Adaptive Neural Control for a Class of Stochastic Nonlinear Systems with Input Delay

This paper considers the problem of adaptive neural control for a class of uncertain stochastic nonlinear systems with input delay. By defining the design variable with the input integral term, the input delayed system is converted to the nondelayed systems. And then, an adaptive neural control scheme is presented by combining the neural network approximation, integral mean value theorem and the decoupled backstepping technique. It is shown that the proposed controller can guarantee the 4moment semiglobally uniformly ultimately boundedness for all the signals in the closedloop system. Moreover, the tracking error can converge to a small neighborhood around the origin. A numerical example is given to illustrate the effectiveness of the control scheme.