Robust adaptive control for surface-underwater vehicles: A case of the cable-laying path following via an ALOS guidance

Abstract

This paper develops a robust adaptive control framework for the cable-laying operation of the surface-underwater vehicles cooperative system that uses an adaptive line-of-sight (ALOS) guidance and minimum learning parameter (MLP) mechanism. In particular, the ALOS guidance principle can estimate the varying sideslip angle caused by the external unmeasured disturbances. The developed guidance principle explicitly accounts for ocean current effects and uneven seabed topography to meet practical underwater cable-laying demands. For the control part, a robust neural damping technique is designed to address strong state couplings and underactuated constraints. A key advantage of this controller is its requirement for only two adaptive parameters to be updated online, significantly reducing computational load. Substantial theoretical effort ensures that all closed-loop system signals are semi-globally uniformly ultimately bounded (SGUUB). Simulation results validate the effectiveness and robustness of the proposed algorithm, demonstrating its performance specifically under simulated marine disturbances and uneven seabed conditions.