Model predictive path tracking control for automated road vehicles: A review

Abstract

Thanks to their road safety potential, automated vehicles are rapidly becoming a reality. In the last decade, automated driving has been the focus of intensive automotive engineering research, with the support of industry and governmental organisations. In automated driving systems, the path tracking layer defines the actuator commands to follow the reference path and speed profile. Model predictive control (MPC) is widely used for trajectory tracking because of its capability of managing multi-variable problems, and systematically considering constraints on states and control actions, as well as accounting for the expected future behaviour of the system. Despite the very large number of publications of the last few years, the literature lacks a comprehensive and updated survey on MPC for path tracking. To cover the gap, this literature review deals with the research conducted from 2015 until 2021 on model predictive path tracking control. Firstly, the survey highlights the significance of MPC in the recent path tracking control literature, with respect to alternative control structures. After classifying the different typologies of MPC for path tracking control, the adopted prediction models are critically analysed, together with typical optimal control problem formulations. This is followed by a summary of the most relevant results, which provides practical design indications, e.g., in terms of selection of prediction and control horizons. Finally, the most recent development trends are analysed, together with likely areas of further investigations, and the main conclusions are drawn.