Approximate Projection-Based Control of Networks
Name
CDC20.pdf
Description
Accepted version
Size
671.41 KB
Format
Adobe PDF
Checksum (MD5)
13cb9d576833ee57b87f946db0f85d69
Author(s)
Li, Max Z
Gopalakrishnan, Karthik
Balakrishnan, Hamsa
Date Issued
2020
Journal
Proceedings of the IEEE Conference on Decision and Control
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Li, Max Z, Gopalakrishnan, Karthik and Balakrishnan, Hamsa. 2020. "Approximate Projection-Based Control of Networks." Proceedings of the IEEE Conference on Decision and Control, 2020-December.
Version
Author's final manuscript
Abstract
© 2020 IEEE. Modern infrastructures such as transportation and communication networks are large-scale systems with complex dependence structures between various sub-systems. Human-interpretable performance targets in such systems are often represented in terms of lower-dimensional projections of the high-dimensional state space. We consider the problem of designing control strategies for high-dimensional systems that lack a detailed model. To do so, we leverage the ability of copulas to represent dependant structures in high-dimensional data, and approximate the state space model through inverse sampling. We demonstrate the applicability of the control policies obtained from our methodology through a data-driven case study of controlling flight delays within the US air transportation network.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CDC42340.2020.9304414
