PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Neuro-fuzzy control design of processes in chemical technologies

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and laboratory mixing process.
Rocznik
Strony
233--250
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
autor
autor
  • Department of Process Control, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinskeho 9, 812 37 Bratislava, Slovak Republic
Bibliografia
  • [1] Armfield. Instruction manual PCT40, 4th edition, 2005.
  • [2] Armfield. Instruction manual PCT41, 3rd edition, 2006.
  • [3] Armfield. Instruction manual PCT42, 2nd edition, 2006.
  • [4] K. J. Åstroöm and B. Wittenmark: Adaptive Control. Reading: Addison-Wesley Publishing Company, 1989.
  • [5] R. Babuška and H. Verbruggen: Neuro-fuzzy methods for nonlinear system identification. Annual Reviews in Control, 2003, 73-85.
  • [6] M. Bakoš Ová, D. Puna, P. Dostál and J. Závacká: Robust stabilization of a chemical reactor. Chemical Papers, 5(63), (2009), 527-536.
  • [7] G. Bastin and D. Dochain: On-line estimation and adaptive control of bioreactors. Elsevier Science Publishers B. V., 1990.
  • [8] L. Blahová and J. Dvoran: Neuro-fuzzy control of chemical reactor with disturbances. In Latest Trends on Systems, WSEAS Press, Corfu Island, Greece, 14 (2010), 336-340.
  • [9] J. Z. Chu, P. F. Tsai, W. Y. Tsai, S. S. Jang, D. S. H. Wong, S. S. Shieh, P. H. Lin and S. J. Jiang: An experimental study of model predictive control based on artificial neural networks. In Proc. of 7th Int. Conf. on Knowledge-Based Intelligent Information and Engineering Systems, Springer, Oxford, UK, (2003), 1296-1302.
  • [10] J. E. Dennis JR. and R. B. Schnabel: Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, Englewood Cliffs, 1983.
  • [11] P. Dostal, F. Gazdos, V. Bobal and J. Vojtesek: Adaptive control of a continuous stirred tank reactor by two feedback controllers. In 9th IFAC Workshop on Adaptation and Learning in Control and Signal Processing, Imperial Anichkov Palace, Russia, (2007).
  • [12] M. A. Henson and D. E. Seborg: Nonlinear process control. Prentice Hall, 1997.
  • [13] J. S. R. Jang: Adaptive-network-based fuzzy inference system. IEEE Trans. on Systems, Man, and Cybernetics, 23 (1993), 665-685.
  • [14] M. Kvasnica, M. Herceg, L. Čirka and M. Fikar: Model predictive control of a CSTR: A hybrid modeling approach. Chemical papers, 3(64), (2010), 301-309.
  • [15] S. Liu and J. Yu: Robust control based on neuro-fuzzy systems for a continuous stirred tank reactor. Proc. of the First Int. Conf. on Machine Learning and Cybernetics, Beijing, (2002).
  • [16] J. M. Maciejowski: Predictive Control with Constraints. Prentice Hall, 2001.
  • [17] D. W. Marquardt: An algorithm for least squares estimation of nonlinear parameters. J. of Society for Industrial and Applied Mathematics, 11 (1963), 431-441.
  • [18] A. Mészáros, L. Čirka and L. Šperka: Intelligent control of a pH process. Chemical Papers, 2(63), (2009), 180-187.
  • [19] J. Mikleš and M. Fikar: Process Modeling, Identification, and Control. Springer Verlag, Berlin Heidelberg, 2007.
  • [20] M. Morari and E. Zafiriou: Robust Process Control. Prentice Hall, 1989.
  • [21] D. Sámek and L. Macku: Semi-batch reactor predictive control using artificial neural network. 16th Mediterranean Conf. on Control and Automation, Ajaccio, France, (2008), 1532-1537.
  • [22] T. Takagi and M. Sugeno: Fuzzy identification of fuzzy systems and its applications to modeling and control. IEEE Trans. Systems, Man and Cybernetics, 15 (1985), 116-132.
  • [23] The Mathworks: Neural Network Toolbox, User's Guide, 2002.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0098-0015
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.