Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics

Yu Jiang; Zhong Ping Jiang

doi:10.1016/j.automatica.2012.06.096

Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics

Yu Jiang, Zhong Ping Jiang

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

This paper presents a novel policy iteration approach for finding online adaptive optimal controllers for continuous-time linear systems with completely unknown system dynamics. The proposed approach employs the approximate/adaptive dynamic programming technique to iteratively solve the algebraic Riccati equation using the online information of state and input, without requiring the a priori knowledge of the system matrices. In addition, all iterations can be conducted by using repeatedly the same state and input information on some fixed time intervals. A practical online algorithm is developed in this paper, and is applied to the controller design for a turbocharged diesel engine with exhaust gas recirculation. Finally, several aspects of future work are discussed.

Original language	English (US)
Pages (from-to)	2699-2704
Number of pages	6
Journal	Automatica
Volume	48
Issue number	10
DOIs	https://doi.org/10.1016/j.automatica.2012.06.096
State	Published - Oct 2012

Keywords

Adaptive optimal control
Linear-quadratic regulator (LQR)
Policy iterations

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1016/j.automatica.2012.06.096

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title = "Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics",

abstract = "This paper presents a novel policy iteration approach for finding online adaptive optimal controllers for continuous-time linear systems with completely unknown system dynamics. The proposed approach employs the approximate/adaptive dynamic programming technique to iteratively solve the algebraic Riccati equation using the online information of state and input, without requiring the a priori knowledge of the system matrices. In addition, all iterations can be conducted by using repeatedly the same state and input information on some fixed time intervals. A practical online algorithm is developed in this paper, and is applied to the controller design for a turbocharged diesel engine with exhaust gas recirculation. Finally, several aspects of future work are discussed.",

keywords = "Adaptive optimal control, Linear-quadratic regulator (LQR), Policy iterations",

author = "Yu Jiang and Jiang, {Zhong Ping}",

note = "Funding Information: An IEEE Fellow, Dr. Jiang is a Subject Editor for the International Journal of Robust and Nonlinear Control and has served as an Associate Editor for several journals including Mathematics of Control, Signals and Systems (MCSS), Systems & Control Letters, IEEE Transactions on Automatic Control, European Journal of Control and J. Control Theory and Applications. Dr. Jiang is a recipient of the prestigious Queen Elizabeth II Fellowship Award from the Australian Research Council, the CAREER Award from the US National Science Foundation, and the Young Investigator Award from the NSF of China. He received the Best Theory Paper Award (with Y. Wang) at the 2008 World Congress on Intelligent Control and Automation, and with T. Liu and D.J. Hill, the Guan Zhao Zhi Best Paper Award at the 2011 Chinese Control Conference.",

year = "2012",

month = oct,

doi = "10.1016/j.automatica.2012.06.096",

language = "English (US)",

volume = "48",

pages = "2699--2704",

journal = "Automatica",

issn = "0005-1098",

publisher = "Elsevier Ltd",

number = "10",

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AU - Jiang, Yu

AU - Jiang, Zhong Ping

N1 - Funding Information: An IEEE Fellow, Dr. Jiang is a Subject Editor for the International Journal of Robust and Nonlinear Control and has served as an Associate Editor for several journals including Mathematics of Control, Signals and Systems (MCSS), Systems & Control Letters, IEEE Transactions on Automatic Control, European Journal of Control and J. Control Theory and Applications. Dr. Jiang is a recipient of the prestigious Queen Elizabeth II Fellowship Award from the Australian Research Council, the CAREER Award from the US National Science Foundation, and the Young Investigator Award from the NSF of China. He received the Best Theory Paper Award (with Y. Wang) at the 2008 World Congress on Intelligent Control and Automation, and with T. Liu and D.J. Hill, the Guan Zhao Zhi Best Paper Award at the 2011 Chinese Control Conference.

PY - 2012/10

Y1 - 2012/10

N2 - This paper presents a novel policy iteration approach for finding online adaptive optimal controllers for continuous-time linear systems with completely unknown system dynamics. The proposed approach employs the approximate/adaptive dynamic programming technique to iteratively solve the algebraic Riccati equation using the online information of state and input, without requiring the a priori knowledge of the system matrices. In addition, all iterations can be conducted by using repeatedly the same state and input information on some fixed time intervals. A practical online algorithm is developed in this paper, and is applied to the controller design for a turbocharged diesel engine with exhaust gas recirculation. Finally, several aspects of future work are discussed.

AB - This paper presents a novel policy iteration approach for finding online adaptive optimal controllers for continuous-time linear systems with completely unknown system dynamics. The proposed approach employs the approximate/adaptive dynamic programming technique to iteratively solve the algebraic Riccati equation using the online information of state and input, without requiring the a priori knowledge of the system matrices. In addition, all iterations can be conducted by using repeatedly the same state and input information on some fixed time intervals. A practical online algorithm is developed in this paper, and is applied to the controller design for a turbocharged diesel engine with exhaust gas recirculation. Finally, several aspects of future work are discussed.

KW - Adaptive optimal control

KW - Linear-quadratic regulator (LQR)

KW - Policy iterations

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EP - 2704

JO - Automatica

JF - Automatica

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Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics

Abstract

Keywords

ASJC Scopus subject areas

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