Nussbaum-type function based robust neural event-triggered control of unmanned surface vehicle subject to cyber and physical attacks

Abstract

This paper mainly focuses on the path-following control of the unmanned surface vehicle (USV) with the consideration of cyber and physical attacks. The actuator of USV has fully unknown dynamics under deception attack, and the USV system consists of complex nonlinear perturbations caused by physical attack. Therefore, a Nussbaum-type function based robust neural event-triggered path-following control scheme is proposed by combining event-triggered technique with Nussbaum approach. Together with hybrid disturbances and model uncertainties, the lumped nonlinearity is online approximated by employing the robust neural damping technique. Within the backstepping framework, a Nussbaum gain function is further designed to tackle unknown control directions in the actuator system. To facilitate the implementation of the proposed algorithm, an input-based event-triggered mechanism is introduced to reduce the channels clogging and the resources wasting between controllers and actuators. Theoretical analysis reveals that all signals of the closed-loop tracking system are bounded and tracking errors can converge to an arbitrarily small neighborhood of zero. Numerical simulation is performed by employing a class of underactuated surface cable-laying vehicle. The corresponding results demonstrate the effectiveness and superiority of the proposed approach.