Robust neural event-triggered control for dynamic positioning ships with actuator faults

Abstract

This paper presents a novel robust neural event-triggered control algorithm to achieve the dynamic positioning operation of marine surface ships in the presence of actuator faults. In the algorithm, the model uncertainty and the “explosion of complexity” are addressed by fusion of the robust neural damping and dynamic surface control techniques. The gain related adaptive law is constructed to compensate the gain uncertainties and the unknown actuator faults, which improved the stability of the ship dynamic loop system. Furthermore, the event-triggered mechanism is introduced to reduce the communication load between the controller and actuator. For merits of the aforementioned design, the proposed algorithm is with the advantages of concise form and easy to be implemented in practical ocean engineering. Based on the Lyapunov theory, rigorous analysis is proved to guarantee the semi-global uniform ultimate boundedness (SGUUB) of the closed-loop system. Finally, numerical simulations are conducted to demonstrate the validity and feasibility of the proposed algorithm.