Iterative Learning of Dynamic Inverse Filters for Feedforward Tracking Control

A novel method to construct dynamic inversion compensation is proposed for feedforward tracking control. In contrast to common approaches involving parametric system identification followed by inversion synthesis, in this article we apply iterative learning control to track an impulse signal, where...

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Bibliographic Details
Published inIEEE/ASME transactions on mechatronics Vol. 25; no. 1; pp. 349 - 359
Main Authors Chen, Cheng-Wei, Rai, Sandeep, Tsao, Tsu-Chin
Format Journal Article
LanguageEnglish
Published New York IEEE 01.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Aerodynamics
Bandwidth
Control systems
Control theory
Dynamic inversion
Electric motors
Feedforward control
Feedforward systems
Finite impulse response filters
inverse problems
iterative learning control
Learning
Magnetic bearings
MIMO communication
motion control
System identification
Time-domain analysis
Tracking
Tracking control
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Summary:A novel method to construct dynamic inversion compensation is proposed for feedforward tracking control. In contrast to common approaches involving parametric system identification followed by inversion synthesis, in this article we apply iterative learning control to track an impulse signal, where the converged control input is directly used to construct the inverse filter. The method is applicable to multivariable systems without a diagonalization process. The proposed method is implemented on a linear motor and an active magnetic bearing system, respectively. The experimental results are presented to demonstrate the feedforward tracking performance.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2019.2951150