Online Gradient Descent for Linear Dynamical Systems

Publikation: Beitrag in FachzeitschriftKonferenzaufsatz in FachzeitschriftForschungPeer-Review

Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)945-952
Seitenumfang8
FachzeitschriftIFAC-PapersOnLine
Jahrgang53
Ausgabenummer2
PublikationsstatusVeröffentlicht - 2020
Veranstaltung21st IFAC World Congress 2020 - Berlin, Deutschland
Dauer: 12 Juli 202017 Juli 2020

Abstract

In this paper, online convex optimization is applied to the problem of controlling linear dynamical systems. An algorithm similar to online gradient descent, which can handle time-varying and unknown cost functions, is proposed. Then, performance guarantees are derived in terms of regret analysis. We show that the proposed control scheme achieves sublinear regret if the variation of the cost functions is sublinear. In addition, as a special case, the system converges to the optimal equilibrium if the cost functions are invariant after some finite time. Finally, the performance of the resulting closed loop is illustrated by numerical simulations.

ASJC Scopus Sachgebiete

Zitieren

Online Gradient Descent for Linear Dynamical Systems. / Nonhoff, Marko; Müller, Matthias A.
in: IFAC-PapersOnLine, Jahrgang 53, Nr. 2, 2020, S. 945-952.

Publikation: Beitrag in FachzeitschriftKonferenzaufsatz in FachzeitschriftForschungPeer-Review

Nonhoff M, Müller MA. Online Gradient Descent for Linear Dynamical Systems. IFAC-PapersOnLine. 2020;53(2):945-952. doi: 10.1016/j.ifacol.2020.12.1258
Nonhoff, Marko ; Müller, Matthias A. / Online Gradient Descent for Linear Dynamical Systems. in: IFAC-PapersOnLine. 2020 ; Jahrgang 53, Nr. 2. S. 945-952.
Download
@article{87da6f92a94c46eda9f1d7c70f8630e5,
title = "Online Gradient Descent for Linear Dynamical Systems",
abstract = "In this paper, online convex optimization is applied to the problem of controlling linear dynamical systems. An algorithm similar to online gradient descent, which can handle time-varying and unknown cost functions, is proposed. Then, performance guarantees are derived in terms of regret analysis. We show that the proposed control scheme achieves sublinear regret if the variation of the cost functions is sublinear. In addition, as a special case, the system converges to the optimal equilibrium if the cost functions are invariant after some finite time. Finally, the performance of the resulting closed loop is illustrated by numerical simulations.",
keywords = "math.OC, Linear systems, Real-time optimal control, Online convex optimization, Online gradient descent, Online learning, Predictive control",
author = "Marko Nonhoff and M{\"u}ller, {Matthias A.}",
year = "2020",
doi = "10.1016/j.ifacol.2020.12.1258",
language = "English",
volume = "53",
pages = "945--952",
number = "2",
note = "21st IFAC World Congress 2020 ; Conference date: 12-07-2020 Through 17-07-2020",

}

Download

TY - JOUR

T1 - Online Gradient Descent for Linear Dynamical Systems

AU - Nonhoff, Marko

AU - Müller, Matthias A.

PY - 2020

Y1 - 2020

N2 - In this paper, online convex optimization is applied to the problem of controlling linear dynamical systems. An algorithm similar to online gradient descent, which can handle time-varying and unknown cost functions, is proposed. Then, performance guarantees are derived in terms of regret analysis. We show that the proposed control scheme achieves sublinear regret if the variation of the cost functions is sublinear. In addition, as a special case, the system converges to the optimal equilibrium if the cost functions are invariant after some finite time. Finally, the performance of the resulting closed loop is illustrated by numerical simulations.

AB - In this paper, online convex optimization is applied to the problem of controlling linear dynamical systems. An algorithm similar to online gradient descent, which can handle time-varying and unknown cost functions, is proposed. Then, performance guarantees are derived in terms of regret analysis. We show that the proposed control scheme achieves sublinear regret if the variation of the cost functions is sublinear. In addition, as a special case, the system converges to the optimal equilibrium if the cost functions are invariant after some finite time. Finally, the performance of the resulting closed loop is illustrated by numerical simulations.

KW - math.OC

KW - Linear systems

KW - Real-time optimal control

KW - Online convex optimization

KW - Online gradient descent

KW - Online learning

KW - Predictive control

UR - http://www.scopus.com/inward/record.url?scp=85093311163&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2020.12.1258

DO - 10.1016/j.ifacol.2020.12.1258

M3 - Conference article

VL - 53

SP - 945

EP - 952

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 2

T2 - 21st IFAC World Congress 2020

Y2 - 12 July 2020 through 17 July 2020

ER -

Von denselben Autoren