Double-command fuzzy control of a nonlinear CSTR

Morteza Mohammadzaheri; Lei Chen

Korean Journal of Chemical Engineering, Vol.27, No.1, 19-31, January, 2010

DOI10.1007/s11814-009-0347-8 Export Citation

Double-command fuzzy control of a nonlinear CSTR

Morteza Mohammadzaheri^†, and Lei Chen

Mechanical Engineering School, University of Adelaide, Adelaide 5005, South Australia

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In this research, double-command control of a nonlinear chemical system is addressed. The system is a stirred tank reactor; two flows of liquid with different concentrations enter the system through two valves and another flow exits the tank with a concentration between the two input concentrations. Fuzzy logic was employed to design a model-free double-command controller for this system in the simulation environment. In order to avoid output chattering and frequent change of control command (leading to frequent closing-opening of control valves, in practice) a damper rule is added to the fuzzy control system. A feedforward (steady state) control law is also derived from the nonlinear mathematical model of the system to be added to feedback (fuzzy) controller generating transient control command. The hybrid control system leads to a very smooth change of control input, which suits real applications. The proposed control system offers much lower error integral, control command change and processing time in comparison with neuro-predictive controllers.

Keywords:Fuzzy Control;Hybrid Control;CSTR;Nonlinear System;Steady State Control

[References]

Yu DL, Chang TK, Yu DW, Control Engineering Practice, 15, 1577 (2007)
Madhuranthakam CR, Elkamel A, Budman H, Chem. Eng. Process., 47(2), 251 (2008)
Feng L, Wang JL, Poh EK, J. Process Control, 17(8), 683 (2007)
Wu W, J. Process Control, 13(6), 525 (2003)
Prakash J, Senthil R, J. Process Control, 18(5), 504 (2008)
Al Seyab RK, Cao Y, Comput. Chem. Eng., 32(7), 1533 (2008)
Demuth H, Beale M, Hagan M, Neural networks toolbox 5, user’s guide, The MathWorks, Online (2007)
Cao Y, Frank PM, IEEE Transactions on Fuzzy Systems, 8, 200 (2000)
Oysal Y, Becerikli Y, Konar AF, Comput. Chem. Eng., 30(5), 878 (2006)
Belarbi K, Titel F, Bourebia W, Benmahammed K, Engineering Applications of Artificial Intelligence, 18, 875 (2005)
Karakuzu C, ISA Transactions, 47, 229 (2008)
Ding B, Huang B, International Journal of Systems Science, 39, 487 (2008)
Hwang WH, Chey JI, Rhee HK, J. Appl. Polym. Sci., 67(5), 921 (1998)
Christofides PD, Daoutidis P, Automatica, 32(11), 1553 (1996)
Mohammadzaheri M, Chen L, Efficient intelligent nonlinear predictive control of a chemical plant, 15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly, November 25-28, Auckland, New Zealand (2008)
Tan Y, Van Cauwenberghe AR, Neurocomputing, 10, 83 (1996)
Jang JR, Sun C, Mizutani E, Neuro-fuzzy and soft computing, Prentice-Hall of India, New Delhi (2006)
Dennis J, Schnabel R, Handbooks in operations research and management science, Vol. 1, Optimization, Chapter I A view of unconstrained optimization, 1 (1989)

[Referenced By]

[1] Yu DL, Chang TK, Yu DW, Control Engineering Practice, 15, 1577 (2007)

[2] Madhuranthakam CR, Elkamel A, Budman H, Chem. Eng. Process., 47(2), 251 (2008)

[3] Feng L, Wang JL, Poh EK, J. Process Control, 17(8), 683 (2007)

[4] Wu W, J. Process Control, 13(6), 525 (2003)

[5] Prakash J, Senthil R, J. Process Control, 18(5), 504 (2008)

[6] Al Seyab RK, Cao Y, Comput. Chem. Eng., 32(7), 1533 (2008)

[7] Demuth H, Beale M, Hagan M, Neural networks toolbox 5, user’s guide, The MathWorks, Online (2007)

[8] Cao Y, Frank PM, IEEE Transactions on Fuzzy Systems, 8, 200 (2000)

[9] Oysal Y, Becerikli Y, Konar AF, Comput. Chem. Eng., 30(5), 878 (2006)

[10] Belarbi K, Titel F, Bourebia W, Benmahammed K, Engineering Applications of Artificial Intelligence, 18, 875 (2005)

[11] Karakuzu C, ISA Transactions, 47, 229 (2008)

[12] Ding B, Huang B, International Journal of Systems Science, 39, 487 (2008)

[13] Hwang WH, Chey JI, Rhee HK, J. Appl. Polym. Sci., 67(5), 921 (1998)

[14] Christofides PD, Daoutidis P, Automatica, 32(11), 1553 (1996)

[15] Mohammadzaheri M, Chen L, Efficient intelligent nonlinear predictive control of a chemical plant, 15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly, November 25-28, Auckland, New Zealand (2008)

[16] Tan Y, Van Cauwenberghe AR, Neurocomputing, 10, 83 (1996)

[17] Jang JR, Sun C, Mizutani E, Neuro-fuzzy and soft computing, Prentice-Hall of India, New Delhi (2006)

[18] Dennis J, Schnabel R, Handbooks in operations research and management science, Vol. 1, Optimization, Chapter I A view of unconstrained optimization, 1 (1989)