Fuzzy logic based household oven design

Abstract

This paper presents the real-time household oven design application of the microcontroller-based fuzzy control system of temperature. Because of the complexity, nonlinearity, and dead time, temperature control of the oven is still delegated to many factors. In the computer-aided design process, the mathematical model of the system is derived with an open-loop response. The extracted model is used to obtain the conventional PI control parameters and adjust the PI type fuzzy rule base, which is mainly derived intuitively from the control engineering point of view. Using traditional PI parameters and Fuzzy logic PI rule base, real-time temperature control was realized with the PWM signal generated at the output of the PIC18F4520 microcontroller. The PWM signal produced at the output of the PIC18F4520 microcontroller is used to drive the power circuit designed using IGBT's. Oven responses are obtained for both Fuzzy and PI control systems, in which the developed PI fuzzy control has better performance with less overshoot, less rising and settling time, and smaller steady-state error. A detailed description of designed control systems and experimental works with performance are given.

Keywords

• Real-time control
• PI type fuzzy control
• temperature control
• microcontrollers

References

1. [1] S. Yilmaz, A. Rinaldi , M.K. Patel, DSM inter-actions: What is the impact of appliance energy efficiency measures on the demand response (peak load management), Energy Policy 139 (2020) 111323.
2. [2] M. Lucchia, N. Suzzib, M. Lorenzini, Dynamic model for convective heating of a wet brick during energy characterisation of domestic electric ovens, Applied Thermal Engineering 161 (2019) 114117 .
3. [3] H. Mamdani, S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, vol. 7, 1974.pp. 1-13.
4. [4] M. Sugeno, Industrial applications of fuzzy control. Amsterdam, Elsevier, 1985.
5. [5] U. C. Moon, K. Y. Lee, "Hybrid algorithm with fuzzy system and conventional PI control for the temperature control of TV glass furnace," IEEE Transactions on Control Systems Technology, vol. 11, 2003, pp. 548–554.
6. [6] R. E. Precup, H. Hellendoorn, "A survey on industrial applications of fuzzy control," Computers in Industry, vol. 62, 2011, pp. 213–226. [7] M. Ramirez, et al., "Fuzzy control of a multiple hearth furnace," Computers in Industry, vol. 54, 2004, pp. 105-113.
7. [8] S. Dequan, et al., "Application of Expert Fuzzy PID Method for Temperature Control of Heating Furnace," Procedia Engineering, vol. 29, 2012, pp. 257–261.
8. [9] R. Jain, et al., "Design of self-tuning fuzzy con-trollers for nonlinear systems," Expert Systems with Applications, vol. 38, 2011, pp. 4466–4476.

9. [10] O. Lepreux, et al., "Model-based temperature control of a diesel oxidation catalyst," Journal of Process Control, vol. 12, 2002, pp. 695–705.
10. [11] Z. R. Radakovic, et al., "Application of temperature fuzzy controller in an indirect resistance furnace," Appl Energy, vol. 73, 2002, pp. 167–81.
11. [12] G. Bal, et al., "Fuzzy logic based DSP controlled servo position control for ultrasonic motor," Energy Conversion and Management, vol. 45, 2004, pp. 3139–3153.
12. [13] A. Cabrera, et al., "Hardware/software codesign of configurable fuzzy control systems," Applied Soft Computing, vol. 4, 2004, pp. 271–285.
13. [14] M. Frankowiak, et al., "A review of the evolution of microcontroller-based machine and process monitoring," International Journal of Machine Tools and Manufacture, vol. 45, 2005, pp. 573-582.
14. [15] S. Khan, et al., "Design and Implementation of an Optimal Fuzzy Logic Controller Using Genetic Algorithm," Journal of Computer Science, vol. 4, 2008, pp. 799-806.
15. [16] Y. S. Kung, et al., "Simulink/Modelsim Co-Simulation and FPGA Realization of Speed Control IC for PMSM Drive," Procedia Engineering, vol. 23, 2011, pp. 718–727.
16. [17] W. K. Roots, F. Walker, "Disconitinuously-controlled thermal processes," J.Inst.Fuel, vol. 37, 1964, pp. 397-414.
17. [18] W. K. Roots, F. Walker, "Design parameters for thermal processes with on-off controllers," J.Inst. Fuel, vol. 41, 1968, pp. 5-15.
18. [19] P. Xiaohong, et al., "Research and Application on GA-based Two-stage Fuzzy Temperature Control System for a Type of Industrial Furnace," in International Conference on Electrical and Control Engineering, China, 2010, pp. 1558-1561.
19. [20] J. Jing, Z. Xuesong, "Research on Fuzzy-PID control algorithm from the temperature control system," in Computer Science and Information Technology (ICCSIT), 3rd IEEE International Conference, China, 2010, pp. 152 - 155 .
20. [21] X. Liu, et al., "Research and Application of the Temperature Fuzzy-PID Control System for Thermo-Mechanical Simulator," in Control and Decision Conference (CCDC), China, 2010, pp. 3434 - 3438.
21. [22] W. K. Roots, F. Walker, "Discontinuously-controlled thermal process economics," J. Inst. Fuel, vol. 39, 1966, pp. 429-431.
22. [23] L. Wang, et al., "Study on Sunlight Greenhouse Temperature and Humidity Fuzzy Control System," Nature and Science, vol. 3, 2005, pp. 45-48.
23. [24] M. Amengual, et al., "System identification through first and second-order information from the periodogram," in Circuits and Systems, 1988., IEEE International Symposium on, 1988, pp. 1341-1344 vol.2.
24. [25] S. M. Elashhab, M. A. Zohdy, "PEM fuel cell system identification and model order reduction for Hinfin; loop shaping controller design," in Electro/Information Technology, 2008. EIT 2008. IEEE International Conference on, 2008, pp. 60-65.
25. [26] L. Ljung, System Identification Toolbox for Use with MATLAB User's Guide: The Math-Works, Inc., 1995.
26. [27] T. F. Coleman, Y. Zhang, Optimization ToolboxTM User's Guide: The MathWorks, Inc., 2012.
27. [28] L. Reznik, Fuzzy Controllers. Oxford: Reed Educational and Professional Publishing Ltd, 1997.