Finite-time Containment Control for Autonomous Underwater Vehicles with Prescribed Performance

This paper proposes a finite-time prescribed performance containment control method for multiple autonomous underwater vehicles (AUVs) with uncertainty and disturbance. The control law is designed based on the conversion error derived from the prescribed performance control (PPC) framework. A new finite-time performance function, instead of exponential decay function, is used for error transformation, which enables the containment error converges in a finite time. The model uncertainty is approximated using the radial basis function neural networks (RBFNN), and the external disturbance is compensated with the unknow boundary being estimated using the adaptive approach. The simulation results confirm the validity of the proposed control protocol.