Adaptive Control of Stochastic Nonstrict-Feedback Nonlinear Systems with Unknown Hysteresis

Abstract:
For stochastic nonlinear systems with unknown hysteresis, an adaptive tracking control based on neural networks is proposed. Under backstepping framework, by employing radial basis function neural networks to approximate unknown nonlinear function, an adaptive control law is designed. It is proved that the controller can ensure that the signal of the system remain bounded, and the system output converges to within a small neighborhood of the desired signal. Finally, an experiment result show the feasibility of the control strategy.