Adaptive neural tracking control for a class of nonlinear systems with input delay and saturation

For a class of non-strict-feedback nonlinear systems with input delay and saturation, the tracking control problem is addressed in this paper. An auxiliary system is constructed to handle the difficulty in control design caused by input delay. Moreover, hyperbolic tangent function is used to approximate the non-smooth saturation function to achieve controller design. The unknown nonlinear functions generated in backstepping control design are approximated by radial basis function neural networks. And then, with the help of backstepping approach, an adaptive neural control scheme is proposed. It is proved by Lyapunov stability theory that the tracking errors converge to a small neighbourhood of the origin and the other closed-loop signals are bounded. At last, a simulation example is able to verify the validity of this tracking control scheme.