Adaptive Neural Tracking Control with Prescribed Performance for Strict-feedback Stochastic Nonlinear Systems

摘要

In this paper, the problem of prescribed performance adaptive neural tracking control design is investigated for strict-feedback stochastic nonlinear systems. The unknown nonlinear functions are tackled using the neural networks. Combining with prescribed performance function and backstepping technique, the desired adaptive controller is developed. It is proved that all the signals of the close-loop system are bounded in probability and the tracking error remains within a predefined arbitrarily small residual set with the prescribed performance bounds. Simulation results are provided to show the effectiveness of the proposed control method.