Adaptive-Neural Control of Time Delay Nonlinear Systems in the Presence of Actuator Failure

The main purpose of this paper is to present an adaptive-neural controller for strict-feedback nonlinear systems with unknown time delays and in the presence of external disturbances and actuator failure. The proposed adaptive-neural controller is constructed based on DSC design technique. Radial Basis Functions (RBF) networks are utilized to approximate unknown nonlinear functions. Adaptive rules are obtained based on Lyapunov design for updating the parameters of neural networks. Disturbances are unknown functions which their bounds are partially known. Therefore, continuous robust terms are applied in order to minimize their effects. Furthermore, due to the existence of unknown time delays in the system, Lyapunov–Krasovskii functionals are utilized in the process of designing the controller and proofing the stability of the system. In addition, the controller is designed so that it can compensate its effect if the considered actuator failure happens. For the designed controller, the boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighborhood of the origin.