Adaptive Fuzzy Sliding Modes Observer for Phenol Biodegradation

Marco Antonio Márquez-Vera; Julo César Ramos-Fernández; Blanca Diana Balderrama-Hernández

doi:doi:10.11648/j.mlr.20160101.14

| Peer-Reviewed

Adaptive Fuzzy Sliding Modes Observer for Phenol Biodegradation

Marco Antonio Márquez-Vera, Julo César Ramos-Fernández, Blanca Diana Balderrama-Hernández

Published in Machine Learning Research (Volume 1, Issue 1)

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

Fuzzy Logic, Observers Design Sliding Modes, Biodegradation

References

[1]	A. Aceves, Usos y Abusos de la Lógica Difusa para el Control de Procesos: Una Alternativa para Modelar lo Incompleto de la Información y lo Impreciso de una Observación, Proc. Conf. Of Con Mantenimiento Productivo, Mexico. (2001) pp. 12-17.
[2]	K. Astrom, B. Wittenmark, Adaptive Control. Pearson Education. India, 2006.
[3]	K. Astrom, T. Hagglund, Advanced PID Control. Instrumentation, Systems, and Automation Society. United States of America, 2006.
[4]	R. Babuska, Fuzzy Modeling for Control. In Dr. Hans-Jurgen Zimmermann (Ed). International Series in Intelligemt Technologies, Aachen, Germany, 1998.
[5]	O. Bernard, G. Sallet, A. Sciandra, Nonlinear Observers for a class of Biological Systems: Applications to Validation of a Phytoplanktonic Growth Model. IEEE Transactions on Automatic Control, 43 (8) (1998) 1056-1065.
[6]	Z. Bien and J. Xu, Iterative Learning Control: Analysis, Design, Integration and Applications. Kluwer Academic Publishers, Massachusetts, 2008.
[7]	S. Boyd, L. El Ghaoui, E. Feron, V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory. Society for Industrial ans Applied Mathematics. Philadelphia, 1994.
[8]	O. Castillo, P. Melin, Type-2 Fuzzy Logic: Theory and Applications. Studies in Fuzzyness and Soft Computing 223, Springer, 2008.
[9]	I. Dunn, E. Heinzle, J. Ingham, J. Prenosil, Biological Reaction Engineering. Wiley-VCH Verlag GmbH, Germany. 2003.
[10]	C. Feudjio. P. Bogaerts, J. S. Deschenes, A. V. Wouwer, Design of a robust Lipschitz observer-application to monitoring of culture of micro-algae Scenesdesmus obliquus. IFAC-PapersOnLine. 49 (7) (2016) 1056-1061.
[11]	J. M. Guadayol. Control, Instrumentación y Automatización de Procesos Químicos: Problemas. Ediciones de la Universidad Politécnica de Cataluña, Barcelona, 1999.
[12]	Q. P. Ha, Q. H. Nguyen, D. C. Rye, Fuzzy Sliding-Mode Controllers with Applications. IEEE Transactions on Industrial Electronics, 48 (1) (2001) 38-46.
[13]	N. Karnik, J. Mendel, Q. Liang, Type-2 Fuzzy Logic Systems, IEEE Transactions on Fuzzy Systems. 7 (6) (1999) 643-658.
[14]	B. Kuo. Sistemas de Control Digital. Compañía Editorial Continental. Mexico, 2003.
[15]	Z. Lendek, T. Guerra, R. Babuska, B. De Schutter. Stability Analysis and Nonlinear Observer Design Using Takagi-Sugeno Fuzzy Models. Studies in Fuzziness and Soft Computing. Springer. India, 2010.
[16]	M. J. Lin, F. Luo, Adaptive neural control of the dissolved oxygen concentration in WWTPs based on disturbance observer. Neurocomputing. 185 (2016) 133-141.
[17]	R. Marino, P. Tomei, Adaptive Observers with Arbitrary Exponential Rate of Convergence for Nonlinear Systems. IEEE Transactions on Automatic Control. 40 (7) (1995) 1300-1304.
[18]	M. A. Márquez, C. Ben-Youssef, G. Vázquez, J. Waissman, Iterative Learning Control of a SBR Reactor by using a Limited Number of Samples, Proc. Conf. of 2nd International Meeting on Environmental Biotechnology and Engineering, D. F. Mexico 2006 pp. 81-89.
[19]	M. A. Márquez, J. Waissman, O. Gutú, Fuzzy Model Based Iterative Learning Control for Phenol Biodegradation. P. Melin et al. (Eds), Foundations on Fuzzy Logic and Soft Computing, Lecture Notes in Artificial Intelligence 4529, 2007, pp. 328-337.
[20]	J. M. Mendel, R. I. John, Liu, Interval Type-2 Fuzzy logic Systems Made Simple, IEEE Transactions on Fuzzy systems. 14 (6) (2006) 535-550.
[21]	O. Montiel, O. Castillo, P. Melin, R. Sepúlveda. Improving the Human Evolutionary Model: An Intelligent Optimization Method. International Mathematical Forum. 2 (1) (2007) 21-44.
[22]	F. Muñoz, C. Ben-Youssef, Biomass and Phenol Estimation using Dissolved Oxygen Measurement, Proc. Conf. of Electronics, Robotics and Automotive Mechanics Conference, Cuernavaca, Mexico, 2006.
[23]	H. Nguyen, N. Prasad, C. Walker, E. Walker, A First Course in Fuzzy and Neural Control. Chapman & Hall/CRC. Florida, 2003.
[24]	S. Nuñez, H. De Battista, F. Garelli, A. Vignoni, J. Picó, Second-order sliding mode observer for multiple kinetic rates estimation in bioprocesses. Control Engineering Practice. 21 (2013) 1259-1265.
[25]	S. Nuñez, F. Garelli, H. De Battista, Product-based sliding mode observer for biomass and rate estimation in Luedeking-Piret like processes. Chemical Engineering Research and Design. 105 (2016) 24-30.
[26]	K. Passino, S. Yurkovich, Fuzzy Control. Addison Wesley Longman Inc. California, 1998.
[27]	T. Ross, Fuzzy Logic with Engineering Applications, John Wiley & Sons, Ltd, West Sussex, 2008.
[28]	A. Tayebi, J. Xu, Observer-Based Iterative Learning Control for a Class of Time-Varying Nonlinear Systems, IEEE Transactions on Circuits and Systems, 50 (3) (2003) 452-455.
[29]	M. Teixeira, E. Assunçao, R. Avellar,On Relaxed LMI-Based Designs for Fuzzy Regulators and Fuzzy Observers. IEEE Transactions on Fuzzy Systems, 11 (5) (2003) 613-623.
[30]	D. Theilliol, J. C. Ponsart, J. Harmand, C. Join, P. Gras, On-line Estimator of unmeasured Inputs for Anaerobic Wastewater Treatment Process. Control Engineering Practice, 11 (2003) 1007-1019.
[31]	G. Vázquez, C. Ben-Youssef, J. Waissman, Two Step Modelling of the Biodegradation of Phenol by an Acclimated Activated Sludge, Chemical Engineering Jornal 117 (3) (2006) 245-252.
[32]	W. Y. Wang, M. L. Chan, C. C. James, T. T. Lee, H∞ Tracking-Based Sliding Mode Control for Uncertain Nonlinear Systems via and Adaptive Fuzzy-Neural Approach. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 32 (4) (2002) 483-492.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download

Author Information

Marco Antonio Márquez-Vera

Department of Mechatronics, Politechnic University of Pachuca, Zempoala, Mexico
Julo César Ramos-Fernández

Department of Mechatronics, Politechnic University of Pachuca, Zempoala, Mexico
Blanca Diana Balderrama-Hernández

Basic Education, Secretariat of Public Education, Pachuca, Mexico

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download