Data-driven nonlinear adaptive optimal control of connected vehicles

Weinan Gao; Zhong Ping Jiang

doi:10.1007/978-3-319-70136-3_13

Data-driven nonlinear adaptive optimal control of connected vehicles

Weinan Gao, Zhong Ping Jiang

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
Editors	Derong Liu, Shengli Xie, Dongbin Zhao, Yuanqing Li, El-Sayed M. El-Alfy
Publisher	Springer Verlag
Pages	122-129
Number of pages	8
ISBN (Print)	9783319701356
DOIs	https://doi.org/10.1007/978-3-319-70136-3_13
State	Published - 2017
Event	24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China Duration: Nov 14 2017 → Nov 18 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10639 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	24th International Conference on Neural Information Processing, ICONIP 2017
Country/Territory	China
City	Guangzhou
Period	11/14/17 → 11/18/17

Keywords

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

Access to Document

10.1007/978-3-319-70136-3_13

Cite this

Gao, W., & Jiang, Z. P. (2017). Data-driven nonlinear adaptive optimal control of connected vehicles. In D. Liu, S. Xie, D. Zhao, Y. Li, & E.-S. M. El-Alfy (Eds.), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings (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. ed. / Derong Liu; Shengli Xie; Dongbin Zhao; Yuanqing Li; El-Sayed M. El-Alfy. 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 proceeding › Conference contribution

Gao, W & Jiang, ZP 2017, Data-driven nonlinear adaptive optimal control of connected vehicles. in D Liu, S Xie, D Zhao, Y Li & E-SM El-Alfy (eds), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. 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 ZP. Data-driven nonlinear adaptive optimal control of connected vehicles. In Liu D, Xie S, Zhao D, Li Y, El-Alfy ESM, editors, Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Springer Verlag. 2017. p. 122-129. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 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. editor / Derong Liu ; Shengli Xie ; Dongbin Zhao ; Yuanqing Li ; El-Sayed M. El-Alfy. 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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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.",

keywords = "Connected and autonomous vehicles, Cooperative adaptive cruise control, Global adaptive dynamic programming, Nonlinear optimal control",

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N2 - 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.

AB - 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.

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Data-driven nonlinear adaptive optimal control of connected vehicles

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Keywords

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