Adaptive neural control for uncertain switched nonlinear systems with a switched filter-contained hysteretic quantizer

Abstract

This paper demonstrates an adaptive neural control strategy for the uncertain nonlinear switched systems with dwell time. The unknown nonlinear functions, hysteresis nonlinear and external disturbances are taken into account in this system. A filter-contained hysteretic quantizer is introduced to filter out the high frequency components, leading to a durable solution for the conflict between hysteresis actuator and quantization, that is the hysteresis loop deforms when the input signal goes beyond the boundary. In addition, the adaptive neural network control strategy is developed using backstepping techniques to eliminate external disturbances and unknown nonlinear function. This strategy can guarantee that the tracking error converges to an adjustable field near zero.