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

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), 2013. Published by Science Publishing Group

Keywords

Structural Control, Neural Networks, Generalized Minimum Variance Control

References
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[17] M.J. Grimble Non-linear generalized minimum variance feedback, feedforward and tracking control Automatica 41 (2005) pp 957 – 969 Elsevier
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Cite This Article
  • 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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    T1  - Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems
    AU  - L. Guenfaf
    AU  - M. Djebiri
    AU  - M. S. Boucherit
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    T2  - Automation, Control and Intelligent Systems
    JF  - Automation, Control and Intelligent Systems
    JO  - Automation, Control and Intelligent Systems
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    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
  • LSEI Laboratory, USTHB University BP 32 El Alia 16111, Bab Ezzouar, Algiers, Algeria

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

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

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

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