An Online SMC-Based Adaptive Wavelet-Neural Controller for Uncertain Nonlinear Systems with only Output Measurement

Abstract

This paper addresses the SMC-based adaptive wavelet-neural tracking control problem for a class of uncertain nonlinear systems. The adaptive wavelet-neural controller is designed under constrain that not all atate variables are available for measurement. A state observer is used to estimate unmeasured states of the systems. The global asymptotic stability of the closed-loop system is guaranteed according to the Lyapunov stability criterion. The simulation presented in the inverted pendulum system control indicates that the proposed approach is capable of achieving a good trajectory following performance without the knowledge of plant parameters.