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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. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 5 (2). Art. No. 18. ISSN 2476-1249. https://resolver.caltech.edu/CaltechAUTHORS:20210604-111535691

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Abstract

This paper considers an online control problem involving two controllers. 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) and demonstrate that aggregated information can be efficiently learned using reinforcement learning algorithms. The effectiveness of our approach for closed-loop coordination between central and local controllers is validated via an electric vehicle charging application in power systems.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3460085DOIArticle
https://resolver.caltech.edu/CaltechAUTHORS:20210607-115053999Related ItemConference Paper
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. This work is licensed under a Creative Commons Attribution International 4.0 License. Tongxin Li and Steven Low acknowledge the support received from National Science Foundation (NSF) through grants CCF 1637598, ECCS 1931662 and CPS ECCS 1932611. Bo Sun is supported by Hong Kong Research Grant Council (RGC) General Research Fund (Project 16207318). Adam Wierman’s research is funded by NSF (AitF-1637598 and CNS-1518941), PIMCO, and Amazon AWS.
Funders:
Funding AgencyGrant Number
NSFCCF-1637598
NSFECCS-1931662
NSFECCS-1932611
Hong Kong Research Grant Council16207318
NSFCNS-1518941
PIMCOUNSPECIFIED
Amazon Web ServicesUNSPECIFIED
Subject Keywords:online control; closed-loop control; model predictive control; regret analysis; electric vehicle charging
Issue or Number:2
Classification Code:CCS Concepts: Theory of computation→Online algorithms; Regret bounds; Hardware→Smart grid.
Record Number:CaltechAUTHORS:20210604-111535691
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210604-111535691
Official Citation:Tongxin Li, Yue Chen, Bo Sun, Adam Wierman, and Steven Low. 2021. Information Aggregation for Constrained Online Control. Proc. ACM Meas. Anal. Comput. Syst. 5, 2, Article 18 (June 2021), 34 pages. https://doi.org/10.1145/3460085
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109387
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:07 Jun 2021 15:44
Last Modified:07 Jun 2021 22:17

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