Bi-Level Online Control without Regret
Optimization and Control
2017-02-21 v1 Machine Learning
Systems and Control
Abstract
This paper considers a bi-level discrete-time control framework with real-time constraints, consisting of several local controllers and a central controller. The objective is to bridge the gap between the online convex optimization and real-time control literature by proposing an online control algorithm with small dynamic regret, which is a natural performance criterion in nonstationary environments related to real-time control problems. We illustrate how the proposed algorithm can be applied to real-time control of power setpoints in an electrical grid.
Keywords
Cite
@article{arxiv.1702.05548,
title = {Bi-Level Online Control without Regret},
author = {Andrey Bernstein},
journal= {arXiv preprint arXiv:1702.05548},
year = {2017}
}