Adaptive Neural Tracking for a Class of SISO Uncertain and Stochastic Nonlinear Systems

Adaptive neural control schemes based on the backstepping technique are developed to solve the tracking control problem of a combined stochastic and uncertain nonlinear system. As shown by an extensive stability analysis the proposed control scheme ensures that all the error variables are bounded in probability while the mean square tracking error becomes semiglobally uniformly ultimately bounded in an arbitrarily small area around the origin. The effectiveness of the design approach is illustrated by simulation results.