Distributed fixed-time dynamic event-triggered leaderless formation control for multiple AUVs based on FRBFDO

In this paper, the problem of fixed-time dynamic event-triggered formation control is studied for multi-AUV systems with system uncertainties, and a fixed-time leaderless formation control scheme based on dynamic event-triggered strategy is proposed. Firstly, a fixed-time neural network disturbance observer (FRBFDO) with automating structuring neural network (ASNN) is designed to deal with the lumped uncertainty of the system, the lumped uncertainty of the system is estimated precisely within a fixed time, which improves the estimation accuracy and convergence rate of the observer. And the ASNN can constantly adjust the number of neurons, eventually obtaining an optimal number. Then, to reduce the inter-AUV information interaction and thus save communication resources, a fixed-time leaderless formation control method based on the static event-triggered mechanism is proposed, which enables the AUV formation system state to actually converge in a fixed time, and no Zeno behavior occurs through theoretical proof. Furthermore, a dynamic event-triggered mechanism is designed to further reduce the number of event triggers while maintaining the performance of the formation controller, and a control strategy of leaderless formation with fixed-time convergence is proposed based on the dynamic event-triggered mechanism, and it is demonstrated theoretically that no Zeno behavior occurs. Finally, the effectiveness of the two proposed formation control strategies is verified through comparative simulations.