Optimized and Self-Organized Fuzzy Logic Controller for pH Neutralization Process

Автор: Parikshit Kishor Singh, Surekha Bhanot, Hare Krishna Mohanta

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 12 vol.5, 2013 года.

Бесплатный доступ

To conform to strict environmental safety regulations, pH control is used in many industrial applications. For this purpose modern process industries are increasingly relying on intelligent and adaptive control strategies. On one hand intelligent control strategies try to imitate human way of thinking and decision making using artificial intelligence (AI) based techniques such as fuzzy logic whereas on the other hand adaptive mechanism ensures adjusting of the controller parameters. A self-organized fuzzy logic controller (SOFLC) is intelligent in nature and adapts its performance to meet the figure of merit. This paper presents an optimized SOFLC for pH control using performance correction table. The fuzzy adaptation mechanism basically involves a penalty for the output membership functions if the controller performance is poor. The evolutionary genetic algorithm (GA) is used for optimization of input-output scaling factors of the conventional fuzzy logic controller (FLC) as well as elements of the fuzzy performance correction table. The resulting optimized SOFLC is compared with optimized FLC for servo and regulatory control. Comparison indicate superior performance of SOFLC over FLC in terms of much reduced integral of squared error (ISE), maximum overshoot and undershoot, and increased speed of response.

Еще

PH, Neutralization Process, Intelligent Control, Fuzzy, Self-Organizing, Adaptive, Optimization, Genetic Algorithm

Короткий адрес: https://sciup.org/15010504

IDR: 15010504

Список литературы Optimized and Self-Organized Fuzzy Logic Controller for pH Neutralization Process

  • Shinskey F G. Process-Control Systems: Application / Design / Adjustment. McGraw-Hill, USA, 1979.
  • Mellichamp D A, Coughanowr D R, Koppel L B. Characterization and gain identification of time varying flow processes. A.I.Ch.E. J., 1966, 1 (12):75-82.
  • Mellichamp D A, Coughanowr D R, and Koppel L B. Identification and adaptation in control loops with time varying gain. AIChE, 1966, 1 (12):83-89.
  • McAvoy T J, Hsu E, Lowenthals S. Dynamics of pH in controlled stirred tank reactor. Ind. Eng. Chem. Process Des. Develop., 1972, 1 (11):68-70.
  • McAvoy T J, Hsu E, Lowenthals S. Time optimal and Ziegler-Nichols control. Ind. Eng. Chem. Process Des. Develop., 1972, 1 (11): 71-78.
  • Gustafsson T K, Waller K V. Dynamic modeling and reaction invariant control of pH. Chemical Engineering Science, 1983, 3 (38):389-398.
  • Gustafsson T K. An experimental study of a class of algorithms for adaptive pH control. Chemical Engineering Science, 1985, 5 (40):827-837.
  • Wright R A, Kravaris C. Nonlinear control of pH processes using strong acid equivalent. Ind. Eng. Chem. Process Des. Develop., 1991, 7 (30):1561-1572.
  • Wright R A, Soroush M, Kravaris C. Strong acid equivalent control of pH processes: An experimental study. Ind. Eng. Chem. Process Des. Develop., 1991, 11 (30):2437-2444.
  • Bhat N, McAvoy T J. Use of neural nets for dynamic modeling and control of chemical process system. Proceedings of the 1989 American Control Conference. Pittsburg, USA: Institute of Electrical and Electronics Engineers, 1989. 1342-1348.
  • Draeger A, Ranke H, Engell S. Neural network based model predictive control of a continuous neutralization reactor. Proceedings of the third IEEE conference on Control Applications. Strathclyde University, Glasgow: Institute of Electrical and Electronics Engineers, 1994. 427-432.
  • Zhang J, Morris J. Neuro-fuzzy networks for modelling and model-based control. Proceedings of the IEE Colloquium on Neural and Fuzzy systems: Design, Hardware and Applications. London, UK: The Institution of Engineering and Technology, 1997. 6/1-6/4.
  • Zadeh L A. Fuzzy sets. Information and Control, 1965, 3 (8):338-353.
  • Zadeh L A. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. on Systems, Man, and Cybernetics, 1973, 1 (3):28-44.
  • Zadeh L A. From computing with numbers to computing with words – From manipulation of measurements to manipulation of perceptions. Int. J. Appl. Math. Comput. Sci., 2002, 3 (12):307-324.
  • Zadeh L A. Is there a need for fuzzy logic? Information Sciences, 2008, 13 (178):2751-2779.
  • Mamdani E H, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Machine Studies, 1975, 1 (7):1-13.
  • Procyk T J, Mamdani E H. A linguistic self-organizing process controller. Automatica, 1979, 1 (15):15-30.
  • Mamdani E H. Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans. On Computers, 1976, 12 (26):1182-1191.
  • Erenoglu I, Eksin I, Yesil E, et al. An intelligent hybrid fuzzy PID controller. Proceedings of the 20th European Conference on Modeling and Simulation. Bonn, Germany: Bonn-Rhein-Sieg University of Applied Sciences, 2006.
  • Daroogheh N. High gain adaptive control of a neutralization process pH. Proceedings of Chinese Control and Decision Conference. Guilin, People’s Republic of China: Institute of Electrical and Electronics Engineers, 2009. 3477-3480.
  • Saji K S, Sasi K M. Fuzzy sliding mode control for a pH process. Proceedings of IEEE International Conference on Communication Control and Computing Technologies. Ramanathapuram, India: Institute of Electrical and Electronics Engineers, 2010. 276-281.
  • Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Systems, Man, and Cybernetics, 1985, 1 (15):116-132.
  • Jang J –S R, Sun C –T. Neuro-fuzzy modeling and control. Proc. of the IEEE, 1995, 3 (83):378-406.
  • Holland J H. Adaptation in natural and artificial systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT, Cambridge, 1992.
  • Goldberg D E. Genetic Algorithms in Search, Optimization, & Macine Learning. Pearson, 1989.
  • Wang P, Kwok D P. Optimal fuzzy PID control based on genetic algorithm. Proceedings of the 1992 International Conference on Industrial Electronics, Control, Instrumentation, and Automation. San Diego, California: Institute of Electrical and Electronics Engineers, 1992. 977-981.
  • Karr C L, Gentry E J. Fuzzy control of pH using genetic algorithms. IEEE Trans. Fuzzy Systems, 1993, 1 (1):46-53.
  • Bagis A. Determination of the PID controller parameters by modified genetic algorithm for improved performance. Journal of Information Science and Engineering, 2007, 5 (23):1469-1480.
  • Fuzzy Logic Toolbox User's Guide, The MathWorks Inc., USA, http://www.mathworks.in.
  • Astrom K J, Wittenmark B. Adaptive control. Dover Publications, New York, 2008.
  • Global Optimization Toolbox User's Guide, The MathWorks Inc., USA, http://www.mathworks.in
Еще
Статья научная