Cooperative learning formation control of multiple autonomous underwater vehicles with prescribed performance based on position estimation

This paper proposes a cooperative learning formation control method with finite-time prescribed performance based on position estimation for parametric path tracking of multiple autonomous underwater vehicles (AUVs) with uncertainties and external disturbances. The parametric path used in the control law permits the velocity to be specified independently while tracking the path accurately. The localized radial basis function neural net-works learn the uncertainties cooperatively while tracking the period path, and the knowledge gained from learning is utilized to construct an empirically based formation control law using experience to cope with similar uncertainties rather than repeatedly using adaptive methods, which reduces the computing burden. The position of the leader is assumed to be available only for the leader's neighboring AUV, and a novel finite-time distributed observer is presented for the followers to estimate the leader's position. Based on this, the control law is derived from the prescribed performance control method using a finite-time performance function rather than expo-nential decaying function to enable the tracking error converges in finite time, which accelerates the learning process. The simulation results confirm the validity of the presented control protocol.

Automated summary

This paper proposes a cooperative learning formation control method for parametric path tracking of multiple AUVs in the presence of uncertainties and external disturbances. The control law allows independent velocity specification while accurately tracking the path. The use of localized radial basis function neural networks facilitates cooperative learning of uncertainties and construction of an empirically based formation control law. A novel finite-time distributed observer is presented for estimating the leader's position, and a finite-time performance function is used to accelerate the learning process. Simulation results confirm the effectiveness of the proposed control protocol.