Composite learning adaptive control for parameter identification of dynamic positioning vessels

In this paper, the tracking problem of dynamic positioning vessels under performance constraints, external disturbances, and unknown hydrodynamic parameters is studied. Aiming at this technical problem, a new composite learning adaptive control scheme is proposed. First, asymmetric integral barrier Lyapunov functions are constructed to impose asymmetric constraints on the output trajectory to ensure the safety of vessel operation. Then, the integral disturbance observer are introduced to eliminate the adverse effects of external disturbances on the system. The disturbance observer is combined with the parameter identification process to achieve accurate parameter identification and perfect trajectory tracking. The method uses dynamic regressor extension and mixing procedures to generate learning items, which not only relaxes the dependence of the parameter estimation convergence process on persistent excitation conditions but also ensures the independence and flexibility of each parameter selection. Finally, simulation results also verify the effectiveness of the algorithm.