Enhanced Fuzzy Logic Controller for Simulated HE Temperature Control System

Abstract:

This paper presents the performances of an enhanced fuzzy logic controller (EFLC) for simulated Heat Exchanger (HE) temperature control system. The HE system is modeled mathematically using Energy Balance Equation and simulated using MATLAB/Simulink software. The Fuzzy Inference Structure (FIS) used was Sugeno-type. EFLC comprises of two parts which are normalized FLC part and model reference (MR) part. Both normalized and MRFLC part was using Gaussian membership function (MF) with 7x7 rule bases. Set Point (SP) tests conducted for change from 43°C to 39°C, 39°C to 35°C and 43°C to 35°C. The performances on SP tests of the FLC and proposed EFLC were compared to PID controller. The results showed that EFLC produced lower decay ratio (DR) with less oscillations, reduced undershoot (US), shorter settling time (T_s) and minimum Integral Absoluter Error (IAE) compare to FLC and PID controller.

• Periodical: Advanced Materials Research (Volume 1113)
• Pages: 715-722
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1113.715
• Online since: July 2015
• Authors: Mohd Aizad Ahmad*, Abdul Aziz Ishak, Kamariah Noor Ismail
• Keywords: Enhanced Fuzzy Logic Controller, Set Point Tests, Simulated HE, Temperature Control

References

[1] Shah, R.K. and Sekulic, D.P. (2003). Classification according to construction features in Fundamental of Heat Exchanger Design, Chap. 1, Classification of Heat Exchangers, 1st ed., John Wiley & Son Inc, pp.12-14.

[2] Hongquan, Q, & Li- ping, P. (2008). Fuzzy-PI algorithm for manned module thermal control. Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, China, 978-1-4244-2114-5.

DOI: 10.1109/wcica.2008.4593099

[3] Maidi, A., Diaf, M., & Corriou, J. (2008). Optimal linear PI fuzzy controller design of a HE. Journal of Chemical Engineering and Processing, vol. 47 , p.938–945.

DOI: 10.1016/j.cep.2007.03.008

[4] Padhee, S., & Singh, Y. (2010). A comparative analysis of various control strategies implemented on HE system: A case study. Proceedings of the World Congress on Engineering, London, U. K, 978-988-18210-7-2, vol. II.

[5] Mazinan, A.H., & Sadati, N. (2010). On the application of fuzzy predictive control based on multiple models strategy to a tubular HE system. Transactions of the Institute of Measurement and Control, pp.395-418, vol. 32.

DOI: 10.1177/0142331209345153

[6] Jain, R., Sivakumaran, N., & Radhakrishnan, T.K. (2011). Design of self tuning fuzzy controllers for nonlinear systems. Expert Systems with Applications, vol. 38, p.4466–4476.

DOI: 10.1016/j.eswa.2010.09.118

[7] Zhang, W., Li, X., Li, L., Lv, J., Chen, Y., & Mao, X. (2011). Design and application of fuzzy controller. Key Engineering Materials, vol. 464, pp.107-110.

DOI: 10.4028/www.scientific.net/kem.464.107

[8] Seborg, D. E., Edgar, T. F., & Mellichamp, D. A. (2004). Guidelines for common control loops in Process Dynamics and Control, Chap. 12, PID Controller Design, Tuning and Troubleshooting, 2nded., John Wiley & Son Inc, p.326.

[9] Transient unit step responses of first order and second order systems, pp.6-7, Retrieved 3 June 2011, from seit. unsw. adfa. edu. au/coursework/ZEIT3215/responses. pdf.

[10] Jie, D. (2011). Adaptive fuzzy PID arithmetic applied in water tank temperature control. Applied Mechanics and Materials, vols. 58-60, pp.1602-1607.

DOI: 10.4028/www.scientific.net/amm.58-60.1602

[11] Baharudin, R. (2006). Optimum PID controller settings: Effect of various tuning rules to process controllability. Research Project, Fakulti Kejuruteraan Elektrik, UiTM Shah Alam.

[12] Nasir, D. F. (2009). Controllability of flow, pressure and temperature using fuzzy logic controller. Research Project, Fakulti Kejuruteraan Kimia, UiTM Shah Alam.

[13] Imal, E. (2009). CDM based controller design for nonlinear HE process. Turk J ElecEng & Comp Sci, Vol. 17, No. 2.

[14] Jouili, K., Jerbi, H. and Braiek, N.B. (2010). An advanced fuzzy logic gain scheduling trajectory control for nonlinear systems. Journal of Process Control, vol. 20, p.426–440.

DOI: 10.1016/j.jprocont.2010.01.001

[15] Pratishthananda,S., Chatthana-anan, T., & Glankwamdee, W. (2002). GA-Fuzzy supervisory PI controller of a HE, "IEEE Catalogue, No. 01 CH37239.

DOI: 10.1109/tencon.2001.949692

[16] Vasičkaninová, A. and Bakošová, M. (2009). Neural network predictive control of a chemical reactor, ActaChimicaSlovaca, vol. 2, no. 2, p.21 – 36.

[17] Dovˇzan, D. and Skrjanc, I. (2010). Fuzzy predictive functional control with adaptive fuzzy model. IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, Timisora, Romania, pp.143-147.

DOI: 10.1109/icccyb.2010.5491310

[18] Nithya, S., Gour, A. S., Sivakumaran, N., Radhakrishnanand, T.K., & Anantharaman, N. (2007). Predictive controller design for a shell and tube HE, presented at International Conference on Intelligent and Advanced Systems.

DOI: 10.1109/icias.2007.4658550

[19] Kokate, R.D., & Waghmare, L.M. (2009). IMC-PID and predictive controller design for a shell and tube HE in Second International Conference on Emerging Trends in Engineering and Technology, pp.1037-1041.

DOI: 10.1109/icetet.2009.120

[20] Das, S.K. (2006). Advantages of shell-and-tube HEs in Process Heat Transfer, Chap. 6, Shell-and-tube HEs, 1st ed., Alpha Science International Ltd., p.234.