Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems

L. Guenfaf; M. Djebiri; M. S. Boucherit; F. Boudjema

doi:doi:10.11648/j.acis.20130101.12

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Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems

L. Guenfaf, M. Djebiri, M. S. Boucherit, F. Boudjema

Published in Automation, Control and Intelligent Systems (Volume 1, Issue 1)

Received: 26 February 2013 Accepted: Published: 20 February 2013

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Abstract

This paper presents a robustness of the proposed generalized minimum variance algorithm. The main idea is to use artificial neural network for generalization of the GMV. This will give a neural network-based control method wich can be applied to civil engineering structures. The neural network learns the control task from an already existing controller, which is the generalized minimum variance (GMV) controller. The objective is to take advantage of the generalization capabilities and the nonlinear behavior of neural networks in order to overcome the limitations of the existing controller and even to improve its performances. Simulation results demonstrate the robustness of this algorithm and its capability to compensate the structural parameter variations and seismic ground motion.

Published in	Automation, Control and Intelligent Systems (Volume 1, Issue 1)
DOI	10.11648/j.acis.20130101.12
Page(s)	7-15
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Structural Control, Neural Networks, Generalized Minimum Variance Control

References

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APA Style

L. Guenfaf, M. Djebiri, M. S. Boucherit, F. Boudjema. (2013). Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems. Automation, Control and Intelligent Systems, 1(1), 7-15. https://doi.org/10.11648/j.acis.20130101.12

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ACS Style

L. Guenfaf; M. Djebiri; M. S. Boucherit; F. Boudjema. Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems. Autom. Control Intell. Syst. 2013, 1(1), 7-15. doi: 10.11648/j.acis.20130101.12

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AMA Style

L. Guenfaf, M. Djebiri, M. S. Boucherit, F. Boudjema. Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems. Autom Control Intell Syst. 2013;1(1):7-15. doi: 10.11648/j.acis.20130101.12

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@article{10.11648/j.acis.20130101.12,
  author = {L. Guenfaf and M. Djebiri and M. S. Boucherit and F. Boudjema},
  title = {Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems},
  journal = {Automation, Control and Intelligent Systems},
  volume = {1},
  number = {1},
  pages = {7-15},
  doi = {10.11648/j.acis.20130101.12},
  url = {https://doi.org/10.11648/j.acis.20130101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130101.12},
  abstract = {This paper presents a robustness of the proposed generalized minimum variance algorithm. The main idea is to use artificial neural network for generalization of the GMV. This will give a neural network-based control method wich can be applied to civil engineering structures. The neural network learns the control task from an already existing controller, which is the generalized minimum variance (GMV) controller. The objective is to take advantage of the generalization capabilities and the nonlinear behavior of neural networks in order to overcome the limitations of the existing controller and even to improve its performances. Simulation results demonstrate the robustness of this algorithm and its capability to compensate the structural parameter variations and seismic ground motion.},
 year = {2013}
}

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TY  - JOUR
T1  - Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems
AU  - L. Guenfaf
AU  - M. Djebiri
AU  - M. S. Boucherit
AU  - F. Boudjema
Y1  - 2013/02/20
PY  - 2013
N1  - https://doi.org/10.11648/j.acis.20130101.12
DO  - 10.11648/j.acis.20130101.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 7
EP  - 15
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20130101.12
AB  - This paper presents a robustness of the proposed generalized minimum variance algorithm. The main idea is to use artificial neural network for generalization of the GMV. This will give a neural network-based control method wich can be applied to civil engineering structures. The neural network learns the control task from an already existing controller, which is the generalized minimum variance (GMV) controller. The objective is to take advantage of the generalization capabilities and the nonlinear behavior of neural networks in order to overcome the limitations of the existing controller and even to improve its performances. Simulation results demonstrate the robustness of this algorithm and its capability to compensate the structural parameter variations and seismic ground motion.
VL  - 1
IS  - 1
ER  -

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Author Information

L. Guenfaf

LSEI Laboratory, USTHB University BP 32 El Alia 16111, Bab Ezzouar, Algiers, Algeria
M. Djebiri

LCP, Laboratory, ENP, Hassan Badi El harrach, Algiers, Algeria
M. S. Boucherit

LCP, Laboratory, ENP, Hassan Badi El harrach, Algiers, Algeria
F. Boudjema

LCP, Laboratory, ENP, Hassan Badi El harrach, Algiers, Algeria

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Plain Text BibTeX RIS

APA Style

L. Guenfaf, M. Djebiri, M. S. Boucherit, F. Boudjema. (2013). Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems. Automation, Control and Intelligent Systems, 1(1), 7-15. https://doi.org/10.11648/j.acis.20130101.12

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ACS Style

L. Guenfaf; M. Djebiri; M. S. Boucherit; F. Boudjema. Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems. Autom. Control Intell. Syst. 2013, 1(1), 7-15. doi: 10.11648/j.acis.20130101.12

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AMA Style

L. Guenfaf, M. Djebiri, M. S. Boucherit, F. Boudjema. Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems. Autom Control Intell Syst. 2013;1(1):7-15. doi: 10.11648/j.acis.20130101.12

Copy | Download

@article{10.11648/j.acis.20130101.12,
  author = {L. Guenfaf and M. Djebiri and M. S. Boucherit and F. Boudjema},
  title = {Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems},
  journal = {Automation, Control and Intelligent Systems},
  volume = {1},
  number = {1},
  pages = {7-15},
  doi = {10.11648/j.acis.20130101.12},
  url = {https://doi.org/10.11648/j.acis.20130101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130101.12},
  abstract = {This paper presents a robustness of the proposed generalized minimum variance algorithm. The main idea is to use artificial neural network for generalization of the GMV. This will give a neural network-based control method wich can be applied to civil engineering structures. The neural network learns the control task from an already existing controller, which is the generalized minimum variance (GMV) controller. The objective is to take advantage of the generalization capabilities and the nonlinear behavior of neural networks in order to overcome the limitations of the existing controller and even to improve its performances. Simulation results demonstrate the robustness of this algorithm and its capability to compensate the structural parameter variations and seismic ground motion.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems
AU  - L. Guenfaf
AU  - M. Djebiri
AU  - M. S. Boucherit
AU  - F. Boudjema
Y1  - 2013/02/20
PY  - 2013
N1  - https://doi.org/10.11648/j.acis.20130101.12
DO  - 10.11648/j.acis.20130101.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 7
EP  - 15
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20130101.12
AB  - This paper presents a robustness of the proposed generalized minimum variance algorithm. The main idea is to use artificial neural network for generalization of the GMV. This will give a neural network-based control method wich can be applied to civil engineering structures. The neural network learns the control task from an already existing controller, which is the generalized minimum variance (GMV) controller. The objective is to take advantage of the generalization capabilities and the nonlinear behavior of neural networks in order to overcome the limitations of the existing controller and even to improve its performances. Simulation results demonstrate the robustness of this algorithm and its capability to compensate the structural parameter variations and seismic ground motion.
VL  - 1
IS  - 1
ER  -

Copy | Download