Path following of dual propelled unmanned surface vehicle based on lie group model predictive control

A model predictive control (MPC) approach rooted in Lie group description is put forward to address the trajectory tracking problem of dual propelled unmanned surface vehicle (USV). The motion states of the dual propelled USV are defined in the Lie group space, and the geometric characteristics of this group are employed to build a linear model of error dynamics. A Lie group MPC approach is developed rooted in the first-order approximation of the mathematical model of the dual propelled USV. To verify the reliability of the controller, simulations are conducted where the USV is required to track a circular reference trajectory and a global path planned by combining rapidly-exploring random trees star (RRT*) with artificial potential fields. The results show that compared with nonlinear model predictive control (NMPC), the proposed controller significantly reduces computation time while maintaining comparable tracking performance. The controller can achieve high-precision trajectory tracking under ocean current disturbances.