Output-Feedback Based Event-Triggered Formation Control for USVs with Obstacle Avoidance Mechanism

Abstract This paper focuses on the output-feedback based event-triggered control strategy for the formation tracking and obstacle avoidance activity of unmanned surface vehicles (USVs) with unmeasurable velocity, constrained communication and actuator fault. To be specific, an event-triggered based state observer is designed by using the neural networks (NNs) and minimal learning parameter (MLP) technique, which releases the constraint of continuous feedback signal acquisition and reduces the communication burden from sensor to controller channel. Using the designed observer, an adaptive triggering condition with adjustable threshold is derived, so that the triggered state and the adaptive law are updated only in the discrete-time domain. Furthermore, by virtue of the artificial potential field (APF) method, a novel obstacle avoidance mechanism is developed for USVs formation to achieve obstacle avoidance and waypoints-based path navigation performance. Based on above design, the output-feedback based event-triggered controller is derived and only two aperiodic adaptive laws are designed to compensate the actuator fault and system uncertainties. Considerable effort has been made to guarantee the semi-globally uniformly ultimately bounded (SGUUB) stability. Finally, two numerical simulations are illustrated to demonstrate the remarkable performance of the proposed control strategy.