Adaptive Fuzzy Sliding Modes Observer for Phenol Biodegradation
Machine Learning Research
Volume 1, Issue 1, December 2016, Pages: 33-41
Received: Dec. 6, 2016; Accepted: Jan. 6, 2017; Published: Jan. 31, 2017
Views 2844      Downloads 106
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
Article Tools
Follow on us
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.
Fuzzy Logic, Observers Design Sliding Modes, Biodegradation
To cite this article
Marco Antonio Márquez-Vera, Julo César Ramos-Fernández, Blanca Diana Balderrama-Hernández, Adaptive Fuzzy Sliding Modes Observer for Phenol Biodegradation, Machine Learning Research. Vol. 1, No. 1, 2016, pp. 33-41. doi: 10.11648/j.mlr.20160101.14
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
K. Astrom, B. Wittenmark, Adaptive Control. Pearson Education. India, 2006.
K. Astrom, T. Hagglund, Advanced PID Control. Instrumentation, Systems, and Automation Society. United States of America, 2006.
R. Babuska, Fuzzy Modeling for Control. In Dr. Hans-Jurgen Zimmermann (Ed). International Series in Intelligemt Technologies, Aachen, Germany, 1998.
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.
Z. Bien and J. Xu, Iterative Learning Control: Analysis, Design, Integration and Applications. Kluwer Academic Publishers, Massachusetts, 2008.
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.
O. Castillo, P. Melin, Type-2 Fuzzy Logic: Theory and Applications. Studies in Fuzzyness and Soft Computing 223, Springer, 2008.
I. Dunn, E. Heinzle, J. Ingham, J. Prenosil, Biological Reaction Engineering. Wiley-VCH Verlag GmbH, Germany. 2003.
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.
J. M. Guadayol. Control, Instrumentación y Automatización de Procesos Químicos: Problemas. Ediciones de la Universidad Politécnica de Cataluña, Barcelona, 1999.
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.
N. Karnik, J. Mendel, Q. Liang, Type-2 Fuzzy Logic Systems, IEEE Transactions on Fuzzy Systems. 7 (6) (1999) 643-658.
B. Kuo. Sistemas de Control Digital. Compañía Editorial Continental. Mexico, 2003.
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.
M. J. Lin, F. Luo, Adaptive neural control of the dissolved oxygen concentration in WWTPs based on disturbance observer. Neurocomputing. 185 (2016) 133-141.
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.
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.
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.
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.
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.
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.
H. Nguyen, N. Prasad, C. Walker, E. Walker, A First Course in Fuzzy and Neural Control. Chapman & Hall/CRC. Florida, 2003.
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.
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.
K. Passino, S. Yurkovich, Fuzzy Control. Addison Wesley Longman Inc. California, 1998.
T. Ross, Fuzzy Logic with Engineering Applications, John Wiley & Sons, Ltd, West Sussex, 2008.
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.
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.
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.
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.
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.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186