Adaptive course control based on trajectory linearization control for unmanned surface vehicle with unmodeled dynamics and input saturation

This paper presents a novel course control strategy for unmanned surface vehicles (USV) subject to un-modeled dynamics and time-varying external disturbance. A practical adaptive course controller is proposed by trajectory linearization control (TLC) technology, neural network minimum learning parameter method (MLP), disturbance observer (DOB) and auxiliary design system. MLP and DOB are introduced to compensate for unmodeled dynamics and time-varying external disturbance, respectively. In addition, auxiliary design system is hired to deal with input saturation issue. By Lyapunov stability theory, it is proved that all the error signals in the course control system are uniform ultimate bounded. The advantages of the developed control strategy are that first, from the author's point of view, TLC technology is applied to the field of USV motion control for the first time, which opens a new research direction of this algorithm; second, MLP with a smaller computational burden is used to replace radial basis function (RBF) neural network, which is more convenient for engineering implementation; third, the proposed scheme has strong anti-interference ability, which can compensate for the inherent defect that the USV with a smaller volume is susceptible to external disturbance. Finally, numerical simulations prove the effectiveness and correctness of the proposed control strategy. (C) 2018 Published by Elsevier B.V.