Analysis of explicit model predictive control for path-following control

In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration.

• Record URL:
  • https://doi.org/10.1371/journal.pone.0194110
• Record URL:
  • http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0194110
• Supplemental Notes:
  • © 2018 Junho Lee and Hyuk-Jun Chang.
• Authors:
  • Lee, Junho
  • Chang, Hyuk-Jun
• Publication Date: 2018

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Photos; References;
• Pagination: e0194110
• Serial:
  • PLoS One
  • Volume: 13
  • Issue Number: 3
  • Publisher: Public Library of Science
  • EISSN: 1932-6203
  • Serial URL: https://journals.plos.org/plosone/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Computer programs; Lane changing; Process control; Simulation
• Uncontrolled Terms: Lane keeping; Model predictive control; Quadratic programming
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01677010
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 30 2018 8:34AM