Exhaust fan speed controller using fuzzy logic controller

Khairudin, Moh

Abstract

Fuzzy logic is a reasoning method for solving linguistic problems that cannot be solved mathematically. The application of fuzzy logic from our development program regarding the development of exhaust fans with fuzzy logic controllers to regulate air flow velocity in accordance with air conditions in a room. Using a gas sensor and a temperature sensor will create a number of conditions to then be treated in a fuzzy logic system using the Mamdani method. The process that occurs in the Mamdani fuzzy includes four stages including the process of calculating the fuzzy set (fuzzification), the application of the implication function, the composition of the rules to the last stage, the defuzzification process. The inter elements of the mamdani fuzzy process are interconnected in the sense that the order determined by fuzzy mamdani cannot be removed or replaced by another. Development that has been done related to the exhaust fan uses two sensors namely the temperature sensor and gas sensor. Used as a detector for the amount of harmful gas in a room so that it activates the exhaust fan to eliminate it at a speed consistent with the gas content in the room. Also used to measure the temperature of the room and then activate the exhaust fan so that the room can be inhabited comfortably. Fuzzy logic controller system is used to adjust the rotational speed of the exhaust fan. ? 2021 Published under licence by IOP Publishing Ltd.

References

Agus, S. P., & Triyogi, Y. (2018). The effect of inner fan blade angle to the ventilation rate of the turbine ventilator. Jurnal Rekayasa Mesin, 9, 227-233. Retrieved from www.scopus.com

Aslan, D., & Altintas, Y. (2018). On-line chatter detection in milling using drive motor current commands extracted from CNC. International Journal of Machine Tools and Manufacture, 132, 64-80. doi:10.1016/j.ijmachtools.2018.04.007

Faisal, M., Hedjar, R., Al Sulaiman, M., & Al-Mutib, K. (2013). Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment. International Journal of Advanced Robotic Systems, 10 doi:10.5772/54427

Fauziyah, M., Adhisuwignjo, S., Rifai, M., & Dewatama, D. (2018). Based fuzzy logic temperature control for a coffee roaster machine. Paper presented at the IOP Conference Series: Materials Science and Engineering, , 434(1) doi:10.1088/1757-899X/434/1/012202 Retrieved from www.scopus.com

Gouda, M. M., Danaher, S., & Underwood, C. P. (2001). Thermal comfort based fuzzy logic controller. Building Services Engineering Research and Technology, 22(4), 237-253. doi:10.1177/014362440102200403

Khairudin, M. (2016). Quantitative feedback theory-based robust control for a spindle of lathe machine. International Journal on Smart Sensing and Intelligent Systems, 9(4), 1776-1794. doi:10.21307/ijssis-2017-939

Khairudin, M., Refalda, R., Yatmono, S., Pramono, H. S., Triatmaja, A. K., & Shah, A. (2020). The mobile robot control in obstacle avoidance using fuzzy logic controller. Indonesian Journal of Science and Technology, 5(3), 334-351. doi:10.17509/ijost.v5i3.24889

Mohammed, R., Ali, R., & Sung-Ho, K. (2018). Fuzzy logic and fuzzy PID controllers. From International Journal of Fuzzy Logic and Intelligent Systems, 18 Retrieved from www.scopus.com

Wei, X. (2011). Acceleration and deceleration control design of step motor based on TMS320F240. Paper presented at the Procedia Engineering, , 15 501-504. doi:10.1016/j.proeng.2011.08.095 Retrieved from www.scopus.com

Zuhrie, M. S., Basuki, I., & Anifah, L. (2017). Design of an artificial intelligence robot as a tool for teaching media based on contextual teaching and learning. Jurnal Pendidikan Dan Teknologi Kejuruan, 23(4), 369-373. Retrieved from www.scopus.com