Adaptive neural control of nonlinear systems with hysteresis

Abstract

Control of nonlinear systems preceded by unknown hysteresis nonlinearities has received increasing attention in recent years with growing industrial demands involving varied applications. The most common approach is to construct an inverse operator, which, however, has its limits due to the complexity of the hysteresis characteristics. The main focus of the thesis is to explore new avenues to fuse several different hysteresis models with the available control techniques to achieve the stable output tracking performance without constructing a hysteresis inverse. By investigating the characteristics of these hysteresis models, neural network (NN) based control approaches fused with these hysteresis models are developed for the concerned uncertain nonlinear systems, in the presence of unmodelled dynamics and uncertain hysteresis models. The results presented in this thesis can be considered as a stepping stone to be used toward the development of a general control framework for the systems with hysteretic behavior. Analytical and simulation results are presented to show the effectiveness of the proposed schemes.