An improved adaptive neural dynamic surface control for pure-feedback systems with full state constraints and disturbance

摘要

This paper discusses a neural dynamic surface control (DSC) problem for a class of nonlinear systems in the case of full-state constraints and unknown disturbances. Incorporating an improved DSC method and the neural networks (NNs) approximation with the minimization parameter method, an improved neural DSC approach is constructed for the studied system. By introducing a novel barrier Lyapunov function (BLF) in the design steps, the issue of full-state constraints existing in the system can be solved. The highlighting features of the proposed control method are that only one online estimation parameter should be updated, and the same stability property as the conventional backstepping method can be reserved. The transgressions of full state constraints never occur in the case of disturbances. By the Lyapunov stability analysis, all the signals of the closed-loop system are ultimately bounded. Finally, two simulation examples display the effectiveness of the proposed approach.