Data-based tuning of distinct inverse models used in feedforward and disturbance observer

This work proposes a data-based tuning method which is capable of tuning parameters of inverse models used in the feedforward controller and disturbance observer (DOB). Specifically, it aims to enhance the tracking performance of the positioning system by implementing different inverse models for fe...

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Vydáno v:IEEE Conference on Industrial Electronics and Applications (Online) s. 1 - 6
Hlavní autoři: Xu, Yifan, Chen, Silu, Halim, Dunant, Xu, Zhuang, Wang, Weizhen, Zhang, Chi
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 03.08.2025
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ISSN:2158-2297
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Abstract This work proposes a data-based tuning method which is capable of tuning parameters of inverse models used in the feedforward controller and disturbance observer (DOB). Specifically, it aims to enhance the tracking performance of the positioning system by implementing different inverse models for feedforward controller and DOB, allowing a data-based tuning method to find two sets of optimal parameters for both inverse models simultaneously. Real-time operational data are utilized to achieve the optimization of inverse model's parameters. Three experiments are required for the completion of single iteration in the presence of disturbances and noise. Compared to the tuning of uniform inverse model used in feedforward controller and DOB, the tuning of distinct inverse models for feedforward controller and DOB results in enhanced tracking performance. The effectiveness of the proposed method is verified through simulations.
AbstractList This work proposes a data-based tuning method which is capable of tuning parameters of inverse models used in the feedforward controller and disturbance observer (DOB). Specifically, it aims to enhance the tracking performance of the positioning system by implementing different inverse models for feedforward controller and DOB, allowing a data-based tuning method to find two sets of optimal parameters for both inverse models simultaneously. Real-time operational data are utilized to achieve the optimization of inverse model's parameters. Three experiments are required for the completion of single iteration in the presence of disturbances and noise. Compared to the tuning of uniform inverse model used in feedforward controller and DOB, the tuning of distinct inverse models for feedforward controller and DOB results in enhanced tracking performance. The effectiveness of the proposed method is verified through simulations.
Author Chen, Silu
Xu, Zhuang
Zhang, Chi
Wang, Weizhen
Halim, Dunant
Xu, Yifan
Author_xml – sequence: 1
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  surname: Xu
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  email: suxyx2@nottingham.edu.cn
  organization: University of Nottingham Ningbo China,Department of Mechanical, Materials and Manufacturing Engineering,Ningbo,China
– sequence: 2
  givenname: Silu
  surname: Chen
  fullname: Chen, Silu
  email: chensilu@nimte.ac.cn
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  givenname: Dunant
  surname: Halim
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  organization: University of Nottingham Ningbo China,Department of Mechanical, Materials and Manufacturing Engineering,Ningbo,China
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  givenname: Zhuang
  surname: Xu
  fullname: Xu, Zhuang
  email: john.xu@nottingham.edu.cn
  organization: University of Nottingham Ningbo China,Department of Electrical and Electronic Engineering,Ningbo,China
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  givenname: Weizhen
  surname: Wang
  fullname: Wang, Weizhen
  email: wangweizhen@nimte.ac.cn
  organization: Chinese Academy of Sciences,Ningbo Institute of Materials Technology and Engineering,Ningbo,China
– sequence: 6
  givenname: Chi
  surname: Zhang
  fullname: Zhang, Chi
  email: zhangchi@nimte.ac.cn
  organization: Chinese Academy of Sciences,Ningbo Institute of Materials Technology and Engineering,Ningbo,China
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Snippet This work proposes a data-based tuning method which is capable of tuning parameters of inverse models used in the feedforward controller and disturbance...
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SubjectTerms data-based tuning
disturbance oberserver
Disturbance observers
feedforward control
Feedforward systems
Inverse problems
Real-time systems
servo control
Simulation
Stability analysis
Systems modeling
Trajectory
Tuning
Title Data-based tuning of distinct inverse models used in feedforward and disturbance observer
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