Single-Parameter-Learning-based Tracking Control for Surface Vessels with Thruster Saturation and Position Constraints

Consider surface vessels (SVs) systems subject to model uncertainties and external disturbances, a single-parameter-learning-based adaptive tracking practical finite-time control (PFTC) algorithm is proposed. First, an auxiliary dynamic system (ADS) is applied to handle the thruster saturation, and position constraints are handled by constructing a Barrier Lyapunov function (BLF). Then, the total uncertain term containing model uncertainties, external disturbances are formulated by a linear parameterized form with a single parameter. Moreover, by employing the adaptive vector-backstepping method and the differentiable power function, the singularity problem of controller is avoided. In the framework of Lyapunov stability analysis, it is proved that the trajectory tracking error converges to a bounded neighbourhood and actual positions and heading of SVs are guaranteed to lie in the constrained region. Finally, simulations results illustrate the effectiveness of the proposed control algorithm.