Finite-time composite learning control for trajectory tracking of dynamic positioning vessels

Abstract

This paper studies the trajectory tracking control method for dynamic positioning vessels with state constraints and parameter uncertainties. A composite learning method is introduced to identify unknown parameters online. Regressor filtering together with dynamic regressor extension and mixing procedure are combined not only to relax the dependence of parameter estimation convergence process on the persistent excitation condition, but also to ensure the independence and flexibility of each parameter. Subsequently, a finite-time composite learning controller is designed based on asymmetric integral barrier Lyapunov functions, which guarantees the asymmetric constraints on vessel states. Furthermore, Lyapunov stability shows that the parameter identification and tracking errors can converge to zero in finite-time. Finally, tracking control task for dynamic positioning systems is carried out to illustrate the merits of the proposed method.