ARMA-based Model-Free Two-Degree-of-Freedom Predictive Control Strategy for SPMSM Drives

Yao Wei; Dongliang Ke; Hector Young; Fengxiang Wang; José Rodríguez

doi:10.1109/PRECEDE57319.2023.10174573

ARMA-based Model-Free Two-Degree-of-Freedom Predictive Control Strategy for SPMSM Drives

Yao Wei, Dongliang Ke, Hector Young, Fengxiang Wang^*, José Rodríguez

^*Corresponding author for this work

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

Abstract

To realize collaborative improvement for the inner controller and outer controller, and improve control performances suited for high-end applications for model-free predictive control, in the surface-mounted permanent magnet synchronous motor (SPMSM) driving system, an autoregressive moving average (ARMA) -based model-free two-degree-of-freedom PCC (MF2DoF-PCC) strategy is proposed in this paper. The motor is online fitted as an ARMA model, in which the coefficients are updated by the recursive gradient correction (RGC) algorithm. A secondary controller is designed based on the coefficients of the ARMA model to decouple the dynamics, and the suitable orders of the model are also determined. According to the simulation and experimental results, the effectiveness of the proposed method is demonstrated, as well as the advantages including the improved dynamics and current quality with suitable robustness.

Original language	English
Title of host publication	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350396867
ISBN (Print)	9798350396867
DOIs	https://doi.org/10.1109/PRECEDE57319.2023.10174573
State	Published - 2023
Event	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023 - Wuhan, China Duration: 2023 → 2023

Publication series

Name	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023

Conference

Conference	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
Country/Territory	China
City	Wuhan
Period	16/06/23 → 19/06/23

Bibliographical note

Funding Information:
This work was supported in part by National Natural Science Funds of China under Grant 52277070, in part by the Science and Technology Program of Fujian Province 2020T3003, in part by the Science and Technology Plan Project of Fujian Province under Grant 2021I0039, in part by the Science and Technology Plan Project of Fujian Province under Grant 2021T3064, and in part by the Science and Technology Plan Project of Fujian Province under Grant 2021T3035. J. Rodriguez acknowledges the support of ANID through projects FB0008, 1210208 and 1221293.

Publisher Copyright:
© 2023 IEEE.

ASJC Scopus subject areas

Control and Optimization
Modeling and Simulation
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Mechanical Engineering
Safety, Risk, Reliability and Quality

Access to Document

10.1109/PRECEDE57319.2023.10174573

Cite this

Wei, Y., Ke, D., Young, H., Wang, F., & Rodríguez, J. (2023). ARMA-based Model-Free Two-Degree-of-Freedom Predictive Control Strategy for SPMSM Drives. In 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023 (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PRECEDE57319.2023.10174573

Wei, Yao ; Ke, Dongliang ; Young, Hector et al. / ARMA-based Model-Free Two-Degree-of-Freedom Predictive Control Strategy for SPMSM Drives. 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023).

@inproceedings{6316c2e7d770412f8ce853f35e501b60,

title = "ARMA-based Model-Free Two-Degree-of-Freedom Predictive Control Strategy for SPMSM Drives",

abstract = "To realize collaborative improvement for the inner controller and outer controller, and improve control performances suited for high-end applications for model-free predictive control, in the surface-mounted permanent magnet synchronous motor (SPMSM) driving system, an autoregressive moving average (ARMA) -based model-free two-degree-of-freedom PCC (MF2DoF-PCC) strategy is proposed in this paper. The motor is online fitted as an ARMA model, in which the coefficients are updated by the recursive gradient correction (RGC) algorithm. A secondary controller is designed based on the coefficients of the ARMA model to decouple the dynamics, and the suitable orders of the model are also determined. According to the simulation and experimental results, the effectiveness of the proposed method is demonstrated, as well as the advantages including the improved dynamics and current quality with suitable robustness.",

keywords = "Model-free two-degree-of-freedom predictive control, autoregressive moving average model, recursive gradient correction algorithm",

author = "Yao Wei and Dongliang Ke and Hector Young and Fengxiang Wang and Jos{\'e} Rodr{\'i}guez",

note = "Funding Information: This work was supported in part by National Natural Science Funds of China under Grant 52277070, in part by the Science and Technology Program of Fujian Province 2020T3003, in part by the Science and Technology Plan Project of Fujian Province under Grant 2021I0039, in part by the Science and Technology Plan Project of Fujian Province under Grant 2021T3064, and in part by the Science and Technology Plan Project of Fujian Province under Grant 2021T3035. J. Rodriguez acknowledges the support of ANID through projects FB0008, 1210208 and 1221293. Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023 ; Conference date: 16-06-2023 Through 19-06-2023",

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doi = "10.1109/PRECEDE57319.2023.10174573",

language = "English",

isbn = "9798350396867",

series = "2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023",

}

Wei, Y, Ke, D, Young, H, Wang, F & Rodríguez, J 2023, ARMA-based Model-Free Two-Degree-of-Freedom Predictive Control Strategy for SPMSM Drives. in 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023, Wuhan, China, 16/06/23. https://doi.org/10.1109/PRECEDE57319.2023.10174573

ARMA-based Model-Free Two-Degree-of-Freedom Predictive Control Strategy for SPMSM Drives. / Wei, Yao; Ke, Dongliang; Young, Hector et al.
2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023).

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

TY - GEN

T1 - ARMA-based Model-Free Two-Degree-of-Freedom Predictive Control Strategy for SPMSM Drives

AU - Wei, Yao

AU - Ke, Dongliang

AU - Young, Hector

AU - Wang, Fengxiang

AU - Rodríguez, José

N1 - Funding Information: This work was supported in part by National Natural Science Funds of China under Grant 52277070, in part by the Science and Technology Program of Fujian Province 2020T3003, in part by the Science and Technology Plan Project of Fujian Province under Grant 2021I0039, in part by the Science and Technology Plan Project of Fujian Province under Grant 2021T3064, and in part by the Science and Technology Plan Project of Fujian Province under Grant 2021T3035. J. Rodriguez acknowledges the support of ANID through projects FB0008, 1210208 and 1221293. Publisher Copyright: © 2023 IEEE.

PY - 2023

Y1 - 2023

N2 - To realize collaborative improvement for the inner controller and outer controller, and improve control performances suited for high-end applications for model-free predictive control, in the surface-mounted permanent magnet synchronous motor (SPMSM) driving system, an autoregressive moving average (ARMA) -based model-free two-degree-of-freedom PCC (MF2DoF-PCC) strategy is proposed in this paper. The motor is online fitted as an ARMA model, in which the coefficients are updated by the recursive gradient correction (RGC) algorithm. A secondary controller is designed based on the coefficients of the ARMA model to decouple the dynamics, and the suitable orders of the model are also determined. According to the simulation and experimental results, the effectiveness of the proposed method is demonstrated, as well as the advantages including the improved dynamics and current quality with suitable robustness.

AB - To realize collaborative improvement for the inner controller and outer controller, and improve control performances suited for high-end applications for model-free predictive control, in the surface-mounted permanent magnet synchronous motor (SPMSM) driving system, an autoregressive moving average (ARMA) -based model-free two-degree-of-freedom PCC (MF2DoF-PCC) strategy is proposed in this paper. The motor is online fitted as an ARMA model, in which the coefficients are updated by the recursive gradient correction (RGC) algorithm. A secondary controller is designed based on the coefficients of the ARMA model to decouple the dynamics, and the suitable orders of the model are also determined. According to the simulation and experimental results, the effectiveness of the proposed method is demonstrated, as well as the advantages including the improved dynamics and current quality with suitable robustness.

KW - Model-free two-degree-of-freedom predictive control

KW - autoregressive moving average model

KW - recursive gradient correction algorithm

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U2 - 10.1109/PRECEDE57319.2023.10174573

DO - 10.1109/PRECEDE57319.2023.10174573

M3 - Conference contribution

AN - SCOPUS:85166234788

SN - 9798350396867

T3 - 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023

BT - 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023

Y2 - 16 June 2023 through 19 June 2023

ER -

Wei Y, Ke D, Young H, Wang F, Rodríguez J. ARMA-based Model-Free Two-Degree-of-Freedom Predictive Control Strategy for SPMSM Drives. In 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023). doi: 10.1109/PRECEDE57319.2023.10174573

ARMA-based Model-Free Two-Degree-of-Freedom Predictive Control Strategy for SPMSM Drives

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ASJC Scopus subject areas

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