ASAP-MPC: an asynchronous update scheme for online motion planning with nonlinear model predictive control

Abstract

This paper presents a Nonlinear Model Predictive Control (NMPC) update scheme targeted at motion planning for mechatronic motion systems, such as drones and mobile platforms. NMPC-based motion planning typically requires low computation times to be able to provide control inputs at the required rate for system stability, disturbance rejection, and overall performance. To achieve online NMPC updates in complex situations, works in literature typically rely on one of two approaches: attempting to reduce the solution times in NMPC by sacrificing feasibility guarantees, or allowing more time to the motion planning algorithm, which requires additional strategies to ensure robust tracking of the planned motion, e.g., state feedback. Following this second paradigm, this paper presents As-Soon-As-Possible MPC (ASAP-MPC), an asynchronous update scheme for online motion planning with optimal control that abandons the idea of having to satisfy restrictive real-time update rates and that solves the optimal control problem to full convergence. ASAP-MPC combines trajectory generation through optimal control with additional tracking control for improved robustness against disturbances and plant-model mismatch. The scheme seamlessly connects trajectories, resulting from subsequent NMPC solutions, providing a smooth and continuous overall trajectory for the motion system. This framework’s applicability to embedded applications is shown on two different experiment setups where a state-of-the-art method fails to successfully navigate through a given environment: a quadcopter flying through a cluttered environment with hardware-in-the-loop simulation and a scale model truck-trailer manoeuvring in a structured physical lab environment.