Low Complexity Model Predictive Flux Control Based on Discrete Space Vector Modulation and Optimal Switching Sequence for Induction Motors-Reference-Cited by-同舟云学术

Low Complexity Model Predictive Flux Control Based on Discrete Space Vector Modulation and Optimal Switching Sequence for Induction Motors

Published:2024-01 Issue:1 Volume:71 Page:305-315
ISSN:0278-0046
Container-title:IEEE Transactions on Industrial Electronics
language:
Short-container-title:IEEE Trans. Ind. Electron.

Author:

Jin Tao¹^ORCID,Song Huiqing¹^ORCID,Ipoum-Ngome Paul Gistain²^ORCID,Mon-Nzongo Daniel Legrand¹^ORCID,Tang Jinquan²,Zhu Minlong¹^ORCID,Rodriguez Jose³^ORCID

Affiliation:

1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China

2. Pearl Electric Company, Ltd., Guangzhou, China

3. Faculty of Engineering, Universidad San Sebastian Santiago, Santiago, Chile

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Agencia Nacional de Investigación y Desarrollo

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/41/10184152/10049777.pdf?arnumber=10049777

Reference29 articles.

1. Model Predictive Control—A Simple and Powerful Method to Control Power Converters

2. Optimal finite state predictive direct torque control without weighting factors for motor drive applications

3. Adaptive Sliding-Mode-Based Speed Control in Finite Control Set Model Predictive Torque Control for Induction Motors

4. K-Best Sphere Decoding Algorithm for Long Prediction Horizon FCS-MPC

5. Predictive Torque Control of Induction Machines Based on State-Space Models

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