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Rate-dependent hysteresis modeling and compensation using least squares support vector machines
Xu Q.; Wong, Pak Kin; Li Y.
2011-06-06
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Pages85-93
Abstract

This paper is concentrated on the rate-dependent hysteresis modeling and compensation for a piezoelectric actuator. A least squares support vector machines (LS-SVM) model is proposed and trained by introducing the current input value and input variation rate as the input data set to formulate a one-to-one mapping. After demonstrating the effectiveness of the presented model, a LS-SVM inverse model based feedforward control combined with a PID feedback control is designed to compensate the hysteresis nonlinearity. Simulation results show that the hybrid scheme is superior to either of the stand-alone controllers, and the rate-dependent hysteresis is suppressed to a negligible level, which validate the effectiveness of the constructed controller. Owing to the simple procedure, the proposed modeling and control approaches are expected to be extended to other types of hysteretic systems as well. © 2011 Springer-Verlag.

KeywordHysteresis Least Squares Support Vector Machines (Ls-svm) Motion Control Piezoelectric Actuator
DOI10.1007/978-3-642-21090-7_10
URLView the original
Language英語English
Volume6676
IssuePart 2
Indexed BySCIE
WOS IDWOS:000301950800010
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS Research AreaComputer Science
Scopus ID2-s2.0-79957810475
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Citation statistics
Document TypeBook chapter
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Corresponding AuthorXu Q.
AffiliationUniversity of Macau
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Xu Q.,Wong, Pak Kin,Li Y.. Rate-dependent hysteresis modeling and compensation using least squares support vector machines[M]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics):Springer, 2011, 85-93.
APA Xu Q.., Wong, Pak Kin., & Li Y. (2011). Rate-dependent hysteresis modeling and compensation using least squares support vector machines. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6676(Part 2), 85-93.
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