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Adaptive Fuzzy Sliding Modes Observer for Phenol Biodegradation

Received: 6 December 2016     Accepted: 6 January 2017     Published: 31 January 2017
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Abstract

There exist processes difficult to control as the chemical ones, in this work the bacterial grow rate in a biotechnological process is controlled, to make it, a fuzzy model was proposed, this control uses the clustering technique to improve the membership functions for the antecedents rules and least squares for the consequents; the control work in an acceptable manner, but in practice it is common to find that the actuators cannot respond to the signal control due saturation or its frequency response; so, a predictive control was used to anticipate the control signal. A comparative Table shows the comparison between different control horizons. Finally the use of a model reference can reduce the control signal amplitude and reduce some criterion errors.

Published in Machine Learning Research (Volume 1, Issue 1)
DOI 10.11648/j.mlr.20160101.14
Page(s) 33-41
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), 2017. Published by Science Publishing Group

Keywords

Fuzzy Logic, Observers Design Sliding Modes, Biodegradation

References
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Cite This Article
  • APA Style

    Marco Antonio Márquez-Vera, Julo César Ramos-Fernández, Blanca Diana Balderrama-Hernández. (2017). Adaptive Fuzzy Sliding Modes Observer for Phenol Biodegradation. Machine Learning Research, 1(1), 33-41. https://doi.org/10.11648/j.mlr.20160101.14

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    ACS Style

    Marco Antonio Márquez-Vera; Julo César Ramos-Fernández; Blanca Diana Balderrama-Hernández. Adaptive Fuzzy Sliding Modes Observer for Phenol Biodegradation. Mach. Learn. Res. 2017, 1(1), 33-41. doi: 10.11648/j.mlr.20160101.14

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    AMA Style

    Marco Antonio Márquez-Vera, Julo César Ramos-Fernández, Blanca Diana Balderrama-Hernández. Adaptive Fuzzy Sliding Modes Observer for Phenol Biodegradation. Mach Learn Res. 2017;1(1):33-41. doi: 10.11648/j.mlr.20160101.14

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  • @article{10.11648/j.mlr.20160101.14,
      author = {Marco Antonio Márquez-Vera and Julo César Ramos-Fernández and Blanca Diana Balderrama-Hernández},
      title = {Adaptive Fuzzy Sliding Modes Observer for Phenol Biodegradation},
      journal = {Machine Learning Research},
      volume = {1},
      number = {1},
      pages = {33-41},
      doi = {10.11648/j.mlr.20160101.14},
      url = {https://doi.org/10.11648/j.mlr.20160101.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20160101.14},
      abstract = {There exist processes difficult to control as the chemical ones, in this work the bacterial grow rate in a biotechnological process is controlled, to make it, a fuzzy model was proposed, this control uses the clustering technique to improve the membership functions for the antecedents rules and least squares for the consequents; the control work in an acceptable manner, but in practice it is common to find that the actuators cannot respond to the signal control due saturation or its frequency response; so, a predictive control was used to anticipate the control signal. A comparative Table shows the comparison between different control horizons. Finally the use of a model reference can reduce the control signal amplitude and reduce some criterion errors.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Adaptive Fuzzy Sliding Modes Observer for Phenol Biodegradation
    AU  - Marco Antonio Márquez-Vera
    AU  - Julo César Ramos-Fernández
    AU  - Blanca Diana Balderrama-Hernández
    Y1  - 2017/01/31
    PY  - 2017
    N1  - https://doi.org/10.11648/j.mlr.20160101.14
    DO  - 10.11648/j.mlr.20160101.14
    T2  - Machine Learning Research
    JF  - Machine Learning Research
    JO  - Machine Learning Research
    SP  - 33
    EP  - 41
    PB  - Science Publishing Group
    SN  - 2637-5680
    UR  - https://doi.org/10.11648/j.mlr.20160101.14
    AB  - There exist processes difficult to control as the chemical ones, in this work the bacterial grow rate in a biotechnological process is controlled, to make it, a fuzzy model was proposed, this control uses the clustering technique to improve the membership functions for the antecedents rules and least squares for the consequents; the control work in an acceptable manner, but in practice it is common to find that the actuators cannot respond to the signal control due saturation or its frequency response; so, a predictive control was used to anticipate the control signal. A comparative Table shows the comparison between different control horizons. Finally the use of a model reference can reduce the control signal amplitude and reduce some criterion errors.
    VL  - 1
    IS  - 1
    ER  - 

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Author Information
  • Department of Mechatronics, Politechnic University of Pachuca, Zempoala, Mexico

  • Department of Mechatronics, Politechnic University of Pachuca, Zempoala, Mexico

  • Basic Education, Secretariat of Public Education, Pachuca, Mexico

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