Finite-Time Observer-Based Adaptive Control of Switched System with Unknown Backlash-Like Hysteresis

Abstract

This work investigates a finite-time observer problem for a class of uncertain switched nonlinear systems in strict-feedback form, preceded by unknown hysteresis. By using a finite-time performance function, a finite-time switched state observer (FTSO) is derived using radial basis function neural networks (RBFNNs) to estimate the unmeasured states. An adaptive feedback neural network tracking control is derived based on the backstepping technique, which guarantees that all the signals of the closed-loop system are bounded, the output tracking error converges to zero, and the observer error converges to a prescribed arbitrarily small region within a finite-time interval. In addition, two simulation studies and an experiment test are provided to verify the feasibility and effectiveness of the theoretical finding in this study.