Adaptive neural control of unknown non-affine nonlinear systems with input deadzone and unknown disturbance

In this paper, an adaptive neural scheme is developed for unknown non-affine nonlinear systems with input deadzone and internal/external unknown disturbance. With the help of mean value theorem and implicit function theorem, the control problem that the system input cannot be expressed in a linear form can be solved. The unknown input deadzone is approximated by neural networks. The immeasurable states are estimated by a high-gain observer such that output feedback control is obtained. The approximation error of both neural networks and the unknown internal/external disturbance is considered as an overall disturbance which is compensated by a novel disturbance observer. Via Lyapunov’s stability theory, it can be proved that all the state signals are uniformly bounded ultimately. The transient response performance can be improved by tuning the control parameters, and the steady-state error converges to any small neighborhood of zero. Simulation examples are carried out to verify the effectiveness of the proposed method.