Direct data-driven model-reference control with Lyapunov stability guarantees

Valentina Breschi; Claudio De Persis; Simone Formentin; Pietro Tesi

doi:10.1109/CDC45484.2021.9683437

Direct data-driven model-reference control with Lyapunov stability guarantees

Valentina Breschi, Claudio De Persis, Simone Formentin, Pietro Tesi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

19 Citations (Scopus)

Abstract

We introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state. The proposed control action is composed by a static feedback term and a reference tracking block shaped from data to reproduce the desired behavior in closed-loop. By focusing on the case where the reference model and the plant share the same order, we propose an optimal design procedure with Lyapunov stability guarantees, tailored to handle state measurements with additive noise. Two simulation examples are illustrated to show the potential of the proposed strategy.

Original language	English
Title of host publication	60th IEEE Conference on Decision and Control, CDC 2021
Publisher	Institute of Electrical and Electronics Engineers
Pages	1456-1461
Number of pages	6
ISBN (Electronic)	978-1-6654-3659-5
DOIs	https://doi.org/10.1109/CDC45484.2021.9683437
Publication status	Published - 1 Feb 2022
Externally published	Yes
Event	60th IEEE Conference on Decision and Control, CDC 2021 - Austin, TX, USA, Austin, United States Duration: 13 Dec 2021 → 17 Dec 2021 Conference number: 60 https://2021.ieeecdc.org/

Conference

Conference	60th IEEE Conference on Decision and Control, CDC 2021
Abbreviated title	CDC 2021
Country/Territory	United States
City	Austin
Period	13/12/21 → 17/12/21
Internet address	https://2021.ieeecdc.org/

Keywords

Data-driven control
Control design
Model reference control

Access to Document

10.1109/CDC45484.2021.9683437

Cite this

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title = "Direct data-driven model-reference control with Lyapunov stability guarantees",

abstract = "We introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state. The proposed control action is composed by a static feedback term and a reference tracking block shaped from data to reproduce the desired behavior in closed-loop. By focusing on the case where the reference model and the plant share the same order, we propose an optimal design procedure with Lyapunov stability guarantees, tailored to handle state measurements with additive noise. Two simulation examples are illustrated to show the potential of the proposed strategy.",

keywords = "Data-driven control, Control design, Model reference control",

author = "Valentina Breschi and {De Persis}, Claudio and Simone Formentin and Pietro Tesi",

year = "2022",

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doi = "10.1109/CDC45484.2021.9683437",

language = "English",

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booktitle = "60th IEEE Conference on Decision and Control, CDC 2021",

publisher = "Institute of Electrical and Electronics Engineers",

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note = "60th IEEE Conference on Decision and Control, CDC 2021, CDC 2021 ; Conference date: 13-12-2021 Through 17-12-2021",

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}

Breschi, V, De Persis, C, Formentin, S & Tesi, P 2022, Direct data-driven model-reference control with Lyapunov stability guarantees. in 60th IEEE Conference on Decision and Control, CDC 2021., 9683437, Institute of Electrical and Electronics Engineers, pp. 1456-1461, 60th IEEE Conference on Decision and Control, CDC 2021, Austin, Texas, United States, 13/12/21. https://doi.org/10.1109/CDC45484.2021.9683437

Direct data-driven model-reference control with Lyapunov stability guarantees. / Breschi, Valentina; De Persis, Claudio; Formentin, Simone et al.
60th IEEE Conference on Decision and Control, CDC 2021. Institute of Electrical and Electronics Engineers, 2022. p. 1456-1461 9683437.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Direct data-driven model-reference control with Lyapunov stability guarantees

AU - Breschi, Valentina

AU - De Persis, Claudio

AU - Formentin, Simone

AU - Tesi, Pietro

N1 - Conference code: 60

PY - 2022/2/1

Y1 - 2022/2/1

N2 - We introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state. The proposed control action is composed by a static feedback term and a reference tracking block shaped from data to reproduce the desired behavior in closed-loop. By focusing on the case where the reference model and the plant share the same order, we propose an optimal design procedure with Lyapunov stability guarantees, tailored to handle state measurements with additive noise. Two simulation examples are illustrated to show the potential of the proposed strategy.

AB - We introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state. The proposed control action is composed by a static feedback term and a reference tracking block shaped from data to reproduce the desired behavior in closed-loop. By focusing on the case where the reference model and the plant share the same order, we propose an optimal design procedure with Lyapunov stability guarantees, tailored to handle state measurements with additive noise. Two simulation examples are illustrated to show the potential of the proposed strategy.

KW - Data-driven control

KW - Control design

KW - Model reference control

UR - http://www.scopus.com/inward/record.url?scp=85126064586&partnerID=8YFLogxK

U2 - 10.1109/CDC45484.2021.9683437

DO - 10.1109/CDC45484.2021.9683437

M3 - Conference contribution

SP - 1456

EP - 1461

BT - 60th IEEE Conference on Decision and Control, CDC 2021

PB - Institute of Electrical and Electronics Engineers

T2 - 60th IEEE Conference on Decision and Control, CDC 2021

Y2 - 13 December 2021 through 17 December 2021

ER -

Direct data-driven model-reference control with Lyapunov stability guarantees

Abstract

Conference

Keywords

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