A new trajectory tracking algorithm for autonomous vehicles based on model predictive control

Zhejun Huang, Huiyun Li (Corresponding Author), Wenfei Li, Jia Liu, Chao Huang, Zhiheng Yang, Wenqi Fang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


Trajectory tracking is a key technology for precisely controlling autonomous vehicles. In this paper, we propose a trajectory-tracking method based on model predictive control. Instead of using the forward Euler integration method, the backward Euler integration method is used to es-tablish the predictive model. To meet the real-time requirement, a constraint is imposed on the control law and the warm-start technique is employed. The MPC-based controller is proved to be stable. The simulation results demonstrate that, at the cost of no or a little increase in computational time, the tracking performance of the controller is much better than that of controllers using the forward Euler method. The maximum lateral errors are reduced by 69.09%, 47.89% and 78.66%. The real-time performance of the MPC controller is good. The calculation time is below 0.0203 s, which is shorter than the control period.

Original languageEnglish
Article number7165
Issue number21
Publication statusPublished - 1 Nov 2021


  • Autonomous driving
  • Model predictive control
  • Real-time control
  • Trajectory tracking

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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