Details
| Original language | English |
|---|---|
| Pages (from-to) | 2570-2575 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 56 |
| Issue number | 2 |
| Early online date | 22 Nov 2023 |
| Publication status | Published - 2023 |
| Event | 22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 |
Abstract
In this work, we propose a control scheme for linear systems subject to pointwise in time state and input constraints that aims to minimize time-varying and a priori unknown cost functions. The proposed controller is based on online convex optimization and a reference governor. In particular, we apply online gradient descent to track the time-varying and a priori unknown optimal steady state of the system. Moreover, we use a λ-contractive set to enforce constraint satisfaction and a sufficient convergence rate of the closed-loop system to the optimal steady state. We prove that the proposed scheme is recursively feasible, ensures that the state and input constraints are satisfied at all times, and achieves a dynamic regret that is linearly bounded by the variation of the cost functions. The algorithm's performance and constraint satisfaction is illustrated by means of a simulation example.
Keywords
- control of constrained systems, dynamic regret, online convex optimization, Optimal control, reference governor
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
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In: IFAC-PapersOnLine, Vol. 56, No. 2, 2023, p. 2570-2575.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Online convex optimization for constrained control of linear systems using a reference governor
AU - Nonhoff, Marko
AU - Köhler, Johannes
AU - Müller, Matthias A.
N1 - Publisher Copyright: Copyright © 2023 The Authors.
PY - 2023
Y1 - 2023
N2 - In this work, we propose a control scheme for linear systems subject to pointwise in time state and input constraints that aims to minimize time-varying and a priori unknown cost functions. The proposed controller is based on online convex optimization and a reference governor. In particular, we apply online gradient descent to track the time-varying and a priori unknown optimal steady state of the system. Moreover, we use a λ-contractive set to enforce constraint satisfaction and a sufficient convergence rate of the closed-loop system to the optimal steady state. We prove that the proposed scheme is recursively feasible, ensures that the state and input constraints are satisfied at all times, and achieves a dynamic regret that is linearly bounded by the variation of the cost functions. The algorithm's performance and constraint satisfaction is illustrated by means of a simulation example.
AB - In this work, we propose a control scheme for linear systems subject to pointwise in time state and input constraints that aims to minimize time-varying and a priori unknown cost functions. The proposed controller is based on online convex optimization and a reference governor. In particular, we apply online gradient descent to track the time-varying and a priori unknown optimal steady state of the system. Moreover, we use a λ-contractive set to enforce constraint satisfaction and a sufficient convergence rate of the closed-loop system to the optimal steady state. We prove that the proposed scheme is recursively feasible, ensures that the state and input constraints are satisfied at all times, and achieves a dynamic regret that is linearly bounded by the variation of the cost functions. The algorithm's performance and constraint satisfaction is illustrated by means of a simulation example.
KW - control of constrained systems
KW - dynamic regret
KW - online convex optimization
KW - Optimal control
KW - reference governor
UR - http://www.scopus.com/inward/record.url?scp=85165641421&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2211.09088
DO - 10.48550/arXiv.2211.09088
M3 - Conference article
AN - SCOPUS:85165641421
VL - 56
SP - 2570
EP - 2575
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
IS - 2
T2 - 22nd IFAC World Congress
Y2 - 9 July 2023 through 14 July 2023
ER -