Preset-Trajectory-Based Adaptive Neural Autopliot Control for Uncertain Surface Vehicles via Dynamic Event-Triggered Mechanism

Abstract

This paper investigates the tracking control problem of autopliot for maritime autonomous surface ships (MASSs) in the presence of the uncertain dynamics and external disturbances, and proposes a dynamic event triggered adaptive mechanism based on a preset-trajectory function. In this paper, to handle the problem of lumped uncertainties, neural netwok and adaptive control technique are employed. A preset-trajectory function is designed, which can guarantee the autopliot error arrive the pre-set accuracy. A dynamic event triggering protocol is established, to reduce the acting frequency of actuators, and decrease the mechanical wear of the MASS actuators. Finally, a new dynamic event-triggered control mechanism is proposed, integrating adaptive neural control and a preset-trajectory function. Based on Lyapunov control theory, all signals in the autopliot control system are proven to be bounded. The numerical simulation results sufficiently demonstrate the effectiveness of the control strategy.