Comparative analysis of PID and fuzzy logic controller: A case of furnace temperature control
Keywords:fuzzy logic, furnace temperature controller, membership functions, peak overshoot, PID, settling time
Furnace temperature controller has a large overshoot and constant oscillation error. To solve this problem there are several studies done on the PID type furnace temperature controller with different PID parameters, but this method is not efficient because of the nonlinearity of temperature. Due to this reason, the overshoot happens and steady-state errors are observed. Other researchers have shown that the inclusion of one more controller with a PID controller, such as a fuzzy logic controller can improve the results as compared to the use of the PID controller alone. The objective of this research is to experiment on the PID and fuzzy logic controller hardware and compare the results with those obtained from the simulation. In addition to this, the objective also is to find out the type of controller that would be most efficient in terms of settling time and the overshoot. This paper presents the comparison of PID and fuzzy logic controller simulation and experimentation on the hardware of the same. Results show that the fuzzy logic controller is slightly better than the PID controller in terms of the settling time. The PID controller is better than fuzzy logic in terms of peak overshoot. Better results can be obtained from the fuzzy logic controller by increasing the number of inputs or membership functions.
Al-Mashakbeh, A. S. O. (2009). Proportional integral and derivative control of brushless dc motor. European Journal of Scientific Research, 35(2), 198-203.
Ang, K. H., Chong, G., & Li, Y. (2005). PID control system analysis, design, and technology. IEEE transactions on control systems technology, 13(4), 559-576. DOI: 10.1109/TCST.2005.847331
Arulmozhiyal, R., & Kandiban, R. (2012, January). Design of fuzzy PID controller for brushless DC motor. In Computer Communication and Informatics (ICCCI), 2012 International Conference on (pp. 1-7). IEEE. DOI: 10.1109/ICCCI.2012.6158919
Arya, R. K. (2007). Analytical structures and analysis of simplest fuzzy PD controller with asymmetrical/symmetrical, trapezoidal/triangular/singleton output membership function. INTERNATIONAL JOURNAL OF COMPUTATIONAL COGNITION (HTTPS://WWW.IJCC.US), 5(2), 10-24.
Asere, H., Lei, C., & Jia, R. (2015, October). Cruise control design using fuzzy logic controller. In Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on (pp. 2210-2215). IEEE DOI: 10.1109/SMC.2015.386
Bil, Z., & Butkiewicz, B. S. (1999). Furnace temperature control with fuzzy microcontroller. In Soft Computing Methods in Industrial Applications, 1999. SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269), Kuusamo, Finland, 1999, pp. 75-79, DOI: 10.1109/SMCIA.1999.782711
Edalath, S., Kukreti, A. R., & Cohen, K. (2013). Enhancement of a tuned mass damper for building structures using fuzzy logic. Journal of Vibration and Control, 19(12), 1763-1772 DOI: 10.1177/1077546312449034
Elias, N., Yahya, N. M., & Sing, E. H. (2018). Numerical analysis of fuzzy logic temperature and humidity control system in pharmaceutical warehouse using MATLAB fuzzy toolbox. In: Hassan M. (eds) Intelligent Manufacturing & Mechatronics (pp. 623-629). Singapore: Springer. DOI: https://doi.org/10.1007/978-981-10-8788-2_56
Ersoyoglu, A. S., Ata, S., Dincer, K., Önal, G., & Yilmaz, Y. (2017). Modeling of the effects of cyclic voltammetry (CV) using fuzzy logic with different membership functions for proton exchange membrane fuel cell (PEM) with polyvinyl alcohol/nano silver (PVA/Ag). In Nano Hybrids and Composites (Vol. 16, pp. 67-72). Trans Tech Publications Ltd. DOI: https://doi.org/10.4028/www.scientific.net/NHC.16.67
Gaurav, A. K. (2012). Comparison between conventional PID and fuzzy logic controller for liquid flow control: Performance evaluation of fuzzy logic and PID controller by using MATLAB/Simulink. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 1(1), 84-88.
Ghane, M., & Tarokh, M. J. (2012). Multi-objective design of fuzzy logic controller in supply chain. Journal of Industrial Engineering International, 8(1), 10. DOI: https://doi.org/10.1186/2251-712X-8-10
Jackson, A. (1994, February). Fuzzy logic vs traditional approaches to the design of microcontroller-based systems. In Aerospace Applications Conference, 1994. Proceedings, Vail, CO, USA, 1994 IEEE (pp. 19-33). IEEE. DOI: 10.1109/AERO.1994.291206
Kiyak, E., & Gol, G. (2016). A comparison of fuzzy logic and PID controller for a single-axis solar tracking system. Renewables: Wind, Water, and Solar, 3(1), 7. DOI: https://doi.org/10.1186/s40807-016-0023-7
Kumar, Y. P., Rajesh, A., Yugandhar, S., & Srikanth, V. (2013). Cascaded PID controller design for heating furnace temperature control. IOSR Journal of Electronics and Communication Engineering, 5(3), 76-83.
Kumar, P. B., Sujatha, P., & Anjaneyulu, K. S. R. (2013). Design and Analysis of Different Control Strategies for BLDC Motor. International Journal of Innovative Research in Science, Engineering and Technology (IJIREST), 2(9), 4298-4308.
Moon, U. C., & Lee, K. Y. (2003). Hybrid algorithm with fuzzy system and conventional PI control for the temperature control of TV glass furnace. IEEE transactions on control systems technology, 11(4), 548-554. DOI: 10.1109/TCST.2003.813385
Munyaneza, O., Munyazikwiye, B. B., & Karimi, H. R. (2015, November). Speed control design for a vehicle system using fuzzy logic and PID controller. In Fuzzy Theory and Its Applications (iFUZZY), Yilan, 2015 International Conference on (pp. 56-61). IEEE. DOI: 10.1109/iFUZZY.2015.7391894
Pringsakul, N., Puangdownreong, D., Thammarat, C., & Hlangnamthip, S. (2019). Obtaining optimal PIDA controller for temperature control of electric furnace system via flower pollination algorithm. WSEAS Trans. Systems and Control, 14(1), 1-7.
Rabah, M., Rohan, A., & Kim, S. H. (2018). Comparison of position control of a gyroscopic inverted pendulum using PID, fuzzy logic and fuzzy PID controllers. International Journal of Fuzzy Logic and Intelligent Systems, 18(2), 103-110. DOI: https://doi.org/10.5391/IJFIS.2018.18.2.103
Radakovic, Z. R., Milosevic, V. M., & Radakovic, S. B. (2002). Application of temperature fuzzy controller in an indirect resistance furnace. Applied Energy, 73(2), 167-182. DOI: 10.1016/s0306-2619(02)00077-6
Rout, M. K., Sain, D., Swain, S. K., & Mishra, S. K. (2016, March). PID controller design for cruise control system using genetic algorithm. In Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference on (pp. 4170-4174). IEEE. DOI: 10.1109/ICEEOT.2016.7755502
Sakthivel, G., Snehitkumar, B., & Ilangkumaran, M. (2014). Application of fuzzy logic in internal combustion engines to predict the engine performance. International Journal of Ambient Energy, 37(3), 1-11. DOI: 10.1080/01430750.2014.952844
Vaishnav, S. R., & Khan, Z. J. (2007). Design and performance of PID and fuzzy logic controller with smaller rule set for higher order system. In Proceedings of the World Congress on Engineering and Computer Science WCECS 2007, October 24-26, 2007, San Francisco, USA. (pp. 24-26).
Wang, Y., Jin, Q., & Zhang, R. (2017). Improved fuzzy PID controller design using predictive functional control structure. ISA transactions, 71(Part 2), 354-363. DOI: 10.1016/j.isatra.2017.09.005
Xu, L., Xu, T., Wang, J., & Li, X. (2017). A fuzzy PID controller-based two-axis compensation device for airborne laser scanning. IEEE Sensors Journal, 17(5), 1353-1362. DOI: 10.1109/JSEN.2016.2646742
Xu, Z., Di, J., Lu, Y., Liu, T., & Yuan, L. (2019, December). Heating furnace temperature based on WM-GA fuzzy control. In 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chengdu, China, (Vol. 1, pp. 678-682). IEEE. DOI: https://doi.org/10.1109/IAEAC47372.2019.8997924
How to Cite
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.