Variable cost functions' sequence design for model predictive control of IPMSM without weighting factor

Xuezhu Mei; Ran Zu; Fengxiang Wang; Ralph Kennel

doi:10.1109/ICInfA.2018.8812388

Variable cost functions' sequence design for model predictive control of IPMSM without weighting factor

Xuezhu Mei
, Ran Zu
, Fengxiang Wang
, Ralph Kennel

Chair of High-Power Converter Systems

Chinese Academy of Sciences
Technical University of Munich

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

4 Scopus citations

Abstract

A model predictive control (MPC) based torque and flux control strategy with cascaded cost functions' optimization for electric drive systems is presented in this work. Based on the knowledge of previous step's cost functions' values, an intuitive decision-making logic for the sequence of cost function execution in an online self-adjustable manner is adopted. This eliminates the necessity of weighting factor tuning and calculation as in the conventional methods with a single aggregate cost function. The effectiveness and comparable overall performance of the proposed system are verified through simulations.

Original language	English
Title of host publication	2018 IEEE International Conference on Information and Automation, ICIA 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	500-505
Number of pages	6
ISBN (Electronic)	9781538680698
DOIs	https://doi.org/10.1109/ICInfA.2018.8812388
State	Published - Aug 2018
Event	2018 IEEE International Conference on Information and Automation, ICIA 2018 - Wuyishan, Fujian, China Duration: 11 Aug 2018 → 13 Aug 2018

Publication series

Name	2018 IEEE International Conference on Information and Automation, ICIA 2018

Conference

Conference	2018 IEEE International Conference on Information and Automation, ICIA 2018
Country/Territory	China
City	Wuyishan, Fujian
Period	11/08/18 → 13/08/18

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Cost function
Electric drives
Model predictive control

Access to Document

10.1109/ICInfA.2018.8812388

Cite this

Mei, X., Zu, R., Wang, F., & Kennel, R. (2018). Variable cost functions' sequence design for model predictive control of IPMSM without weighting factor. In 2018 IEEE International Conference on Information and Automation, ICIA 2018 (pp. 500-505). Article 8812388 (2018 IEEE International Conference on Information and Automation, ICIA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICInfA.2018.8812388

Mei, Xuezhu ; Zu, Ran ; Wang, Fengxiang et al. / Variable cost functions' sequence design for model predictive control of IPMSM without weighting factor. 2018 IEEE International Conference on Information and Automation, ICIA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 500-505 (2018 IEEE International Conference on Information and Automation, ICIA 2018).

@inproceedings{76e6202c801b403ba64b0bb73b4dc50f,

title = "Variable cost functions' sequence design for model predictive control of IPMSM without weighting factor",

abstract = "A model predictive control (MPC) based torque and flux control strategy with cascaded cost functions' optimization for electric drive systems is presented in this work. Based on the knowledge of previous step's cost functions' values, an intuitive decision-making logic for the sequence of cost function execution in an online self-adjustable manner is adopted. This eliminates the necessity of weighting factor tuning and calculation as in the conventional methods with a single aggregate cost function. The effectiveness and comparable overall performance of the proposed system are verified through simulations.",

keywords = "Cost function, Electric drives, Model predictive control",

author = "Xuezhu Mei and Ran Zu and Fengxiang Wang and Ralph Kennel",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Information and Automation, ICIA 2018 ; Conference date: 11-08-2018 Through 13-08-2018",

year = "2018",

month = aug,

doi = "10.1109/ICInfA.2018.8812388",

language = "English",

series = "2018 IEEE International Conference on Information and Automation, ICIA 2018",

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

pages = "500--505",

booktitle = "2018 IEEE International Conference on Information and Automation, ICIA 2018",

}

Mei, X, Zu, R, Wang, F & Kennel, R 2018, Variable cost functions' sequence design for model predictive control of IPMSM without weighting factor. in 2018 IEEE International Conference on Information and Automation, ICIA 2018., 8812388, 2018 IEEE International Conference on Information and Automation, ICIA 2018, Institute of Electrical and Electronics Engineers Inc., pp. 500-505, 2018 IEEE International Conference on Information and Automation, ICIA 2018, Wuyishan, Fujian, China, 11/08/18. https://doi.org/10.1109/ICInfA.2018.8812388

Variable cost functions' sequence design for model predictive control of IPMSM without weighting factor. / Mei, Xuezhu; Zu, Ran; Wang, Fengxiang et al.
2018 IEEE International Conference on Information and Automation, ICIA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 500-505 8812388 (2018 IEEE International Conference on Information and Automation, ICIA 2018).

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

TY - GEN

T1 - Variable cost functions' sequence design for model predictive control of IPMSM without weighting factor

AU - Mei, Xuezhu

AU - Zu, Ran

AU - Wang, Fengxiang

AU - Kennel, Ralph

PY - 2018/8

Y1 - 2018/8

N2 - A model predictive control (MPC) based torque and flux control strategy with cascaded cost functions' optimization for electric drive systems is presented in this work. Based on the knowledge of previous step's cost functions' values, an intuitive decision-making logic for the sequence of cost function execution in an online self-adjustable manner is adopted. This eliminates the necessity of weighting factor tuning and calculation as in the conventional methods with a single aggregate cost function. The effectiveness and comparable overall performance of the proposed system are verified through simulations.

AB - A model predictive control (MPC) based torque and flux control strategy with cascaded cost functions' optimization for electric drive systems is presented in this work. Based on the knowledge of previous step's cost functions' values, an intuitive decision-making logic for the sequence of cost function execution in an online self-adjustable manner is adopted. This eliminates the necessity of weighting factor tuning and calculation as in the conventional methods with a single aggregate cost function. The effectiveness and comparable overall performance of the proposed system are verified through simulations.

KW - Cost function

KW - Electric drives

KW - Model predictive control

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U2 - 10.1109/ICInfA.2018.8812388

DO - 10.1109/ICInfA.2018.8812388

M3 - Conference contribution

AN - SCOPUS:85072324868

T3 - 2018 IEEE International Conference on Information and Automation, ICIA 2018

SP - 500

EP - 505

BT - 2018 IEEE International Conference on Information and Automation, ICIA 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Information and Automation, ICIA 2018

Y2 - 11 August 2018 through 13 August 2018

ER -

Mei X, Zu R, Wang F, Kennel R. Variable cost functions' sequence design for model predictive control of IPMSM without weighting factor. In 2018 IEEE International Conference on Information and Automation, ICIA 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 500-505. 8812388. (2018 IEEE International Conference on Information and Automation, ICIA 2018). doi: 10.1109/ICInfA.2018.8812388

Variable cost functions' sequence design for model predictive control of IPMSM without weighting factor

Abstract

Publication series

Conference

UN SDGs

Keywords

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