Adaptive anti-disturbance control for a class of unknown pure feedback switched nonlinear systems

Abstract

In this study, an adaptive anti-disturbance control scheme is investigated for a class of unknown pure feedback switched nonlinear systems subjected to immeasurable states and external disturbances. Radial basis function neural networks (RBFNNs) are employed to identify the switched unknown nonlinearities, and a Butterworth low-pass filter is adopted to remove the algebraic loop problem. Subsequently, a novel switched neural state observer and a novel switched disturbance are presented via the coupled design method to estimate the immeasurable states and compounded disturbances. Then, an improved adaptive control strategy for the studied problem is designed with the help of a filtering method to eliminate the “explosion of complexity” problem, and certain compensating signals are set up to compensate for the filter errors, where switched updated laws are constructed to lessen the conservativeness caused by adoption of a common updated law for all subsystems. By utilizing the Lyapunov stability theorem, the developed control scheme can guarantee that all signals in the closed-loop system are bounded under a class of switching signals with the average dwell time (ADT), while the tracking error can converge to a small neighbourhood of origin. Finally, simulation results are provided to demonstrate the effectiveness of the presented approach.