Trajectory‐tracking control of an unmanned surface vehicle based on characteristic modelling approach: Implementation and field testing

Abstract

In this study, a practical adaptive control scheme is proposed for the trajectory tracking of an unmanned surface vehicle via the characteristic modelling approach. Therefore, accurate tracking control can be achieved in the presence of unknown time-varying model parameters and environmental disturbances. The control scheme comprises a trajectory guidance module based on the virtual target approach and a tracking control module designed by characteristic modelling theory. Firstly, the ideal control commands of the yaw speed and surge speed are generated using the position errors between the vehicle and the virtual target. Then, a second-order characteristic model for the heading and surge speed channel is developed. The parameters of the model are updated by a real-time parameter identification algorithm. Based on this model, an integrated adaptive control law is designed which consists of golden-section control, feed-forward control and integral control. Finally, the development processes of the vehicle platform and the control algorithms are described, and the results of simulation and field experiments are presented and discussed.

Trajectory Following Control of an Unmanned Vehicle for Marine Environment Sensing

“…To this end, researchers have employed various control strategies. One of the most widely used controllers is the proportional, integral, and derivative (PID) controller [ 13 , 14 , 15 , 16 ], but it lacks robustness and is highly dependent on the accuracy of the system dynamics [ 17 ]. Model predictive control (MPC) was applied in [ 18 , 19 ], which is sufficient for handling system constraints; but it is computationally intensive when the prediction horizon increases, making practical implementation difficult [ 12 ].…”