Adaptive optimal tracking control for a class of nonlinear systems with fully unknown parameters

In this paper, a new adaptive optimal tracking approximate solution for the infinite-horizon function is presented to design a new controller for a class of fully unknown continuous-times nonlinear systems. A dynamic neural network identifier (DNN) derived from a Lyapunov function, is achieved to approximate the unknown system dynamics. We utilize an adaptive steady-state controller based on the identified plant to keep tracking performance and an adaptive optimal controller is used to stabilize the systems. A critic neural network is utilized for estimating optimal value function of the Hamilton-Jacobi-Bellman (HJB). The simulation examples are presented to confirm the effectiveness of the proposed controller method.