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An Improved Sliding‐Mode Repetitive Learning Control Scheme Using Wavelet Transform
Author(s) -
Lu YuSheng,
Wu BingXuan,
Lien ShuFen
Publication year - 2012
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.433
Subject(s) - wavelet , second generation wavelet transform , wavelet transform , lifting scheme , discrete wavelet transform , signal (programming language) , computer science , stationary wavelet transform , wavelet packet decomposition , harmonic wavelet transform , process (computing) , fast wavelet transform , compensation (psychology) , artificial intelligence , control theory (sociology) , algorithm , speech recognition , pattern recognition (psychology) , control (management) , programming language , operating system , psychology , psychoanalysis
This paper presents an enhanced sliding‐mode repetitive learning control (SMRLC) scheme using the wavelet transform. Distinct from previous wavelet transform‐based repetitive control schemes, the proposed SMRLC learns from a switching signal that is equivalent to the compensation error of the SMRLC, thereby speeding up the learning process. The wavelet analysis is employed to decompose the switching signal, capture its low‐frequency components effectively, and synthesize a de‐noised, high‐scale signal for the learning process. Experimental study on a directly driven Hoekens straight‐line mechanism is conducted, and the proposed wavelet transform‐based SMRLC is experimentally compared with a Fourier series‐based SMRLC. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society