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
![]() |
PDF
- Published Version
Creative Commons Attribution. 990kB |
Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20210607-115053999
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: |
| ||||||||||||||||
ORCID: |
| ||||||||||||||||
Additional Information: | © 2021 Copyright held by the owner/author(s). | ||||||||||||||||
Funders: |
| ||||||||||||||||
Subject Keywords: | online control; closed-loop control; model predictive control; regret analysis; electric vehicle charging | ||||||||||||||||
DOI: | 10.1145/3410220.3461737 | ||||||||||||||||
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: | 16 Nov 2021 19:35 |
Repository Staff Only: item control page