Data-driven nonlinear adaptive optimal control of connected vehicles

Weinan Gao, Zhong Ping Jiang

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


This paper studies the cooperative adaptive cruise control (CACC) problem of connected vehicles with unknown nonlinear dynamics. Different from the existing literature on CACC, a data-driven optimal control policy is developed by global adaptive dynamic programming (GADP). Interestingly, the developed control policy achieves global stabilization of the nonlinear vehicular platoon system in the absence of the a priori knowledge of system dynamics. Numerical simulation results are presented to validate the effectiveness of the developed approach.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
EditorsDerong Liu, Shengli Xie, Dongbin Zhao, Yuanqing Li, El-Sayed M. El-Alfy
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319701356
StatePublished - 2017
Event24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
Duration: Nov 14 2017Nov 18 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10639 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other24th International Conference on Neural Information Processing, ICONIP 2017


  • Connected and autonomous vehicles
  • Cooperative adaptive cruise control
  • Global adaptive dynamic programming
  • Nonlinear optimal control

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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