Integration of an Empirical Mode Decomposition Algorithm With Iterative Learning Control for High-Precision Machining

In this paper, a novel algorithm (ILC-EMD) that integrates iterative learning control (ILC) with empirical mode decomposition (EMD) is proposed to improve learning process. To explain the divergence behavior under the conventional ILC, the EMD is utilized to decompose the tracking error signal into...

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Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics 2013-06, Vol.18 (3), p.878-886
Main Authors: Tsai, Meng-Shiun, Yen, Chung-Liang, Yau, Hong-Tzong
Format: Article
Language:English
Subjects:
Algorithms
Amplification
Bandwidth
Convergence
Decomposition
Empirical analysis
Empirical mode decomposition (EMD)
Frequency domain analysis
intrinsic mode function (IMF)
iterative learning control (ILC)
Learning
Machine tools
Mechatronics
Nonuniform
resonance
Servomotors
Studies
Tracking errors
Transfer functions
zero-phase filtering
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Summary:In this paper, a novel algorithm (ILC-EMD) that integrates iterative learning control (ILC) with empirical mode decomposition (EMD) is proposed to improve learning process. To explain the divergence behavior under the conventional ILC, the EMD is utilized to decompose the tracking error signal into 11 intrinsic mode functions (IMFs). By observing the root mean square and the correlation values of the IMFs during iterations, the first IMF is determined to be the undesired signal which could not be reduced by learning process. Furthermore, the command containing the first IMF could further excite the machine tool due to the resonance effects and cause the amplification of the error signal. The ILC-EMD can filter out the undesired signal and prevent the amplification effect. Experimental results on tracking the butterfly and dragon nonuniform rational B-spline curves validate the effectiveness of the ILC-EMD algorithm.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2012.2194162