Moving Path Following Control of Constrained Underactuated Vehicles: A Nonlinear Model Predictive Control Approach

This paper addresses the design of a stabilizing continuous time sampled-data Nonlinear Model Predictive Control (NMPC) law to solve the Moving Path Following (MPF) motion control problem for constrained under-actuated robotic vehicles. In this scenario, the robotic vehicle is tasked to converge to a desired geometric path, expressed with respect to a moving frameof reference, while satisfying the actuation constraints. This control problem is addressed in the NMPC framework. Specifically, first a suboptimal Lyapunov-based nonlinear auxiliary control law is designed to solve the MPF problem. Then, the latter is used for the design of a suitable terminal set and terminal cost of theMPC controller to enforce closed-loop guarantees. Exploiting the properties of the auxiliary control law, we show that for a suitable selection of the input constraints, the terminal set can be removed, resulting in a global region of attraction of the proposed controller. Simulation results are provided to illustrate the proposed control strategy.