Robust Adaptive Neural Control for a Class of Stochastic Nonlinear Systems

In this paper, adaptive neural control is investigated for a class of nonlinear stochastic systems with stochastic disturbances and unknown parameters. Under the condition of all system states being available for feedback, by employing the back stepping method, a suitable stochastic control Lyapunov function is then proposed to construct an adaptive neural network state-feedback controller, and unknown parameters are reasonably disposed. It is shown that, the the closed-loop system can be proved to be global asymptotically stable in probability. The simulation results demonstrate the effectiveness of the proposed control scheme.