Adaptive neural optimal control of uncertain nonlinear systems with output constraints

Abstract

In this paper, adaptive optimal control is proposed for a class of strict-feedback nonlinear systems with unmodeled dynamics and output constraints. The controller design procedure contains two parts. In the first part, to satisfy output constraints, nonlinear mapping is used to transform constrained system into a novel one without output constraints. A dynamical signal is utilized to deal with unmodeled dynamics. A feedforward controller is designed using the dynamics surface control technique. In the second part, an auxiliary dynamical system is introduced to optimize cost function, and neural-network based adaptive dynamic programming(ADP) is employed to approximate the optimal cost function and the optimal control law. It is proved that the closed-loop system is semi-globally uniformly ultimately bounded (SGUUB) and the output constraints are not triggered by theoretical analysis. Two simulation examples are provided to illustrate the effectiveness of the proposed scheme.