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

Jan-Willem van Wingerden; Sebastiaan P. Mulders; Rogier Dinkla; Tom Oomen; Michel Verhaegen

doi:10.1109/CDC51059.2022.9992824

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

Jan-Willem van Wingerden, Sebastiaan P. Mulders, Rogier Dinkla, Tom Oomen, Michel Verhaegen

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

11 Citations (Scopus)

52 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	61th IEEE Conference on Decision and Control, Cancun, Mexico, 2022
Publisher	Institute of Electrical and Electronics Engineers
Pages	2111-2116
Number of pages	6
ISBN (Electronic)	978-1-6654-6761-2
DOIs	https://doi.org/10.1109/CDC51059.2022.9992824
Publication status	Published - 10 Jan 2023
Externally published	Yes
Event	61st IEEE Conference on Decision and Control, CDC 2022 - The Marriott Cancún Collection, Cancun, Mexico Duration: 6 Dec 2022 → 9 Dec 2022 Conference number: 61 https://cdc2022.ieeecss.org/

Conference

Conference	61st IEEE Conference on Decision and Control, CDC 2022
Abbreviated title	CDC 2022
Country/Territory	Mexico
City	Cancun
Period	6/12/22 → 9/12/22
Internet address	https://cdc2022.ieeecss.org/

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10.1109/CDC51059.2022.9992824

WingerdenMulDinOomVer2022Final author version , 589 KB

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van Wingerden, J.-W., Mulders, S. P., Dinkla, R., Oomen, T., & Verhaegen, M. (2023). Data-enabled predictive control with instrumental variables: The direct equivalence with subspace predictive control. In 61th IEEE Conference on Decision and Control, Cancun, Mexico, 2022 (pp. 2111-2116). Article 9992824 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CDC51059.2022.9992824

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van Wingerden, J-W, Mulders, SP, Dinkla, R, Oomen, T & Verhaegen, M 2023, Data-enabled predictive control with instrumental variables: The direct equivalence with subspace predictive control. in 61th IEEE Conference on Decision and Control, Cancun, Mexico, 2022., 9992824, Institute of Electrical and Electronics Engineers, pp. 2111-2116, 61st IEEE Conference on Decision and Control, CDC 2022, Cancun, 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, Jan-Willem; Mulders, Sebastiaan P.; Dinkla, Rogier et al.
61th IEEE Conference on Decision and Control, Cancun, Mexico, 2022. Institute of Electrical and Electronics Engineers, 2023. p. 2111-2116 9992824.

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

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AU - Oomen, Tom

AU - Verhaegen, Michel

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Y1 - 2023/1/10

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.

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

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