Three Dimensional Moving Path Following Control for Robotic Vehicles With Minimum Positive Forward Speed

This letter addresses the problem of steering a robotic vehicle along a geometric path specified with respect to a reference frame moving in three dimensions, termed the Moving Path Following (MPF) motion control problem. The MPF problem is solved for a large class of robotic vehicles that require a minimum positive forward speed to operate. This restriction poses additional challenges with respect to the control design, and is developed using geometric concepts, wherein the attitude control problem is formulated on the Special Orthogonal group SO(3). To this end, a MPF error model formulation is first derived that allows to exclude the conservative constraints on the initial position of the vehicle with respect to the reference path by enabling the explicit control of the progression of a virtual point moving along the reference path. The task of the MPF control law is then to steer the vehicle towards the moving path and to converge to the virtual point. Stability and convergence guarantees of the resulting closed-loop system are provided using the Input-to-State Stability concept. In particular, we show that the proposed controller is robust to imperfect tracking errors and external disturbances (e.g., autopilot errors and wind gusts). Simulation results are presented to illustrate the efficacy of the proposed MPF control law.

Automated summary

This letter addresses the problem of steering a robotic vehicle along a geometric path specified with respect to a reference frame moving in three dimensions, termed the Moving Path Following (MPF) motion control problem. The MPF problem is solved for a large class of robotic vehicles that require a minimum positive forward speed to operate. This restriction poses additional challenges with respect to the control design, and is developed using geometric concepts, wherein the attitude control problem is formulated on the Special Orthogonal group SO(3). To this end, a MPF error model formulation is first derived that allows to exclude the conservative constraints on the initial position of the vehicle with respect to the reference path by enabling the explicit control of the progression of a virtual point moving along the reference path. The task of the MPF control law is then to steer the vehicle towards the moving path and to converge to the virtual point. Stability and convergence guarantees of the resulting closed-loop system are provided using the Input-to-State Stability concept. In particular, we show that the proposed controller is robust to imperfect tracking errors and external disturbances (e.g., autopilot errors and wind gusts). Simulation results are presented to illustrate the efficacy of the proposed MPF control law.