Adaptive wavelet neural network friction compensation of mechanical systems

Shen Min Song; Zhuo Yi Song; Xing Lin Chen; Guangren Duan

doi:10.1007/11760023_166

Adaptive wavelet neural network friction compensation of mechanical systems

Shen Min Song^*
, Zhuo Yi Song
, Xing Lin Chen
, Guangren Duan

^*Corresponding author for this work

School of Astronautics, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Recently, based on multi-resolution analysis, wavelet neural networks (WNN) have been proposed as an alternative to NN for approximating arbitrary nonlinear functions in L²(R). Discontinuous friction function is an unavoidable nonlinear effect that can limit control performance in mechanical systems. In this paper, adaptive WNN is used to design a friction compensator for a single joint mechanical system. Then asymptotically stability of the system is assured by adding a PD controller and adaptive robust terms. The simulation results show the validity of the control scheme.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2006
Subtitle of host publication	Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II
Publisher	Springer Verlag
Pages	1131-1139
Number of pages	9
ISBN (Print)	3540344373, 9783540344377
DOIs	https://doi.org/10.1007/11760023_166
State	Published - 2006
Externally published	Yes
Event	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China Duration: 28 May 2006 → 1 Jun 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3972 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Country/Territory	China
City	Chengdu
Period	28/05/06 → 1/06/06

Access to Document

10.1007/11760023_166

Cite this

Song, S. M., Song, Z. Y., Chen, X. L., & Duan, G. (2006). Adaptive wavelet neural network friction compensation of mechanical systems. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II (pp. 1131-1139). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3972 LNCS). Springer Verlag. https://doi.org/10.1007/11760023_166

Song, Shen Min ; Song, Zhuo Yi ; Chen, Xing Lin et al. / Adaptive wavelet neural network friction compensation of mechanical systems. Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II. Springer Verlag, 2006. pp. 1131-1139 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{42e46c27dff24749a9b6796fe3bb12de,

title = "Adaptive wavelet neural network friction compensation of mechanical systems",

abstract = "Recently, based on multi-resolution analysis, wavelet neural networks (WNN) have been proposed as an alternative to NN for approximating arbitrary nonlinear functions in L2(R). Discontinuous friction function is an unavoidable nonlinear effect that can limit control performance in mechanical systems. In this paper, adaptive WNN is used to design a friction compensator for a single joint mechanical system. Then asymptotically stability of the system is assured by adding a PD controller and adaptive robust terms. The simulation results show the validity of the control scheme.",

author = "Song, \{Shen Min\} and Song, \{Zhuo Yi\} and Chen, \{Xing Lin\} and Guangren Duan",

year = "2006",

doi = "10.1007/11760023\_166",

language = "英语",

isbn = "3540344373",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1131--1139",

booktitle = "Advances in Neural Networks - ISNN 2006",

address = "德国",

note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",

}

Song, SM, Song, ZY, Chen, XL & Duan, G 2006, Adaptive wavelet neural network friction compensation of mechanical systems. in Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3972 LNCS, Springer Verlag, pp. 1131-1139, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, China, 28/05/06. https://doi.org/10.1007/11760023_166

Adaptive wavelet neural network friction compensation of mechanical systems. / Song, Shen Min; Song, Zhuo Yi; Chen, Xing Lin et al.
Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II. Springer Verlag, 2006. p. 1131-1139 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3972 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Adaptive wavelet neural network friction compensation of mechanical systems

AU - Song, Shen Min

AU - Song, Zhuo Yi

AU - Chen, Xing Lin

AU - Duan, Guangren

PY - 2006

Y1 - 2006

N2 - Recently, based on multi-resolution analysis, wavelet neural networks (WNN) have been proposed as an alternative to NN for approximating arbitrary nonlinear functions in L2(R). Discontinuous friction function is an unavoidable nonlinear effect that can limit control performance in mechanical systems. In this paper, adaptive WNN is used to design a friction compensator for a single joint mechanical system. Then asymptotically stability of the system is assured by adding a PD controller and adaptive robust terms. The simulation results show the validity of the control scheme.

AB - Recently, based on multi-resolution analysis, wavelet neural networks (WNN) have been proposed as an alternative to NN for approximating arbitrary nonlinear functions in L2(R). Discontinuous friction function is an unavoidable nonlinear effect that can limit control performance in mechanical systems. In this paper, adaptive WNN is used to design a friction compensator for a single joint mechanical system. Then asymptotically stability of the system is assured by adding a PD controller and adaptive robust terms. The simulation results show the validity of the control scheme.

UR - https://www.scopus.com/pages/publications/33745916742

U2 - 10.1007/11760023_166

DO - 10.1007/11760023_166

M3 - 会议稿件

AN - SCOPUS:33745916742

SN - 3540344373

SN - 9783540344377

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1131

EP - 1139

BT - Advances in Neural Networks - ISNN 2006

PB - Springer Verlag

T2 - 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

Y2 - 28 May 2006 through 1 June 2006

ER -

Song SM, Song ZY, Chen XL, Duan G. Adaptive wavelet neural network friction compensation of mechanical systems. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II. Springer Verlag. 2006. p. 1131-1139. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11760023_166

Adaptive wavelet neural network friction compensation of mechanical systems

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