Fuzzy logic to adjust room temperature depending on the number of people
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Published:
May 30, 2023
Keywords:
ATMega 328, Controlling Temprature, Tsukamoto Fuzzy Logic, System Performance
Section
Decision Support System
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Abstract
This study focuses on discussing how fuzzy logic can be used in temperature control systems. To avoid unsuitable and excessive performance as a result of the growing number of individuals entering the room, fuzzy logic is utilized to determine the amount of values provided. The effectiveness of the system in controlling the temperature and the quantity of individuals entering the space is its performance. When people enter the space, the sensor system counts them to establish the room's capacity. The ATmega328 microcontroller was used in the creation of this system. The quantity of people entering the room is forecast using PIR and ultrasonic sensors. To prevent system failure, the maximum number is restricted to 250 persons for calculating and evaluating system performance. In this case, the Tsukamoto fuzzy logic model was applied, with experimental results showing increased accuracy in determining how many rotations the dynamo and relay experience. The measured average value of the results obtained an accuracy of 54.11%, and the system shows that it is more stable than without applying the fuzzy logic model.
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How to Cite
Siregar, M. F. and Imam, C. . (2023) “Fuzzy logic to adjust room temperature depending on the number of people”, Jurnal Mantik, 7(1), pp. 358-365. doi: 10.35335/mantik.v7i1.3792.
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Siregar, M. F., & Chairul Imam. (2022). Application of the Fuzzy Logic Method in Determining the Volume of Water Discharge to the Number of Humans Based on a Microcontroller. Journal of Science Technology (JoSTec), 4(1), 187–192. https://doi.org/10.55299/jostec.v4i1.260
Siregar, R., & Zarlis, M. (2020). Tsukamoto ’ s Fuzzy Logic Development Analysis to Predict Caesarean or Normal Delivery. 152–157.
Soeharwinto, Agrath, S., Pane, Z., & Nasution, T. H. (2019). Benchmarking of Wireless Microcontroller-based Three Phase Multi Meter with Industrial Standard Instrument. 2019 3rd International Conference on Electrical, Telecommunication and Computer Engineering, ELTICOM 2019 - Proceedings, 98–101. https://doi.org/10.1109/ELTICOM47379.2019.8943844
Tavakoli, P., & Karimpour, A. (2021). A New Approach Based on Fuzzy-Adaptive Structure & Parameter Learning Applied in Meta-Cognitive Algorithm for ANFIS.
Todorovic, M., & Simic, M. (2020). Computational intelligence and automated methods for control fuzzy system design. IEEE International Conference on Fuzzy Systems, 2020-July, 0–5. https://doi.org/10.1109/FUZZ48607.2020.9177550
Unger, M., Fries, G., Steinecke, T., Waghmare, C., & Ramaswamy, R. (2019). Functional Safety Test Strategy for Automotive Microcontrollers during Electro-Magnetic Compatibility Characterization. EMC COMPO 2019 - 2019 12th International Workshop on the Electromagnetic Compatibility of Integrated Circuits, 49–51. https://doi.org/10.1109/EMCCompo. 2019.8919673
Wang, J., Jin, J., Zhu, J., Guo, Y., & Xing, Y. (2020). Unified Control of APF and SMES Based on Fuzzy Logic Control. 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2020, 2020–2021. https://doi.org/10.1109/ASEMD49065. 2020.9276216
Zhang, P., & Shen, Q. (2019). A Novel Framework of Fuzzy Rule Interpolation for Takagi-Sugeno-Kang Inference Systems. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE),1-6.
Zulkifli, S. A., Tan, M. F. M. F., & Yusof, M. J. M. (2020). Study on Power Converters Control in Hardware System Using Low Cost Microcontroller. 2020 IEEE Student Conference on Research and Development, SCOReD 2020, September, 401–405. https://doi.org/10.1109/ SCOReD50371.2020.9251039
Al-mahturi, A., Santoso, F., Garratt, M. A., & Anavatti, S. G. (n.d.). An Intelligent Control of an Inverted Pendulum Based on an Adaptive Interval Type-2 Fuzzy Inference System. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–6.
Alrashoud, M. (n.d.). Hierarchical Fuzzy Inference System for Diagnosing Dengue Disease. 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 31–36. https://doi.org/10.1109/ICMEW.2019.00013
Alves, E., Tanscheit, R., & Vellasco, M. (2019). SENFIS - Selected Ensemble of Fuzzy Inference Systems. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–6.
Cherniy, S. P., Buzikayeva, A. V., & Gudim, A. S. (2019). A Model of Multi-Cascade Fuzzy Logic Controller Implemented Using Different Variations of Inference Algorithms. 2019 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2019, 15–18. https://doi.org/10.1109/FarEastCon.2019.8934872
Cuong, B. C. (2019). Some Fuzzy Inference Processes in Picture Fuzzy Systems. 2019 11th International Conference on Knowledge and Systems Engineering (KSE), 1–5.
Furqon Siregar, Muhammad Imam, C., & Nasution, A. (2020). Pemanfaatan Solenoid Valve Dan Sensor Hc-Sr04 Sebagai Pencuci Tangan Otomatis. 07, 65–71.
Furqon Siregar, M., & Sihombing, P. (n.d.). Analysis of Fuzzy Logic Method for Load Lifting Robot.
Gencer, A. (2019). Analysis of Speed/Position Controller Based on Several Types of a Fuzzy Logic for Travelling Wave Ultrasonic Motor. Proceedings - 2019 IEEE 1st Global Power, Energy and Communication Conference, GPECOM 2019, 170–174. https://doi.org/10.1109/GPECOM.2019.8778619
Giang, N. L., Truong, V. O., Ngoc, N. H. U., & Hai, P. V. A. N. (2020). A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making. 8. https://doi.org/10.1109/ACCESS.2020.3021097
Jian-Guo, G., & Jun, Z. (2008). Altitude control system of autonomous airship based on fuzzy logic. 2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics, ISSCAA 2008. https://doi.org/10.1109/ISSCAA.2008.4776171
Juniawan, F. P., & Pradana, H. A. (2018). Design Fuzzy Expert System And Certainty Factor In Early Detection Of Stroke Disease.
Kabir, M. (2021). Fuzzy membership function design?: An adaptive neuro-fuzzy inference system ( ANFIS ) based approach. 1–5.
Kafiev, I. (2020). ? ontrol System of Portal Car Wash based on the Mamdani Fuzzy Algorithm. 1–6.
Kamide, N. (2020). Sequential Fuzzy Description Logic: Reasoning for Fuzzy Knowledge Bases with Sequential Information. Proceedings of The International Symposium on Multiple-Valued Logic, 2020-Novem, 218–223. https://doi.org/10.1109/ISMVL49045.2020.000-2
Kerk, Y. W., Tay, K. M., & Lim, C. P. (2021). Monotone Fuzzy Rule Interpolation for Practical Modelling of the Zero-Order TSK Fuzzy Inference System. 6706(2). https://doi.org/10.1109/TFUZZ.2021.3057239
Ledeneva, T. (2020). Special Aspects of the Design of Fuzzy Inference Mechanism.
Lee, T., Moon, T., Kim, S. J., Yoon, S., & Member, S. (2016). Regularization and Kernelization of the Maximin Correlation Approach. IEEE Access, 4, 1385–1392. https://doi.org/10.1109/ACCESS.2016.2551727
Linh Nguyen. (2020). Integrating The Probabilistic Uncertainty to Fuzzy Systems in Fuzzy Natural Logic. International Conference on Knowladge and Systems Engineering (KSE), 1–23.
Madrid-herrera, L., Chacon-murguia, M. I., Posada-urrutia, D. A., & Ramirez-quintana, J. A. (n.d.). Inference System. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–6.
Napitupulu, S., Nababan, E. B., & Sihombing, P. (2020). Comparative Analysis of Fuzzy Inference Tsukamoto Mamdani and Sugeno in the Horticulture Export Selling Price. 183–187.
Ontiveros-robles, E., Melin, P., & Castillo, O. (n.d.). Relevance of Polynomial Order in Takagi-Sugeno Fuzzy Inference Systems Applied in Diagnosis Problems. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 3, 1–6.
Pekaslan, D., Member, S., Wagner, C., Member, S., & Garibaldi Senior Member, J. M. (2020). IEEE TRANSACTIONS ON FUZZY SYSTEMS 1 ADONiS-Adaptive Online Non-Singleton Fuzzy Logic Systems. 1–10.
Schilcher, U., Toumpis, S., Haenggi, M., Crismani, A., Brandner, G., Bettstetter, C., & Member, S. (2016). Interference Functionals in Poisson Networks. 62(1), 370–383. https://doi.org/10.1109/TIT.2015.2501799
Selvachandran, G., Quek, S. G., Thi, L., Lan, H., Son, L. H., Giang, N. L., & Ding, W. (2019). IEEE TRANSACTIONS ON FUZZY SYSTEMS A New Design of Mamdani Complex Fuzzy Inference System for Multi-attribute Decision Making Problems. IEEE Transactions on Fuzzy Systems, PP(c), 1. https://doi.org/10.1109/TFUZZ.2019.2961350
Shishkin, I. E., Grekov, A. N., & Nikishin, V. V. (2019). Intelligent Decision Support System for Detection of Anomalies and Unmanned Surface Vehicle Inertial Navigation Correction. Proceedings - 2019 International Russian Automation Conference, RusAutoCon 2019, 1–6. https://doi.org/10.1109/RUSAUTOCON.2019.8867601
Siregar, M. F., & Chairul Imam. (2022). Application of the Fuzzy Logic Method in Determining the Volume of Water Discharge to the Number of Humans Based on a Microcontroller. Journal of Science Technology (JoSTec), 4(1), 187–192. https://doi.org/10.55299/jostec.v4i1.260
Siregar, R., & Zarlis, M. (2020). Tsukamoto ’ s Fuzzy Logic Development Analysis to Predict Caesarean or Normal Delivery. 152–157.
Soeharwinto, Agrath, S., Pane, Z., & Nasution, T. H. (2019). Benchmarking of Wireless Microcontroller-based Three Phase Multi Meter with Industrial Standard Instrument. 2019 3rd International Conference on Electrical, Telecommunication and Computer Engineering, ELTICOM 2019 - Proceedings, 98–101. https://doi.org/10.1109/ELTICOM47379.2019.8943844
Tavakoli, P., & Karimpour, A. (2021). A New Approach Based on Fuzzy-Adaptive Structure & Parameter Learning Applied in Meta-Cognitive Algorithm for ANFIS.
Todorovic, M., & Simic, M. (2020). Computational intelligence and automated methods for control fuzzy system design. IEEE International Conference on Fuzzy Systems, 2020-July, 0–5. https://doi.org/10.1109/FUZZ48607.2020.9177550
Unger, M., Fries, G., Steinecke, T., Waghmare, C., & Ramaswamy, R. (2019). Functional Safety Test Strategy for Automotive Microcontrollers during Electro-Magnetic Compatibility Characterization. EMC COMPO 2019 - 2019 12th International Workshop on the Electromagnetic Compatibility of Integrated Circuits, 49–51. https://doi.org/10.1109/EMCCompo. 2019.8919673
Wang, J., Jin, J., Zhu, J., Guo, Y., & Xing, Y. (2020). Unified Control of APF and SMES Based on Fuzzy Logic Control. 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2020, 2020–2021. https://doi.org/10.1109/ASEMD49065. 2020.9276216
Zhang, P., & Shen, Q. (2019). A Novel Framework of Fuzzy Rule Interpolation for Takagi-Sugeno-Kang Inference Systems. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE),1-6.
Zulkifli, S. A., Tan, M. F. M. F., & Yusof, M. J. M. (2020). Study on Power Converters Control in Hardware System Using Low Cost Microcontroller. 2020 IEEE Student Conference on Research and Development, SCOReD 2020, September, 401–405. https://doi.org/10.1109/ SCOReD50371.2020.9251039
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