Optimal commutation for switched reluctance motors using Gaussian Process regression

Max van Meer; Gert Witvoet; Tom A.E. Oomen

doi:10.1016/j.ifacol.2022.11.201

Optimal commutation for switched reluctance motors using Gaussian Process regression

Max van Meer, Gert Witvoet, Tom A.E. Oomen

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

2 Citations (Scopus)

94 Downloads (Pure)

Abstract

Switched reluctance motors are appealing because they are inexpensive in both construction and maintenance. The aim of this paper is to develop a commutation function that linearizes the nonlinear motor dynamics in such a way that the torque ripple is reduced. To this end, a convex optimization problem is posed that directly penalizes torque ripple in between samples, as well as power consumption, and Gaussian Process regression is used to obtain a continuous commutation function. The resulting function is fundamentally different from conventional commutation functions, and closed-loop simulations show significant reduction of the error. The results offer a new perspective on suitable commutation functions for accurate control of reluctance motors.

Original language	English
Pages (from-to)	302-307
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	55
Issue number	37
DOIs	https://doi.org/10.1016/j.ifacol.2022.11.201
Publication status	Published - 31 Oct 2022
Event	2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States Duration: 2 Oct 2022 → 5 Oct 2022

Keywords

Convex optimization
Feedback Control
Linearization
Nonparametric methods
Static optimization problems
Switched Reluctance Motor

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title = "Optimal commutation for switched reluctance motors using Gaussian Process regression",

abstract = "Switched reluctance motors are appealing because they are inexpensive in both construction and maintenance. The aim of this paper is to develop a commutation function that linearizes the nonlinear motor dynamics in such a way that the torque ripple is reduced. To this end, a convex optimization problem is posed that directly penalizes torque ripple in between samples, as well as power consumption, and Gaussian Process regression is used to obtain a continuous commutation function. The resulting function is fundamentally different from conventional commutation functions, and closed-loop simulations show significant reduction of the error. The results offer a new perspective on suitable commutation functions for accurate control of reluctance motors.",

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AU - van Meer, Max

AU - Witvoet, Gert

AU - Oomen, Tom A.E.

PY - 2022/10/31

Y1 - 2022/10/31

N2 - Switched reluctance motors are appealing because they are inexpensive in both construction and maintenance. The aim of this paper is to develop a commutation function that linearizes the nonlinear motor dynamics in such a way that the torque ripple is reduced. To this end, a convex optimization problem is posed that directly penalizes torque ripple in between samples, as well as power consumption, and Gaussian Process regression is used to obtain a continuous commutation function. The resulting function is fundamentally different from conventional commutation functions, and closed-loop simulations show significant reduction of the error. The results offer a new perspective on suitable commutation functions for accurate control of reluctance motors.

AB - Switched reluctance motors are appealing because they are inexpensive in both construction and maintenance. The aim of this paper is to develop a commutation function that linearizes the nonlinear motor dynamics in such a way that the torque ripple is reduced. To this end, a convex optimization problem is posed that directly penalizes torque ripple in between samples, as well as power consumption, and Gaussian Process regression is used to obtain a continuous commutation function. The resulting function is fundamentally different from conventional commutation functions, and closed-loop simulations show significant reduction of the error. The results offer a new perspective on suitable commutation functions for accurate control of reluctance motors.

KW - Convex optimization

KW - Feedback Control

KW - Linearization

KW - Nonparametric methods

KW - Static optimization problems

KW - Switched Reluctance Motor

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DO - 10.1016/j.ifacol.2022.11.201

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VL - 55

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EP - 307

JO - IFAC-PapersOnLine

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T2 - 2nd Modeling, Estimation and Control Conference, MECC 2022

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Optimal commutation for switched reluctance motors using Gaussian Process regression

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