Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

Kai Yang, Xiaolin Tang*, Yechen Qin, Yanjun Huang, Hong Wang, Huayan Pu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)

Abstract

A comparative study of model predictive control (MPC) schemes and robust H state feedback control (RSC) method for trajectory tracking is proposed in this paper. The main objective of this paper is to compare MPC and RSC controllers’ performance in tracking predefined trajectory under different scenarios. MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire, which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode. RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison. Then, three test cases are built in CarSim-Simulink joint platform. Specifically, the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions. Besides, the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability. Furthermore, an extreme curve test is built where the road adhesion changes suddenly, in order to test the performance of both controllers under extreme conditions. Finally, the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

Original languageEnglish
Article number74
JournalChinese Journal of Mechanical Engineering (English Edition)
Volume34
Issue number1
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Automated vehicles
  • Model predictive control
  • Robust H state feedback control
  • Trajectory tracking

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