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. |
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Copyright © The Author(s), 2017. Published by Science Publishing Group |
Fuzzy Logic, Observers Design Sliding Modes, Biodegradation
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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
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
@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} }
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 -