Adaptive Neural Control of Uncertain Nonlinear Systems With Input Hysteresis Modeled By Preisach Operator

In this paper, we propose a new and novel adaptive neural control scheme for uncertain unparametrizable nonlinear systems with actuator hysteresis modeled by Preisach operator. Since the hysteresis operator couples with uncertain nonlinear dynamics, identifying the hysteresis parameter (or density function) is rather difficult so that the traditional idea of constructing an inverse model of the Preisach operator to compensate the hysteresis effect may not be feasible. To overcome this problem, we used the factorization of the Preisach operator and successfully fused it with our backstepping recursive design, based on which an adaptive neural control scheme was developed to ensure that all signals of the closed-loop system are uniformly ultimately bounded and the tracking error goes into the adjustable neighborhood of the origin. The results are also verified by simulation study.