Data-driven nonlinear adaptive optimal control of connected vehicles

Weinan Gao, Zhong-Ping Jiang

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

Abstract

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
PublisherSpringer Verlag
Pages122-129
Number of pages8
Volume10639 LNCS
ISBN (Print)9783319701356
DOIs
StatePublished - Jan 1 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

Other

Other24th International Conference on Neural Information Processing, ICONIP 2017
CountryChina
CityGuangzhou
Period11/14/1711/18/17

Fingerprint

Adaptive cruise control
Cooperative Control
Control Policy
Data-driven
Adaptive Control
Optimal Control
Adaptive Dynamics
Global Dynamics
Optimal Policy
Dynamic programming
System Dynamics
Nonlinear Dynamics
Dynamic Programming
Control Problem
Dynamical systems
Stabilization
Unknown
Numerical Simulation
Computer simulation
Knowledge

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Gao, W., & Jiang, Z-P. (2017). Data-driven nonlinear adaptive optimal control of connected vehicles. In Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings (Vol. 10639 LNCS, pp. 122-129). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10639 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-70136-3_13

Data-driven nonlinear adaptive optimal control of connected vehicles. / Gao, Weinan; Jiang, Zhong-Ping.

Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Vol. 10639 LNCS Springer Verlag, 2017. p. 122-129 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10639 LNCS).

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

Gao, W & Jiang, Z-P 2017, Data-driven nonlinear adaptive optimal control of connected vehicles. in Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. vol. 10639 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10639 LNCS, Springer Verlag, pp. 122-129, 24th International Conference on Neural Information Processing, ICONIP 2017, Guangzhou, China, 11/14/17. https://doi.org/10.1007/978-3-319-70136-3_13
Gao W, Jiang Z-P. Data-driven nonlinear adaptive optimal control of connected vehicles. In Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Vol. 10639 LNCS. Springer Verlag. 2017. p. 122-129. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-70136-3_13
Gao, Weinan ; Jiang, Zhong-Ping. / Data-driven nonlinear adaptive optimal control of connected vehicles. Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Vol. 10639 LNCS Springer Verlag, 2017. pp. 122-129 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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