inverse optimal control (AIOC) scheme is developed for unknown nonlinear systems. First, a dynamic neural network (DNN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed based on control Lyapunov function (CLF) via inverse optimal method to obtain the optimal feedback control. In this framework, a two-layer DNN is used to construct the observer which can be implemented to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics. The optimal control scheme is implemented to online control the unknown nonlinear systems and the DNN observer simultaneously. An asymptotic stability of the closed-loop system is guaranteed using Lyapunov-based analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme.