Robust adaptive dynamic programming for large-scale systems with an application to multimachine power systems

Yu Jiang; Zhong Ping Jiang

doi:10.1109/TCSII.2012.2213353

Robust adaptive dynamic programming for large-scale systems with an application to multimachine power systems

Yu Jiang, Zhong Ping Jiang

Electrical and Computer Engineering

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

Abstract

This brief presents a new approach to decentralized control design of complex systems with unknown parameters and dynamic uncertainties. A key strategy is to use the theory of robust adaptive dynamic programming and the policy iteration technique. An iterative control algorithm is given to devise a decentralized optimal controller that globally asymptotically stabilizes the system in question. Stability analysis is accomplished by means of the small-gain theorem. The effectiveness of the proposed computational control algorithm is demonstrated via the online learning control of multimachine power systems with governor controllers.

Original language	English (US)
Article number	6297448
Pages (from-to)	693-697
Number of pages	5
Journal	IEEE Transactions on Circuits and Systems II: Express Briefs
Volume	59
Issue number	10
DOIs	https://doi.org/10.1109/TCSII.2012.2213353
State	Published - 2012

Keywords

Adaptive dynamic programming (ADP)
decentralized control
multimachine power systems
small-gain

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/TCSII.2012.2213353

Cite this

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title = "Robust adaptive dynamic programming for large-scale systems with an application to multimachine power systems",

abstract = "This brief presents a new approach to decentralized control design of complex systems with unknown parameters and dynamic uncertainties. A key strategy is to use the theory of robust adaptive dynamic programming and the policy iteration technique. An iterative control algorithm is given to devise a decentralized optimal controller that globally asymptotically stabilizes the system in question. Stability analysis is accomplished by means of the small-gain theorem. The effectiveness of the proposed computational control algorithm is demonstrated via the online learning control of multimachine power systems with governor controllers.",

keywords = "Adaptive dynamic programming (ADP), decentralized control, multimachine power systems, small-gain",

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

note = "Funding Information: Manuscript received March 31, 2012; revised June 5, 2012; accepted July 18, 2012. Date of publication September 7, 2012; date of current version October 12, 2012. This work was supported in part by the National Science Foundation under Grants DMS-0906659 and ECCS-1101401. This brief was recommended by Associate Editor Y. Nishio.",

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doi = "10.1109/TCSII.2012.2213353",

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AU - Jiang, Zhong Ping

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AB - This brief presents a new approach to decentralized control design of complex systems with unknown parameters and dynamic uncertainties. A key strategy is to use the theory of robust adaptive dynamic programming and the policy iteration technique. An iterative control algorithm is given to devise a decentralized optimal controller that globally asymptotically stabilizes the system in question. Stability analysis is accomplished by means of the small-gain theorem. The effectiveness of the proposed computational control algorithm is demonstrated via the online learning control of multimachine power systems with governor controllers.

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KW - small-gain

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Robust adaptive dynamic programming for large-scale systems with an application to multimachine power systems

Abstract

Keywords

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