Neural adaptive performance guaranteed formation control for USVs with event-triggered quantized inputs

This study presents an adaptive fault-tolerant performance guaranteed formation control strategy with event-triggered quantised inputs (EDQI) for unmanned surface vessels (USVs) in the presence of model uncertainties and actuator faults. Firstly, a prescribed performance formation guidance control law is proposed to allow formation tracking errors to evolve within the performance envelope and convergence to prescribed steady regions at an appointed time. Then, minimal-parameter-learning-based neural networks (MLP-NN) are used to tackle model uncertainties. To reduce the frequency of actuator updates and the communication burden on the channel, a dynamic memory event-triggered quantised input strategy is investigated. In addition, adaptive estimators and auxiliary design signals are built to compensate for the effects caused by input quantisation and actuator faults. It is proved that all closed-loop signals are bounded and formation errors can converge to the prescribed region at an appointed time. Finally, the effectiveness of the proposed controllers is verified by numerical simulations.