Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control

J.W. van Wingerden; S.P. Mulders; R.T.O. Dinkla; T.A.E. Oomen; M.H.G. Verhaegen

doi:10.1109/CDC51059.2022.9992824

Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control

J.W. van Wingerden, S.P. Mulders, R.T.O. Dinkla, T.A.E. Oomen, M.H.G. Verhaegen

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

8 Citations (Scopus)

11 Downloads (Pure)

Abstract

Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable (IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable noise mitigation properties, and demonstrates the direct equivalence between DeePC and Subspace Predictive Control (SPC). The methodology relies on the derivation of the characteristic equation in DeePC along the lines of subspace identification algorithms. A particular choice of IVs is presented that is uncorrelated with future noise, but at the same time highly correlated with the data matrix. A simulation study demonstrates the improved performance of the proposed algorithm in the presence of process and measurement noise.

Original language	English
Title of host publication	Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022)
Publisher	IEEE
Pages	2111-2116
ISBN (Print)	978-1-6654-6761-2
DOIs	https://doi.org/10.1109/CDC51059.2022.9992824
Publication status	Published - 2022
Event	IEEE 61st Conference on Decision and Control (CDC 2022) - Cancún, Mexico Duration: 6 Dec 2022 → 9 Dec 2022

Conference

Conference	IEEE 61st Conference on Decision and Control (CDC 2022)
Country/Territory	Mexico
City	Cancún
Period	6/12/22 → 9/12/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Instruments
Measurement uncertainty
Prediction algorithms
Mathematical models
Parametric statistics
Noise measurement
Predictive control

Access to Document

10.1109/CDC51059.2022.9992824

Data-enabled_predictive_control_with_instrumental_variables_the_direct_equivalence_with_subspace_predictive_controlFinal published version, 1.26 MB

Cite this

@inproceedings{0464de025c2840c2b0afbef38e0ab411,

title = "Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control",

abstract = "Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable (IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable noise mitigation properties, and demonstrates the direct equivalence between DeePC and Subspace Predictive Control (SPC). The methodology relies on the derivation of the characteristic equation in DeePC along the lines of subspace identification algorithms. A particular choice of IVs is presented that is uncorrelated with future noise, but at the same time highly correlated with the data matrix. A simulation study demonstrates the improved performance of the proposed algorithm in the presence of process and measurement noise.",

keywords = "Instruments, Measurement uncertainty, Prediction algorithms, Mathematical models, Parametric statistics, Noise measurement, Predictive control",

author = "{van Wingerden}, J.W. and S.P. Mulders and R.T.O. Dinkla and T.A.E. Oomen and M.H.G. Verhaegen",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; IEEE 61st Conference on Decision and Control (CDC 2022) ; Conference date: 06-12-2022 Through 09-12-2022",

year = "2022",

doi = "10.1109/CDC51059.2022.9992824",

language = "English",

isbn = "978-1-6654-6761-2",

pages = "2111--2116",

booktitle = "Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022)",

publisher = "IEEE",

address = "United States",

}

van Wingerden, JW , Mulders, SP , Dinkla, RTO , Oomen, TAE & Verhaegen, MHG 2022, Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control. in Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022). IEEE, pp. 2111-2116, IEEE 61st Conference on Decision and Control (CDC 2022), Cancún, Mexico, 6/12/22. https://doi.org/10.1109/CDC51059.2022.9992824

Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control. / van Wingerden, J.W.; Mulders, S.P.; Dinkla, R.T.O. et al.
Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022). IEEE, 2022. p. 2111-2116.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Data-enabled predictive control with instrumental variables

T2 - IEEE 61st Conference on Decision and Control (CDC 2022)

AU - van Wingerden, J.W.

AU - Mulders, S.P.

AU - Dinkla, R.T.O.

AU - Oomen, T.A.E.

AU - Verhaegen, M.H.G.

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2022

Y1 - 2022

N2 - Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable (IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable noise mitigation properties, and demonstrates the direct equivalence between DeePC and Subspace Predictive Control (SPC). The methodology relies on the derivation of the characteristic equation in DeePC along the lines of subspace identification algorithms. A particular choice of IVs is presented that is uncorrelated with future noise, but at the same time highly correlated with the data matrix. A simulation study demonstrates the improved performance of the proposed algorithm in the presence of process and measurement noise.

AB - Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable (IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable noise mitigation properties, and demonstrates the direct equivalence between DeePC and Subspace Predictive Control (SPC). The methodology relies on the derivation of the characteristic equation in DeePC along the lines of subspace identification algorithms. A particular choice of IVs is presented that is uncorrelated with future noise, but at the same time highly correlated with the data matrix. A simulation study demonstrates the improved performance of the proposed algorithm in the presence of process and measurement noise.

KW - Instruments

KW - Measurement uncertainty

KW - Prediction algorithms

KW - Mathematical models

KW - Parametric statistics

KW - Noise measurement

KW - Predictive control

UR - http://www.scopus.com/inward/record.url?scp=85146995925&partnerID=8YFLogxK

U2 - 10.1109/CDC51059.2022.9992824

DO - 10.1109/CDC51059.2022.9992824

M3 - Conference contribution

SN - 978-1-6654-6761-2

SP - 2111

EP - 2116

BT - Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022)

PB - IEEE

Y2 - 6 December 2022 through 9 December 2022

ER -

Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this