Spontaneous Cooperative Fencing Control of Discrete-Time Multiple Autonomous Underwater Vehicles with Unknown Target Velocity and Unknown Dynamics

In this paper, we propose a cooperative fencing controller for multiple autonomous underwater vehicles (AUVs) subjected to unmodeled dynamics and external disturbances so that they can form a convex hull spontaneously to capture a moving target with unknown velocity in the discrete-time domain. The controller is comprised of three distinct components. The first component is an attraction input, which drives the AUVs to track the target by leveraging the difference in position and velocity data. The second component is a repulsion input, which maintains a fixed distance between the AUVs. The third component is a nonlinear compensation input. To compensate for the missing target velocity information, the radial basis function neural networks (RBFNNs) are utilized to construct an estimator to obtain the velocity approximations. Also, RBFNNs are used to compensate for the unmodeled dynamics and external disturbances. Then, the error convergence behavior of the closed-loop system is analyzed. Finally, numerical simulations and comparisons are presented to verify the effectiveness and superiority of the proposed controller.