Adaptive output-feedback event-triggered formation control for underactuated ships with obstacle avoidance mechanism

Abstract

This paper focuses on the adaptive output-feedback event-triggered control strategy for the formation tracking and obstacle avoidance activity of unmanned surface vessels (USVs) with unmeasurable velocity, constrained communication and actuator fault. By using the neural networks and minimal learning parameter, a novel output-feedback event-triggered controller is deigned, effectively alleviates constraints related to continuous feedback signal acquisition in dynamic systems. Subsequently, by incorporating an adjustable threshold, the event-triggered condition is established, thus facilitating updates to the state and adaptive law solely within the discrete-time domain. Then, only two aperiodic adaptive laws are derived to mitigate actuator faults and system uncertainties. As for the path planning, benefiting from the artificial potential field method, a novel obstacle avoidance mechanism is developed for obstacle avoidance and waypoint-based path navigation. Considerable effort has been made to guarantee the semi-globally uniformly ultimately bounded stability. Finally, two numerical simulations are illustrated to demonstrate the remarkable performance of the proposed control strategy.