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Full paper-ICEMS 2023+1447+Yuefei Zuo.pdf (595.25 kB)

Frequency adaptive torque ripple suppression for electrical drives using radial basis function neural network

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posted on 2024-02-19, 10:59 authored by Yuefei ZuoYuefei Zuo, Jian An Tan, Chenhao Zhao, Huanzhi Wang, Christopher H. T. Lee, Jun YangJun Yang
Torque ripple suppression in electric drives employing a resonant controller or observer involves harmonic frequency knowledge. The frequency-locked loop technique can be used to achieve frequency adaptive control, however performance suffers when the system contains numerous harmonics. In this paper, a radial basis function neural network (RBFNN) is used to achieve frequency adaptive torque ripple suppression for electric drives. The RBFNN is combined with active disturbance rejection control (ADRC) to provide good rejection properties for both constant and ripple disturbances. Unlike the ADRC system based on RBFNN with offline learning, the suggested method can update the weights vector online, considerably improving system robustness and flexibility. Various experiments are carried out to validate the effectiveness of the proposed method.

Funding

Modulator-free Performance-Oriented Control (MfPOC) for Direct Electric Drives

Engineering and Physical Sciences Research Council

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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

2023 26th International Conference on Electrical Machines and Systems (ICEMS)

Pages

5209 - 5214

Source

2023 26th International Conference on Electrical Machines and Systems (ICEMS)

Publisher

IEEE

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2023-12-14

Copyright date

2023

ISBN

9798350317589; 9798350317596

eISSN

2642-5513

Language

  • en

Location

Zhuhai, China

Event dates

5th November 2023 - 8th November 2023

Depositor

Dr Jun Yang. Deposit date: 16 February 2024

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