Details
| Original language | English |
|---|---|
| Title of host publication | 2021 American Control Conference (ACC) |
| Pages | 2523-2528 |
| Number of pages | 6 |
| ISBN (electronic) | 978-1-6654-4197-1 |
| Publication status | Published - 2021 |
Publication series
| Name | Proceedings of the American Control Conference |
|---|---|
| ISSN (Print) | 0743-1619 |
| ISSN (electronic) | 2378-5861 |
Abstract
This paper studies the problem of controlling linear dynamical systems subject to point-wise-in-time constraints. We present an algorithm similar to online gradient descent, that can handle time-varying and a priori unknown convex cost functions while restraining the system states and inputs to polytopic constraint sets. Analysis of the algorithm's performance, measured by dynamic regret, reveals that sub-linear regret is achieved if the variation of the cost functions is sublinear in time. Finally, we present an example to illustrate implementation details as well as the algorithm's performance and show that the proposed algorithm ensures constraint satisfaction.
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
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2021 American Control Conference (ACC). 2021. p. 2523-2528 9482877 (Proceedings of the American Control Conference).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - An online convex optimization algorithm for controlling linear systems with state and input constraints
AU - Nonhoff, Marko
AU - Müller, Matthias A.
PY - 2021
Y1 - 2021
N2 - This paper studies the problem of controlling linear dynamical systems subject to point-wise-in-time constraints. We present an algorithm similar to online gradient descent, that can handle time-varying and a priori unknown convex cost functions while restraining the system states and inputs to polytopic constraint sets. Analysis of the algorithm's performance, measured by dynamic regret, reveals that sub-linear regret is achieved if the variation of the cost functions is sublinear in time. Finally, we present an example to illustrate implementation details as well as the algorithm's performance and show that the proposed algorithm ensures constraint satisfaction.
AB - This paper studies the problem of controlling linear dynamical systems subject to point-wise-in-time constraints. We present an algorithm similar to online gradient descent, that can handle time-varying and a priori unknown convex cost functions while restraining the system states and inputs to polytopic constraint sets. Analysis of the algorithm's performance, measured by dynamic regret, reveals that sub-linear regret is achieved if the variation of the cost functions is sublinear in time. Finally, we present an example to illustrate implementation details as well as the algorithm's performance and show that the proposed algorithm ensures constraint satisfaction.
UR - http://www.scopus.com/inward/record.url?scp=85103864395&partnerID=8YFLogxK
U2 - 10.23919/ACC50511.2021.9482877
DO - 10.23919/ACC50511.2021.9482877
M3 - Conference contribution
SN - 978-1-6654-4198-8
SN - 978-1-7281-9704-3
T3 - Proceedings of the American Control Conference
SP - 2523
EP - 2528
BT - 2021 American Control Conference (ACC)
ER -