Decoupling control of bearingless brushless DC motor using particle swarm optimized neural network inverse system-Reference-Cited by-同舟云学术

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Decoupling control of bearingless brushless DC motor using particle swarm optimized neural network inverse system

Published:2024-02 Issue: Volume:31 Page:100952
ISSN:2665-9174
Container-title:Measurement: Sensors
language:en
Short-container-title:Measurement: Sensors

Author:

Tao Tao,Hua Lianghao

Funder

Jiangsu Province Natural Science Foundation

University Natural Science Research Project of Anhui Province

Science and Technology Plan Project of Wenzhou Municipality

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanics of Materials,Electronic, Optical and Magnetic Materials

Reference34 articles.

1. Mathematical modelling and control of bearingless brushless direct current machine with motor and generator double modes for flywheel battery;Liu;IET Power Electron.,2022

2. Optimal design of a multi-phase double-stator bearingless brushless direct current motor;Qin;Adv. Mech. Eng.,2017

3. Control of Out-Rotor Bearingless Brushless DC Motor;Sun,2017

4. Out Rotor Bearingless Brushless DC Motor for Flywheel Energy Storage;Sun,2017

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1. Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids;E3S Web of Conferences;2024

2. Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids;E3S Web of Conferences;2024

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