Publication

Data-Driven Model Reduction of Monotone Systems by Nonlinear DC Gains

Kawano, Y., Besselink, B., Scherpen, J. M. A. & Cao, M., 20-May-2020, In : IEEE-Transactions on Automatic Control. 65, 5, p. 2094 - 2106 13 p., 8822957.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Kawano, Y., Besselink, B., Scherpen, J. M. A., & Cao, M. (2020). Data-Driven Model Reduction of Monotone Systems by Nonlinear DC Gains. IEEE-Transactions on Automatic Control, 65(5), 2094 - 2106. [8822957]. https://doi.org/10.1109/TAC.2019.2939191

Author

Kawano, Yu ; Besselink, Bart ; Scherpen, Jacquelien M.A. ; Cao, Ming. / Data-Driven Model Reduction of Monotone Systems by Nonlinear DC Gains. In: IEEE-Transactions on Automatic Control. 2020 ; Vol. 65, No. 5. pp. 2094 - 2106.

Harvard

Kawano, Y, Besselink, B, Scherpen, JMA & Cao, M 2020, 'Data-Driven Model Reduction of Monotone Systems by Nonlinear DC Gains', IEEE-Transactions on Automatic Control, vol. 65, no. 5, 8822957, pp. 2094 - 2106. https://doi.org/10.1109/TAC.2019.2939191

Standard

Data-Driven Model Reduction of Monotone Systems by Nonlinear DC Gains. / Kawano, Yu; Besselink, Bart; Scherpen, Jacquelien M.A.; Cao, Ming.

In: IEEE-Transactions on Automatic Control, Vol. 65, No. 5, 8822957, 20.05.2020, p. 2094 - 2106.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Kawano Y, Besselink B, Scherpen JMA, Cao M. Data-Driven Model Reduction of Monotone Systems by Nonlinear DC Gains. IEEE-Transactions on Automatic Control. 2020 May 20;65(5):2094 - 2106. 8822957. https://doi.org/10.1109/TAC.2019.2939191


BibTeX

@article{9f82e8f4ecf041e8993aeae7ad938058,
title = "Data-Driven Model Reduction of Monotone Systems by Nonlinear DC Gains",
abstract = "In this paper, we develop data-driven model reduction methods for monotone nonlinear control systems based on a nonlinear version of the dc gain. The nonlinear dc gain is a function of the amplitude of the input and can be used to evaluate the importance of each state variable. In fact, the nonlinear dc gain is directly related to the infinity-induced norm of the system as well as a notion of output reachability. Given the dc gain, model reduction is performed by either truncating not-so-important state variables or aggregating state variables having similar importance. Under such truncation and clustering, monotonicity and boundedness of the nonlinear dc gain are preserved; moreover, these two operations can be approximately performed based on simulation or experimental data alone. This empirical model reduction approach is illustrated by an example of a gene regulatory network.",
author = "Yu Kawano and Bart Besselink and Scherpen, {Jacquelien M.A.} and Ming Cao",
year = "2020",
month = may,
day = "20",
doi = "10.1109/TAC.2019.2939191",
language = "English",
volume = "65",
pages = "2094 -- 2106",
journal = "IEEE-Transactions on Automatic Control",
issn = "0018-9286",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "5",

}

RIS

TY - JOUR

T1 - Data-Driven Model Reduction of Monotone Systems by Nonlinear DC Gains

AU - Kawano, Yu

AU - Besselink, Bart

AU - Scherpen, Jacquelien M.A.

AU - Cao, Ming

PY - 2020/5/20

Y1 - 2020/5/20

N2 - In this paper, we develop data-driven model reduction methods for monotone nonlinear control systems based on a nonlinear version of the dc gain. The nonlinear dc gain is a function of the amplitude of the input and can be used to evaluate the importance of each state variable. In fact, the nonlinear dc gain is directly related to the infinity-induced norm of the system as well as a notion of output reachability. Given the dc gain, model reduction is performed by either truncating not-so-important state variables or aggregating state variables having similar importance. Under such truncation and clustering, monotonicity and boundedness of the nonlinear dc gain are preserved; moreover, these two operations can be approximately performed based on simulation or experimental data alone. This empirical model reduction approach is illustrated by an example of a gene regulatory network.

AB - In this paper, we develop data-driven model reduction methods for monotone nonlinear control systems based on a nonlinear version of the dc gain. The nonlinear dc gain is a function of the amplitude of the input and can be used to evaluate the importance of each state variable. In fact, the nonlinear dc gain is directly related to the infinity-induced norm of the system as well as a notion of output reachability. Given the dc gain, model reduction is performed by either truncating not-so-important state variables or aggregating state variables having similar importance. Under such truncation and clustering, monotonicity and boundedness of the nonlinear dc gain are preserved; moreover, these two operations can be approximately performed based on simulation or experimental data alone. This empirical model reduction approach is illustrated by an example of a gene regulatory network.

U2 - 10.1109/TAC.2019.2939191

DO - 10.1109/TAC.2019.2939191

M3 - Article

VL - 65

SP - 2094

EP - 2106

JO - IEEE-Transactions on Automatic Control

JF - IEEE-Transactions on Automatic Control

SN - 0018-9286

IS - 5

M1 - 8822957

ER -

ID: 125881444