Computationally Efficient Finite-Set Model Predictive Current Control of Interior Permanent Magnet Synchronous Motors with Model-Based Online Inductance Estimation

Issa Hammoud; Sebastian Hentzelt; Thimo Oehlschlaegel; Ralph Kennel

doi:10.1109/CPERE45374.2019.8980058

Computationally Efficient Finite-Set Model Predictive Current Control of Interior Permanent Magnet Synchronous Motors with Model-Based Online Inductance Estimation

Issa Hammoud, Sebastian Hentzelt, Thimo Oehlschlaegel, Ralph Kennel

Chair of High-Power Converter Systems

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

7 Scopus citations

Abstract

In this paper, solutions to the main drawbacks of the traditional finite-set model predictive current control (FS-MPCC) of interior permanent magnet synchronous motors (IPMSM) are proposed. These drawbacks are: high computational load and high sensitivity to any model mismatch. The proposed computationally efficient FS-MPCC is based on finding the optimal voltage vector (VV) that would enhance the flow of the desired currents analytically. Based on its location in the stationary α-β plane, only three iterations of a modified cost function will be needed. Furthermore, a novel model-based online inductance estimation technique is proposed to enhance the robustness of the controller against model mismatch.

Original language	English
Title of host publication	IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	290-295
Number of pages	6
ISBN (Electronic)	9781728109107
DOIs	https://doi.org/10.1109/CPERE45374.2019.8980058
State	Published - Oct 2019
Event	2019 IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019 - Aswan City, Egypt Duration: 23 Oct 2019 → 25 Oct 2019

Publication series

Name	IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019

Conference

Conference	2019 IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019
Country/Territory	Egypt
City	Aswan City
Period	23/10/19 → 25/10/19

Keywords

Model predictive current control
electrical drives
interior permenant magnet synchronus motor
model-based inductance estimation
online optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CPERE45374.2019.8980058

Cite this

Hammoud, I., Hentzelt, S., Oehlschlaegel, T., & Kennel, R. (2019). Computationally Efficient Finite-Set Model Predictive Current Control of Interior Permanent Magnet Synchronous Motors with Model-Based Online Inductance Estimation. In IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019 (pp. 290-295). Article 8980058 (IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CPERE45374.2019.8980058

Hammoud, Issa ; Hentzelt, Sebastian ; Oehlschlaegel, Thimo et al. / Computationally Efficient Finite-Set Model Predictive Current Control of Interior Permanent Magnet Synchronous Motors with Model-Based Online Inductance Estimation. IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 290-295 (IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019).

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title = "Computationally Efficient Finite-Set Model Predictive Current Control of Interior Permanent Magnet Synchronous Motors with Model-Based Online Inductance Estimation",

abstract = "In this paper, solutions to the main drawbacks of the traditional finite-set model predictive current control (FS-MPCC) of interior permanent magnet synchronous motors (IPMSM) are proposed. These drawbacks are: high computational load and high sensitivity to any model mismatch. The proposed computationally efficient FS-MPCC is based on finding the optimal voltage vector (VV) that would enhance the flow of the desired currents analytically. Based on its location in the stationary α-β plane, only three iterations of a modified cost function will be needed. Furthermore, a novel model-based online inductance estimation technique is proposed to enhance the robustness of the controller against model mismatch.",

keywords = "Model predictive current control, electrical drives, interior permenant magnet synchronus motor, model-based inductance estimation, online optimization",

author = "Issa Hammoud and Sebastian Hentzelt and Thimo Oehlschlaegel and Ralph Kennel",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019 ; Conference date: 23-10-2019 Through 25-10-2019",

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Hammoud, I, Hentzelt, S, Oehlschlaegel, T & Kennel, R 2019, Computationally Efficient Finite-Set Model Predictive Current Control of Interior Permanent Magnet Synchronous Motors with Model-Based Online Inductance Estimation. in IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019., 8980058, IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019, Institute of Electrical and Electronics Engineers Inc., pp. 290-295, 2019 IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019, Aswan City, Egypt, 23/10/19. https://doi.org/10.1109/CPERE45374.2019.8980058

Computationally Efficient Finite-Set Model Predictive Current Control of Interior Permanent Magnet Synchronous Motors with Model-Based Online Inductance Estimation. / Hammoud, Issa; Hentzelt, Sebastian; Oehlschlaegel, Thimo et al.
IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 290-295 8980058 (IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019).

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

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AU - Kennel, Ralph

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N2 - In this paper, solutions to the main drawbacks of the traditional finite-set model predictive current control (FS-MPCC) of interior permanent magnet synchronous motors (IPMSM) are proposed. These drawbacks are: high computational load and high sensitivity to any model mismatch. The proposed computationally efficient FS-MPCC is based on finding the optimal voltage vector (VV) that would enhance the flow of the desired currents analytically. Based on its location in the stationary α-β plane, only three iterations of a modified cost function will be needed. Furthermore, a novel model-based online inductance estimation technique is proposed to enhance the robustness of the controller against model mismatch.

AB - In this paper, solutions to the main drawbacks of the traditional finite-set model predictive current control (FS-MPCC) of interior permanent magnet synchronous motors (IPMSM) are proposed. These drawbacks are: high computational load and high sensitivity to any model mismatch. The proposed computationally efficient FS-MPCC is based on finding the optimal voltage vector (VV) that would enhance the flow of the desired currents analytically. Based on its location in the stationary α-β plane, only three iterations of a modified cost function will be needed. Furthermore, a novel model-based online inductance estimation technique is proposed to enhance the robustness of the controller against model mismatch.

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KW - electrical drives

KW - interior permenant magnet synchronus motor

KW - model-based inductance estimation

KW - online optimization

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Hammoud I, Hentzelt S, Oehlschlaegel T, Kennel R. Computationally Efficient Finite-Set Model Predictive Current Control of Interior Permanent Magnet Synchronous Motors with Model-Based Online Inductance Estimation. In IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 290-295. 8980058. (IEEE Conference on Power Electronics and Renewable Energy, CPERE 2019). doi: 10.1109/CPERE45374.2019.8980058

Computationally Efficient Finite-Set Model Predictive Current Control of Interior Permanent Magnet Synchronous Motors with Model-Based Online Inductance Estimation

Abstract

Publication series

Conference

Keywords

UN SDGs

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