Neural network adaptive output regulation for non-linear uncertain systems with full-state constraints

Abstract

Abstract In this paper, we study the output regulation problem (ORP) of non-linear systems with full-state constraints (FSC). First, in order to deal with the ORP of non-linear systems with FSC, a radical constraint function is proposed to avoid the drawbacks of the barrier Lyapunov functions (BLF) and the logarithmic constraint functions. Then, a control algorithm is proposed based on neural network control method without using the backstepping method, so that all signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converge to a small neighbourhood of the origin, and all states of the system can be constrained. Finally, a simulation example illustrates its effectiveness.