Neural network-based target tracking control of underactuated autonomous underwater vehicles with a prescribed performance

Abstract

In this paper, the target tracking control problem is addressed for underactuated autonomous underwater vehicles (AUV) with a prescribed performance. For this purpose, the range and bearing angles of the AUV relative to an underwater target are transformed to a second-order open-loop error dynamic model by using the prescribed performance bound technique. Then, a new tracking controller is proposed such that the tracking errors converge to an arbitrary small ultimate bound and their transient performance are guaranteed with a pre-specified maximum overshoot and the convergence rate. To overcome unmodeled dynamics and external disturbances that are imposed on the vehicle by the wind, waves, and ocean currents, a multi-layer neural network and an adaptive robust controller are adopted. A Lyapunov stability synthesis shows that all signals of the control system are bounded, and tracking errors converge to a small region containing the origin with a prescribed performance. Finally, simulations are performed in MATLAB software and a comparative study verifies the theoretical results.