Robust predefined-time adaptive tracking control for underactuated autonomous underwater vehicles with multiple constraints

This paper addresses the trajectory tracking control problem of underactuated autonomous underwater vehicles (AUVs) subject to model uncertainties, external disturbances, and asymmetric input saturation. A robust predefined-time controller is proposed to ensure rapid convergence of tracking errors within a predefined time, rather than the asymptotic convergence achieved by most existing methods. First, an output redefinition-based dynamic transformation (ORDT) is introduced to overcome the relative degree deficiency caused by underactuation, enabling direct control of sway and heave motions. Subsequently, a predefined-time stable integrated controller is designed, which employs a Gaussian error function to handle asymmetric input saturation and uses a neural network (NN) with minimal learning parameters for disturbance compensation. The convergence time of the proposed controller can be preset by a simple parameter, without dependence on initial conditions or complex tuning. The stability analysis based on the Lyapunov method proves that all closed-loop signals converge to a sufficiently small neighborhood of the origin within a predefined time. Finally, simulation results demonstrate the effectiveness and superiority of the proposed control scheme.