Event-triggered Adaptive Estimated Inverse Neural Network Control of Uncertain Nonlinear Systems With Input Hysteresis Nonlinearity

Abstract

This article investigates an event-triggered adaptive estimated inverse control scheme for a class of uncertain nonlinear systems with hysteresis effects, parametric uncertainties and disturbances. An online estimated inverse hysteresis compensation mechanism is developed, where an adaptive technique is employed to obtain the value of unknown hysteresis parameters. Compared with the common approaches, its biggest advantage lies in that it is not necessary to obtain the hysteresis parameters by means of experiment, which relaxes time-consuming off-line identification work.Moreover, an adaptive radial basis functions neural network (RBFNN) is utilized to approximate the unknown disturbances, whose weight coefficients along with parametric uncertainties are all estimated by the adaptive technique. Besides, the communication cost can be largely saved by introducing the relative threshold event-triggered control (ETC). Through Lyapunov analysis, the proposed controller guarantees the boundedness of all the signals and the convergence of the error signals. The results of numerical simulation illustrate the effectiveness and superiority of the developed controller.