Model-Free Predictive Current Control of a Voltage Source Inverter based on Identification Algorithm

Rasool Heydari; Héctor A. Young; Zahra Rafiee; Freddy Flores-Bahamonde; Mehdi Savaghebi; José Rodríguez

doi:10.1109/IECON43393.2020.9254834

Model-Free Predictive Current Control of a Voltage Source Inverter based on Identification Algorithm

Rasool Heydari
, Héctor A. Young
, Zahra Rafiee
, Freddy Flores-Bahamonde
, Mehdi Savaghebi
, José Rodríguez

University of the Frontier
Shahid Beheshti University
Andrés Bello National University

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

The behavior of Model Predictive Control (MPC) is by principle affected by the quality of the model used for the controlled system. A parameter mismatch between the plant and the controller can affect drastically the performance of MPC. This paper presents a new strategy called model-free predictive control (MF-PC) to overcome these problems. In this approach, a recursive least squares algorithm is implemented to identify the parameters of an auto-regressive with exogenous input (ARX) model, using input and output measurements of the controlled system. The proposed method enables an accurate prediction of the controlled variables without requiring detailed knowledge about the physical system. Simulation results obtained for the current control of a two-level, three-phase voltage source inverter, demonstrate that the MF-PC is exceptionally robust against the parameter variations and the model uncertainties, compared to conventional finite-control-set MPC.

Original language	English
Title of host publication	Proceedings - IECON 2020 : 46th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE
Publication date	18. Nov 2020
Pages	3065-3070
ISBN (Print)	978-1-7281-5415-2
ISBN (Electronic)	978-1-7281-5414-5
DOIs	https://doi.org/10.1109/IECON43393.2020.9254834
Publication status	Published - 18. Nov 2020
Event	The 46th Annual Conference of the IEEE Industrial Electronics Society - Singapore, Singapore, Singapore Duration: 18. Oct 2020 → 21. Oct 2020

Conference

Conference	The 46th Annual Conference of the IEEE Industrial Electronics Society
Location	Singapore
Country/Territory	Singapore
City	Singapore
Period	18/10/2020 → 21/10/2020

Series	Proceedings of the Annual Conference of the IEEE Industrial Electronics Society
ISSN	1553-572X

Keywords

Model-free predictive control
auto-regressive with exogenous input (ARX)
model identification
model predictive control (MPC)
robustness
voltage source inverter

Documents & Links

10.1109/IECON43393.2020.9254834

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Cite this

@inproceedings{13fe3975250145b894de8e0dbefe0c14,

title = "Model-Free Predictive Current Control of a Voltage Source Inverter based on Identification Algorithm",

abstract = "The behavior of Model Predictive Control (MPC) is by principle affected by the quality of the model used for the controlled system. A parameter mismatch between the plant and the controller can affect drastically the performance of MPC. This paper presents a new strategy called model-free predictive control (MF-PC) to overcome these problems. In this approach, a recursive least squares algorithm is implemented to identify the parameters of an auto-regressive with exogenous input (ARX) model, using input and output measurements of the controlled system. The proposed method enables an accurate prediction of the controlled variables without requiring detailed knowledge about the physical system. Simulation results obtained for the current control of a two-level, three-phase voltage source inverter, demonstrate that the MF-PC is exceptionally robust against the parameter variations and the model uncertainties, compared to conventional finite-control-set MPC.",

keywords = "Model-free predictive control, auto-regressive with exogenous input (ARX), model identification, model predictive control (MPC), robustness, voltage source inverter",

author = "Rasool Heydari and Young, \{H{\'e}ctor A.\} and Zahra Rafiee and Freddy Flores-Bahamonde and Mehdi Savaghebi and Jos{\'e} Rodr{\'i}guez",

year = "2020",

month = nov,

day = "18",

doi = "10.1109/IECON43393.2020.9254834",

language = "English",

isbn = "978-1-7281-5415-2",

series = "Proceedings of the Annual Conference of the IEEE Industrial Electronics Society",

publisher = "IEEE",

pages = "3065--3070",

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address = "United States",

note = "The 46th Annual Conference of the IEEE Industrial Electronics Society<br/>, IECON 2020 ; Conference date: 18-10-2020 Through 21-10-2020",

}

Heydari, R, Young, HA, Rafiee, Z, Flores-Bahamonde, F, Savaghebi, M & Rodríguez, J 2020, Model-Free Predictive Current Control of a Voltage Source Inverter based on Identification Algorithm. in Proceedings - IECON 2020: 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE, Proceedings of the Annual Conference of the IEEE Industrial Electronics Society, pp. 3065-3070, The 46th Annual Conference of the IEEE Industrial Electronics Society
, Singapore, Singapore, 18/10/2020. https://doi.org/10.1109/IECON43393.2020.9254834

Model-Free Predictive Current Control of a Voltage Source Inverter based on Identification Algorithm. / Heydari, Rasool; Young, Héctor A.; Rafiee, Zahra et al.
Proceedings - IECON 2020: 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2020. p. 3065-3070 (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Model-Free Predictive Current Control of a Voltage Source Inverter based on Identification Algorithm

AU - Heydari, Rasool

AU - Young, Héctor A.

AU - Rafiee, Zahra

AU - Flores-Bahamonde, Freddy

AU - Savaghebi, Mehdi

AU - Rodríguez, José

PY - 2020/11/18

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N2 - The behavior of Model Predictive Control (MPC) is by principle affected by the quality of the model used for the controlled system. A parameter mismatch between the plant and the controller can affect drastically the performance of MPC. This paper presents a new strategy called model-free predictive control (MF-PC) to overcome these problems. In this approach, a recursive least squares algorithm is implemented to identify the parameters of an auto-regressive with exogenous input (ARX) model, using input and output measurements of the controlled system. The proposed method enables an accurate prediction of the controlled variables without requiring detailed knowledge about the physical system. Simulation results obtained for the current control of a two-level, three-phase voltage source inverter, demonstrate that the MF-PC is exceptionally robust against the parameter variations and the model uncertainties, compared to conventional finite-control-set MPC.

AB - The behavior of Model Predictive Control (MPC) is by principle affected by the quality of the model used for the controlled system. A parameter mismatch between the plant and the controller can affect drastically the performance of MPC. This paper presents a new strategy called model-free predictive control (MF-PC) to overcome these problems. In this approach, a recursive least squares algorithm is implemented to identify the parameters of an auto-regressive with exogenous input (ARX) model, using input and output measurements of the controlled system. The proposed method enables an accurate prediction of the controlled variables without requiring detailed knowledge about the physical system. Simulation results obtained for the current control of a two-level, three-phase voltage source inverter, demonstrate that the MF-PC is exceptionally robust against the parameter variations and the model uncertainties, compared to conventional finite-control-set MPC.

KW - Model-free predictive control

KW - auto-regressive with exogenous input (ARX)

KW - model identification

KW - model predictive control (MPC)

KW - robustness

KW - voltage source inverter

U2 - 10.1109/IECON43393.2020.9254834

DO - 10.1109/IECON43393.2020.9254834

M3 - Article in proceedings

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T3 - Proceedings of the Annual Conference of the IEEE Industrial Electronics Society

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ER -

Heydari R, Young HA, Rafiee Z, Flores-Bahamonde F, Savaghebi M, Rodríguez J. Model-Free Predictive Current Control of a Voltage Source Inverter based on Identification Algorithm. In Proceedings - IECON 2020: 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE. 2020. p. 3065-3070. (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society). doi: 10.1109/IECON43393.2020.9254834

Model-Free Predictive Current Control of a Voltage Source Inverter based on Identification Algorithm

Abstract

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Keywords

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