Adaptive neural model based fault tolerant control for multi-variable process

Cuimei Bo; Jun Li; Zhiquan Wang; Jinguo Lin

doi:10.1007/11816171_72

Adaptive neural model based fault tolerant control for multi-variable process

Cuimei Bo, Jun Li, Zhiquan Wang, Jinguo Lin

COLLEGE OF ELECTRICAL ENGINEERING AND CONTROL SCIENCE

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

Abstract

A new FTC scheme based on adaptive radial basis function (RBF) neural network (NN) model for unknown multi-variable dynamic systems is proposed. The scheme designs an adaptive RBF model to built process model and uses extended Kalman filter (EKF) technique to online learn the fault dynamics. Then, a model inversion controller is designed to produce the fault tolerant control (FTC) actions. The proposed scheme is applied to a three-tank process to evaluate the performance of the scheme. The simulation results show that component fault can be quickly compensated so that the system performances are recovered well.

Original language	English
Title of host publication	Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings
Publisher	Springer Verlag
Pages	596-601
Number of pages	6
ISBN (Print)	3540372741, 9783540372745
DOIs	https://doi.org/10.1007/11816171_72
State	Published - 2006
Event	International Conference on Intelligent Computing, ICIC 2006 - Kunming, China Duration: 16 Aug 2006 → 19 Aug 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4114 LNAI - II
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Intelligent Computing, ICIC 2006
Country/Territory	China
City	Kunming
Period	16/08/06 → 19/08/06

Access to Document

10.1007/11816171_72

Cite this

Bo, C., Li, J., Wang, Z., & Lin, J. (2006). Adaptive neural model based fault tolerant control for multi-variable process. In Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings (pp. 596-601). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4114 LNAI - II). Springer Verlag. https://doi.org/10.1007/11816171_72

Bo, Cuimei ; Li, Jun ; Wang, Zhiquan et al. / Adaptive neural model based fault tolerant control for multi-variable process. Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings. Springer Verlag, 2006. pp. 596-601 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b7f0f58fba47429cb7ad0d05b5f5dbd9,

title = "Adaptive neural model based fault tolerant control for multi-variable process",

abstract = "A new FTC scheme based on adaptive radial basis function (RBF) neural network (NN) model for unknown multi-variable dynamic systems is proposed. The scheme designs an adaptive RBF model to built process model and uses extended Kalman filter (EKF) technique to online learn the fault dynamics. Then, a model inversion controller is designed to produce the fault tolerant control (FTC) actions. The proposed scheme is applied to a three-tank process to evaluate the performance of the scheme. The simulation results show that component fault can be quickly compensated so that the system performances are recovered well.",

author = "Cuimei Bo and Jun Li and Zhiquan Wang and Jinguo Lin",

year = "2006",

doi = "10.1007/11816171_72",

language = "英语",

isbn = "3540372741",

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

publisher = "Springer Verlag",

pages = "596--601",

booktitle = "Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings",

address = "德国",

note = "International Conference on Intelligent Computing, ICIC 2006 ; Conference date: 16-08-2006 Through 19-08-2006",

}

Bo, C, Li, J, Wang, Z & Lin, J 2006, Adaptive neural model based fault tolerant control for multi-variable process. in Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4114 LNAI - II, Springer Verlag, pp. 596-601, International Conference on Intelligent Computing, ICIC 2006, Kunming, China, 16/08/06. https://doi.org/10.1007/11816171_72

Adaptive neural model based fault tolerant control for multi-variable process. / Bo, Cuimei; Li, Jun; Wang, Zhiquan et al.
Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings. Springer Verlag, 2006. p. 596-601 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4114 LNAI - II).

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

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T1 - Adaptive neural model based fault tolerant control for multi-variable process

AU - Bo, Cuimei

AU - Li, Jun

AU - Wang, Zhiquan

AU - Lin, Jinguo

PY - 2006

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N2 - A new FTC scheme based on adaptive radial basis function (RBF) neural network (NN) model for unknown multi-variable dynamic systems is proposed. The scheme designs an adaptive RBF model to built process model and uses extended Kalman filter (EKF) technique to online learn the fault dynamics. Then, a model inversion controller is designed to produce the fault tolerant control (FTC) actions. The proposed scheme is applied to a three-tank process to evaluate the performance of the scheme. The simulation results show that component fault can be quickly compensated so that the system performances are recovered well.

AB - A new FTC scheme based on adaptive radial basis function (RBF) neural network (NN) model for unknown multi-variable dynamic systems is proposed. The scheme designs an adaptive RBF model to built process model and uses extended Kalman filter (EKF) technique to online learn the fault dynamics. Then, a model inversion controller is designed to produce the fault tolerant control (FTC) actions. The proposed scheme is applied to a three-tank process to evaluate the performance of the scheme. The simulation results show that component fault can be quickly compensated so that the system performances are recovered well.

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M3 - 会议稿件

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

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BT - Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings

PB - Springer Verlag

T2 - International Conference on Intelligent Computing, ICIC 2006

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Bo C, Li J, Wang Z, Lin J. Adaptive neural model based fault tolerant control for multi-variable process. In Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings. Springer Verlag. 2006. p. 596-601. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11816171_72

Adaptive neural model based fault tolerant control for multi-variable process

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