Neural Network-Based Adaptive Finite-Time Consensus Tracking Control for Multiple Autonomous Underwater Vehicles

Considering the problem of consensus tracking control for multiple autonomous underwater vehicle (AUV) system, a neural network-based finite-time nonsingular fast terminal sliding mode control method is proposed. First, in order to elaborate on the communication relationship, the algebraic graph theory is combined with a leader-follower architecture. Next, the modified nonsingular fast terminal sliding mode is adopted to improve the fast response characteristic of the system, and distributed control laws are constructed based on the force analysis of each AUV. Furthermore, neural networks technique is employed to approximate the uncertain dynamics and forces caused by the harsh environment of the ocean. Finally, it is proved that the tracking errors can converge to a small neighborhood of the origin by using the proposed algorithm. The effectiveness and robustness of the proposed algorithm are illustrated by a simulation example.