Adaptive Trajectory Tracking Control for Underactuated Unmanned Surface Vehicle Subject to Unknown Dynamics and Time-Varing Disturbances

Abstract

This paper proposes an adaptive trajectory tracking control strategy for underactuated unmanned surface vehicles subject to unknown dynamics and time-varing external disturbances. In short, the goal of this paper is to provide a control strategy that allows an underactuated unmanned surface vehicle to track a time dependent trajectory. First, a first-order sliding surface is introduced into the design of surge control law to converge to surge tracking error, and then a second-order sliding surface is hired to design yaw control law to deal with sway motion tracking error. Meanwhile, neural network minimum learning parameter method, which has a smaller amount of computation than a multilayer neural network, is employed to preserve the control law robustness against unknown dynamics and time-varing disturbances induced by wind, waves and ocean currents. Furthermore, much effort is made to obtain uniform ultimate bounded stability for the closed-loop control system. Finally, the numerical simulation experiments of straight line and circle trajectory tracking have been given to prove the correctness and feasibility of the proposed control strategy.

Adaptive Sliding Mode Trajectory Tracking Control for Unmanned Surface Vehicle with Modeling Uncertainties and Input Saturation

“…In [11], when |x e | and |y e | are relatively large, the virtual control laws can exceed the velocity range of USV, and they can lead to system instability or controller crash. However, the designed virtual control laws of this paper can avoid the above problem.…”

“…In order to overcome this difficulty, an adaptive tracking controller based on backstepping and neural network is developed in [10], in which neural network is hired to handle the unknown dynamics. Based on the sliding mode control and the minimum learning parameter (MLP) technique, an adaptive tracking control scheme is proposed in [11], where dynamical uncertainty and time-varying ocean disturbances are centrally compensated by employing neural network MLP. In addition, for the trajectory tracking problem of the underactuated USV, the biggest challenge is that the number of actuators is less than the degree of freedom, which increases the design difficulty of the controller.…”

Modeling and Identification for Vector Propulsion of an Unmanned Surface Vehicle: Three Degrees of Freedom Model and Response Model

“…Its ultimate purpose is to improve the level of ship automation and intelligence, and to ensure the safety, economy and comfort of a ship’s navigation [ 1 , 2 , 3 ]. Meanwhile, with the continuous development of technology and the extension of the field of ship application, the model of a traditional ship is developing towards miniaturization, speediness and intellectualization, and a new surface carrier has been developed—Unmanned Surface Vehicle (USV) [ 4 , 5 , 6 , 7 ]. To allow better maneuverability, the propulsion system of a USV has higher requirements.…”

Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

“…Wang et al [12] proposed the use of a radial basis function (RBF) network for control input compensation and designed an intelligent tracking control algorithm for USVs based on SMC under limited input conditions. Dongdong et al [13] proposed an adaptive trajectory tracking control strategy for underactuated unmanned surface vehicles. The neural network minimum learning parameter method proposed in this paper features a small amount of computation.…”