Handling unmeasured disturbances in data-driven distributed control with virtual reference feedback tuning

Tom R.V. Steentjes; Paul M.J. Van den Hof; Mircea Lazar

doi:10.1016/j.ifacol.2021.08.359

Handling unmeasured disturbances in data-driven distributed control with virtual reference feedback tuning

Tom R.V. Steentjes
, Paul M.J. Van den Hof
, Mircea Lazar

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

84 Downloads (Pure)

Abstract

The data-driven synthesis of a distributed controller in the presence of noise is considered, via the distributed virtual reference feedback tuning (DVRFT) framework. The analysis is performed for a linear interconnected system on an arbitrary graph that is subject to unmeasured exogenous inputs. By solving a dynamic network identification problem with prediction-error filtering and a tailor-made noise model, we show that the distributed model-reference control problem can be solved directly from data. Sufficient conditions are provided for which the local controller estimates are consistent. Moreover, it is shown how the method can be applied in the single-input-single-output case, leading to consistent estimates with standard virtual reference feedback tuning as well. The effectiveness of the method is demonstrated via a small network example with two interconnected systems.

Original language	English
Pages (from-to)	204-209
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	54
Issue number	7
DOIs	https://doi.org/10.1016/j.ifacol.2021.08.359
Publication status	Published - 1 Jul 2021
Event	19th IFAC Symposium on System Identification (SYSID 2021) - Virtual, Padova, Italy Duration: 13 Jul 2021 → 16 Jul 2021 Conference number: 19 https://www.sysid2021.org/

Bibliographical note

Funding Information:
Funded by the European Research Council (ERC), Advanced Research Grant SYSDYNET, under the European Unions Horizon 2020 research and innovation programme (grant agreement No. 694504).

Funding

Funded by the European Research Council (ERC), Advanced Research Grant SYSDYNET, under the European Unions Horizon 2020 research and innovation programme (grant agreement ? 694504).

Keywords

Data-driven control
Distributed control
Dynamic networks
System identification

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10.1016/j.ifacol.2021.08.359

published versionFinal published version, 484 KB

Cite this

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title = "Handling unmeasured disturbances in data-driven distributed control with virtual reference feedback tuning",

abstract = "The data-driven synthesis of a distributed controller in the presence of noise is considered, via the distributed virtual reference feedback tuning (DVRFT) framework. The analysis is performed for a linear interconnected system on an arbitrary graph that is subject to unmeasured exogenous inputs. By solving a dynamic network identification problem with prediction-error filtering and a tailor-made noise model, we show that the distributed model-reference control problem can be solved directly from data. Sufficient conditions are provided for which the local controller estimates are consistent. Moreover, it is shown how the method can be applied in the single-input-single-output case, leading to consistent estimates with standard virtual reference feedback tuning as well. The effectiveness of the method is demonstrated via a small network example with two interconnected systems.",

keywords = "Data-driven control, Distributed control, Dynamic networks, System identification",

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note = "Funding Information: Funded by the European Research Council (ERC), Advanced Research Grant SYSDYNET, under the European Unions Horizon 2020 research and innovation programme (grant agreement No. 694504). ; 19th IFAC Symposium on System Identification (SYSID 2021), SYSID 2021 ; Conference date: 13-07-2021 Through 16-07-2021",

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AU - Van den Hof, Paul M.J.

AU - Lazar, Mircea

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PY - 2021/7/1

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N2 - The data-driven synthesis of a distributed controller in the presence of noise is considered, via the distributed virtual reference feedback tuning (DVRFT) framework. The analysis is performed for a linear interconnected system on an arbitrary graph that is subject to unmeasured exogenous inputs. By solving a dynamic network identification problem with prediction-error filtering and a tailor-made noise model, we show that the distributed model-reference control problem can be solved directly from data. Sufficient conditions are provided for which the local controller estimates are consistent. Moreover, it is shown how the method can be applied in the single-input-single-output case, leading to consistent estimates with standard virtual reference feedback tuning as well. The effectiveness of the method is demonstrated via a small network example with two interconnected systems.

AB - The data-driven synthesis of a distributed controller in the presence of noise is considered, via the distributed virtual reference feedback tuning (DVRFT) framework. The analysis is performed for a linear interconnected system on an arbitrary graph that is subject to unmeasured exogenous inputs. By solving a dynamic network identification problem with prediction-error filtering and a tailor-made noise model, we show that the distributed model-reference control problem can be solved directly from data. Sufficient conditions are provided for which the local controller estimates are consistent. Moreover, it is shown how the method can be applied in the single-input-single-output case, leading to consistent estimates with standard virtual reference feedback tuning as well. The effectiveness of the method is demonstrated via a small network example with two interconnected systems.

KW - Data-driven control

KW - Distributed control

KW - Dynamic networks

KW - System identification

UR - https://www.scopus.com/pages/publications/85118103450

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DO - 10.1016/j.ifacol.2021.08.359

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T2 - 19th IFAC Symposium on System Identification (SYSID 2021)

Y2 - 13 July 2021 through 16 July 2021

ER -

Handling unmeasured disturbances in data-driven distributed control with virtual reference feedback tuning

Abstract

Bibliographical note

Funding

Keywords

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