Design, Analysis, and Experiments of Preview Path Tracking Control for Autonomous Vehicles

• Published in: IEEE transactions on intelligent transportation systems Vol. 21; no. 1; pp. 48 - 58
• Main Authors: Xu, Shaobing, Peng, Huei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automation; Autonomous vehicles; Computational modeling; Computer simulation; Control algorithms; Control systems design; Control theory; Feedback control; Feedforward control; Frequency-domain analysis; Heuristic algorithms; Hybrid vehicles; Optimal control; Optimization; Path tracking; Predictive control; Preview control; Roads; Smoothness; State feedback; State vectors; Steering; Systems analysis; Tracking control; Tracking errors; Trajectory; Vehicle dynamics; vehicle dynamics control; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2892926

This paper presents a preview steering control algorithm and its closed-loop system analysis and experimental validation for accurate, smooth, and computationally inexpensive path tracking of automated vehicles. The path tracking issue is formulated as an optimal control problem with dynamic disturbance, i.e., the future road curvature. A discrete-time preview controller is then designed on the top of a linear augmented error system, in which the disturbances within a finite preview window are augmented as part of the state vector. The obtained optimal steering control law is in an analytic form and consists of two parts: 1) a feedback control responding to tracking errors and 2) a feedforward control dealing with the future road curvatures. The designed control's nature, capacity, computation load, and underlying mechanism are revealed by the analysis of system responses in the time domain and the frequency domain, theoretical steady-state error, and comparison with the model predictive control (MPC). The algorithm was implemented on an automated vehicle platform, a hybrid Lincoln MKZ. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy, steering smoothness, and computational efficiency compared to the MPC and the full-state feedback control.