Adaptive Output-Feedback Control for Stochastic Mechanical System Based on Neural Network

This paper is concerned with the problem of adaptive output-feedback control for a class of mass-spring mechanical system with stochastic disturbances. The adaptive parameter is only one and the nonlinear terms are successfully handled without growth conditions by using the radial basis function neural networks approximation approach. Based on the adaptive backstepping technique, an adaptive output-feedback controller is constructed. It is proved that the designed controller guarantees the closed-loop system is semi-globally uniformly ultimately bounded (SGUUB). Finally, the simulation results demonstrate the effectiveness of the controller.