Details
| Original language | English |
|---|---|
| Pages (from-to) | 180-193 |
| Number of pages | 14 |
| Journal | IEEE Open Journal of Control Systems |
| Volume | 1 |
| Publication status | Published - 19 Aug 2022 |
Abstract
Keywords
- Data-driven control, linear systems, online optimization, optimal control
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Engineering(all)
- Electrical and Electronic Engineering
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Vision and Pattern Recognition
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In: IEEE Open Journal of Control Systems, Vol. 1, 19.08.2022, p. 180-193.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Online Convex Optimization for Data-Driven Control of Dynamical Systems
AU - Nonhoff, Marko
AU - Müller, Matthias A.
N1 - Publisher Copyright: © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2022/8/19
Y1 - 2022/8/19
N2 - We propose an algorithm based on online convex optimization for controlling discrete-time linear dynamical systems. The algorithm is data-driven, i.e., does not require a model of the system, and is able to handle a priori unknown and time-varying cost functions. To this end, we make use of a single persistently exciting input-output sequence of the system and results from behavioral systems theory which enable it to handle unknown linear time-invariant systems. Moreover, we consider noisy output feedback instead of full state measurements and allow general economic cost functions. Our analysis of the closed loop reveals that the algorithm is able to achieve sublinear regret, where the measurement noise only adds an additional constant term to the regret upper bound. In order to do so, we derive a data-driven characterization of the steady-state manifold of an unknown system. Moreover, our algorithm is able to asymptotically exactly estimate the measurement noise. The effectiveness and applicational aspects of the proposed method are illustrated by means of a detailed simulation example in thermal control.
AB - We propose an algorithm based on online convex optimization for controlling discrete-time linear dynamical systems. The algorithm is data-driven, i.e., does not require a model of the system, and is able to handle a priori unknown and time-varying cost functions. To this end, we make use of a single persistently exciting input-output sequence of the system and results from behavioral systems theory which enable it to handle unknown linear time-invariant systems. Moreover, we consider noisy output feedback instead of full state measurements and allow general economic cost functions. Our analysis of the closed loop reveals that the algorithm is able to achieve sublinear regret, where the measurement noise only adds an additional constant term to the regret upper bound. In order to do so, we derive a data-driven characterization of the steady-state manifold of an unknown system. Moreover, our algorithm is able to asymptotically exactly estimate the measurement noise. The effectiveness and applicational aspects of the proposed method are illustrated by means of a detailed simulation example in thermal control.
KW - Data-driven control
KW - linear systems
KW - online optimization
KW - optimal control
UR - http://www.scopus.com/inward/record.url?scp=85142876590&partnerID=8YFLogxK
U2 - 10.1109/OJCSYS.2022.3200021
DO - 10.1109/OJCSYS.2022.3200021
M3 - Article
VL - 1
SP - 180
EP - 193
JO - IEEE Open Journal of Control Systems
JF - IEEE Open Journal of Control Systems
SN - 2694-085X
ER -