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Information Aggregation for Constrained Online Control

Li, Tongxin and Chen, Yue and Sun, Bo and Wierman, Adam and Low, Steven (2021) Information Aggregation for Constrained Online Control. In: SIGMETRICS '21: Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. ACM , New York, NY, pp. 7-8. ISBN 9781450380720. https://resolver.caltech.edu/CaltechAUTHORS:20210607-115053999

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Abstract

We consider a two-controller online control problem where a central controller chooses an action from a feasible set that is determined by time-varying and coupling constraints, which depend on all past actions and states. The central controller's goal is to minimize the cumulative cost; however, the controller has access to neither the feasible set nor the dynamics directly, which are determined by a remote local controller. Instead, the central controller receives only an aggregate summary of the feasibility information from the local controller, which does not know the system costs. We show that it is possible for an online algorithm using feasibility information to nearly match the dynamic regret of an online algorithm using perfect information whenever the feasible sets satisfy a causal invariance criterion and there is a sufficiently large prediction window size. To do so, we use a form of feasibility aggregation based on entropic maximization in combination with a novel online algorithm, named Penalized Predictive Control (PPC).


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3410220.3461737DOIArticle
https://resolver.caltech.edu/CaltechAUTHORS:20210604-111535691Related ItemJournal Article
ORCID:
AuthorORCID
Li, Tongxin0000-0002-9806-8964
Chen, Yue0000-0002-7594-7587
Sun, Bo0000-0003-3172-7811
Low, Steven0000-0001-6476-3048
Additional Information:© 2021 Copyright held by the owner/author(s).
Funders:
Funding AgencyGrant Number
NSFCCF-1637598
Amazon Web ServicesUNSPECIFIED
Hong Kong Research Grant Council16207318
PIMCOECCS-1931662
NSFECCS-1932611
NSFCCF-1637598
NSFCNS-1518941
Subject Keywords:online control; closed-loop control; model predictive control; regret analysis; electric vehicle charging
Record Number:CaltechAUTHORS:20210607-115053999
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210607-115053999
Official Citation:Tongxin Li, Yue Chen, Bo Sun, Adam Wierman, and Steven Low. 2021. Information Aggregation for Constrained Online Control. In Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS ’21 Abstracts), June 14–18, 2021, Virtual Event, China. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3410220.3461737
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109416
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:07 Jun 2021 22:17
Last Modified:07 Jun 2021 22:17

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