Development of optimal control system for safe distance of platooning using model predictive control

Xin Zhao, Dongmei Wu, Yichun Yeh, Harutoshi Ogai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Platooning technology is becoming a future task which suggests as a way of reducing carbon dioxide emissions and realizing safe driving at a high velocity. This paper presents a unique optimal control method of velocity and distance for platooning using model predictive control. The vehicle-platoon's distance model which is based on the road condition and weather condition is used in this rigorous approach of deriving the control input. A combination of Continuation and Generalized Minimum Residual Methods is used to optimize the sequence of vehicle control commands which is required in the prediction horizon aiming at minimizing the relative velocity and keeping safe distance of the vehicle-platoon while the vehicle-platoon is on a high velocity driving.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages65-74
Number of pages10
Volume6457 LNCS
DOIs
Publication statusPublished - 2010
Event8th International Conference on Simulated Evolution and Learning, SEAL 2010 - Kanpur
Duration: 2010 Dec 12010 Dec 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6457 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th International Conference on Simulated Evolution and Learning, SEAL 2010
CityKanpur
Period10/12/110/12/4

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Keywords

  • model predictive control
  • Platooning
  • safe distance

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Zhao, X., Wu, D., Yeh, Y., & Ogai, H. (2010). Development of optimal control system for safe distance of platooning using model predictive control. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6457 LNCS, pp. 65-74). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6457 LNCS). https://doi.org/10.1007/978-3-642-17298-4_6