Event-triggered formation control of AUVs with fixed-time RBF disturbance observer

Aiming at the problem of model parameter uncertainty, unknown current disturbance and actuator input saturation of multi-AUVs formation control, an event-triggered formation strategy based on fixed-time Radial Basis Function(RBF) neural network adaptive disturbance observer is proposed, which can ensure formation control converge in fixed time. First of all, fixed time RBF disturbance observer (FRBFDO) is put forward to estimate lumped disturbance accurately. Based on the FRBFDO, with the combination of command filter and the back-stepping method, which is used to eliminate repeated derivation calculation explosion problem; Secondly, in order to save the energy consumption of network transmission resources, the event trigger mechanism is introduced into the multi-AUVs formation control. The fixed-time distributed formation controller is designed to realize the fixed-time stability of formation system, and the system convergence time is independent of the initial state. Finally, the effectiveness and rationality of the proposed algorithm are proved by the multi-AUVs formation simulation experiment.

Automated summary

An event-triggered formation strategy based on fixed-time RBF neural network adaptive disturbance observer is proposed to address the issues of model parameter uncertainty, unknown current disturbance, and actuator input saturation in multi-AUVs formation control. By introducing the event trigger mechanism and designing a fixed-time distributed formation controller, the system achieves fixed-time stability independent of initial state, while saving energy consumption of network transmission resources. The effectiveness and rationality of the proposed algorithm are demonstrated through multi-AUVs formation simulation experiments.