Qu, Guannan and Shi, Yuanyuan and Lale, Sahin and Anandkumar, Animashree and Wierman, Adam (2021) Stable Online Control of Linear Time-Varying Systems. Proceedings of Machine Learning Research, 144 . pp. 742-753. ISSN 1938-7228. https://resolver.caltech.edu/CaltechAUTHORS:20210510-092451106
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Abstract
Linear time-varying (LTV) systems are widely used for modeling real-world dynamical systems due to their generality and simplicity. Providing stability guarantees for LTV systems is one of the central problems in control theory. However, existing approaches that guarantee stability typically lead to significantly sub-optimal cumulative control cost in online settings where only current or short-term system information is available. In this work, we propose an efficient online control algorithm, COvariance Constrained Online Linear Quadratic (COCO-LQ) control, that guarantees input-to-state stability for a large class of LTV systems while also minimizing the control cost. The proposed method incorporates a state covariance constraint into the semi-definite programming (SDP) formulation of the LQ optimal controller. We empirically demonstrate the performance of COCO-LQ in both synthetic experiments and a power system frequency control example.
Item Type: | Article | |||||||||
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Alternate Title: | Stable Online Control of LTV Systems Stable Online Control of Linear Time-Varying Systems | |||||||||
Additional Information: | © 2021 G. Qu, Y. Shi, S. Lale, A. Anandkumar & A. Wierman. | |||||||||
Subject Keywords: | Time-varying systems, online linear quadratic control, stability guarantee | |||||||||
Record Number: | CaltechAUTHORS:20210510-092451106 | |||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20210510-092451106 | |||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
ID Code: | 109025 | |||||||||
Collection: | CaltechAUTHORS | |||||||||
Deposited By: | Tony Diaz | |||||||||
Deposited On: | 10 May 2021 17:50 | |||||||||
Last Modified: | 16 Jul 2021 20:03 |
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