The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

الملخص EN

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems.

By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed.

The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online.

One example is given to show the effectiveness of the proposed control method.