International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.7, No.10, Sep. 2015
Intelligent Approaches to Real Time Level Control
Full Text (PDF, 552KB), PP.19-27
Liquid level control is important for ensuring energy and material balance in many installations but it also difficult as the plant is nonlinear, inertial and with model uncertainties. Fuzzy logic controllers (FLCs) are successfully applied to ensure system stability and robustness by simple means and a model-free design. This paper suggests a procedure for off-line tuning of the many FLC parameters based on optimization of a suggested multi-objective function defined on several system performance indices using genetic algorithms (GAs). First, a model-free FLC is empirically tuned, then applied for real time control of the plant and the necessary data recorded and used to GA parameter optimize a TSK plant model of an accepted structure. The validated on different set of experimental data model is employed in FLC closed loop system simulation experiments to evaluate the fitness function in the GA optimization of the FLC pre-processing and post-processing parameters. The procedure is applied for the real time PI/PID FLC level control in a laboratory-scale tank system. The improvement of the system performance indices due to the GA optimization is estimated in level real time control.
Cite This Paper
Snejana Yordanova,"Intelligent Approaches to Real Time Level Control", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.10, pp.19-27, 2015. DOI: 10.5815/ijisa.2015.10.03
T. Neshkov, S. Yordanova, and I. Topalova, Process Control and Production Automation. Sofia: Technical University of Sofia, 2007.
G. Stephanopoulos, Chemical Process Control. An Introduction to Theory and Practice. Prentice Hall, 1984.
F.G. Shinskey, Process Control Systems: Application, Design, Adjustment. 2nd ed., McGraw-Hill, Inc., 1979.
J. Jantzen, Foundations of Fuzzy Control. NY: John Wiley & Sons, Inc., 2007.
M. Al-H. Basil, M. Fernando, and A. Jiménez, “Fuzzy control for a liquid level system”, Proceedings of the 3rd Conference of the European Society for Fuzzy Logic and Technology, Zittau, Germany, September 10-12, 2003.
M. Raduca, E. Raduca, C. Hatiegan, and D. Ungureanu, “Fuzzy controller for adjustment of liquid level in the tank”, Annals of the University of Craiova, Mathematics and Computer Science Series, vol. 38(4), 2011, pp. 33-43.
Disha, Mr. P. Pandey, and R. Chugh, “Simulation of water level control in a tank using fuzzy logic”, IOSR Journal of Electrical and Electronics Engineering (IOSRJEEE), vol. 2(3), 2012, pp. 9-12.
D. Ahmad, A. Ahmad, V. Redhu, and U. Gupta, “Liquid level control by using fuzzy logic controller”, International Journal of Advances in Engineering and Technology, vol. 4(1), 2012, pp. 537-549.
R. Malhotr, and R. Sodhi, “Boiler flow control using PID and fuzzy logic controller”, IJCSET, vol. 1(6), 2011, pp. 315-319.
A. Kumar, Rajbir, and Kuldeepak, “Performance comparison of level control with the three, five and nine fuzzy rules based method”, International Journal of Advanced Research in Computer Science and Electronics Engineering, vol. 2(7), 2013, pp. 561-566.
R.L. Haupt, and S. El. Haupt, Practical Genetic Algorithms. NY: John Wiley & Sons, 1998.
B. Kumar, and R. Dhiman, “Optimization of PID controller for liquid level tank system using intelligent techniques”, Canadian Journal on Electrical and Electronics Engineering, vol. 2(11), 2011, pp. 531-535.
T. K. Teng, J. S. Shieh, and C. S. Chen, “Genetic algorithms applied in online autotuning PID parameters of a liquid-level control system”, J. Transactions of the Institute of Measurement and Control, vol. 25(5), 2003, pp. 433–450.
T.T. Erguzel, “Fuzzy controller parameter optimization using genetic algorithm for a real time controlled system”, Proceedings of the World Congress on Engineering, vol. 2, London, U.K., 2013.
T.L. Seng, M. Khalid, and R. Yusof, “Tuning of a neuro-fuzzy controller by genetic algorithms with an application to a coupled-tank liquid-level control system”, Int. J. of Engineering Applications on Artificial Intelligence, vol. 11, 1998, pp. 517-529
H. Cho, K. Cho, and B. Wang, “Fuzzy-PID hybrid control: automatic rule generation using genetic algorithms”, Fuzzy Sets and Systems, vol. 92, 1997, pp. 305-316.
O.I. Hassanein, A.A. Aly and A.A. Abo-Ismail, "Parameter tuning via genetic algorithm of fuzzy controller for fire tube boiler", IJISA, vol.4, no.4, pp. 9-18, 2012.
Y. Misra and H.R Kamath, "Design algorithm and performance analysis of conventional and fuzzy controller for maintaining the cane level during sugar making process", IJISA, vol.7, no.1, pp. 80-93, 2015. DOI: 10.5815/ijisa.2015.01.08
Fuzzy Logic Toolbox. User’s Guide for Use with MATLAB, MathWorks, Inc. 1998.
MATLAB – Genetic Algorithm and Direct Search Toolbox. User’s Guide, MathWorks, Inc., 2004.
S. Yordanova, “Intelligent approaches for linear controllers tuning with application to temperature control”, Journal of Intelligent and Fuzzy Systems, IOS Press, NL, vol. 27(6), 2014, pp. 2809-2820.
S. Yordanova, and Y. Sivchev, “Design and tuning of parallel distributed compensation-based fuzzy logic controller for temperature”, Journal of Automation and Control, vol. 2( 3), 2014, pp. 79-85.
S. Yordanova, and A. Georgieva, “Genetic algorithm based optimization of fuzzy controllers tuning in level control”, J. Electrotechnica and Electronica E+E, vol. 48(9-10), 2013, pp. 45-51.
S. Yordanova, Design of Fuzzy Logic Controllers for Robust Process Control. Sofia: KING, 2011.