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Section Innovation in Electrical Engineering

Centre of Gravity Based Fuzzy Logic Control for Autoclave Pressure Stabilisation with Internet of Things Monitoring

Pengendalian Logika Fuzzy Berbasis Pusat Gravitasi untuk Stabilisasi Tekanan Autoklaf dengan Pemantauan Internet of Things
Vol. 27 No. 4 (2026): October:
DOI : https://doi.org/10.21070/ijins.v27i4.2328
Published : Sep 1, 2026

Agus Hayatal Falah (1), Izza Anshory (2), Arief Wisaksono (3), Syamsudduha Syahrorini (4), Shazana Dhiya Ayuni (5)

(1) Program Studi Teknik Elektro, Fakultas Sains dan Teknologi, Universitas Muhammadiyah Sidoarjo, Indonesia
(2) Program Studi Teknik Elektro, Fakultas Sains dan Teknologi, Universitas Muhammadiyah Sidoarjo, Indonesia
(3) Program Studi Teknik Elektro, Fakultas Sains dan Teknologi, Universitas Muhammadiyah Sidoarjo, Indonesia
(4) Program Studi Teknik Elektro, Fakultas Sains dan Teknologi, Universitas Muhammadiyah Sidoarjo, Indonesia
(5) Program Studi Teknik Elektro, Fakultas Sains dan Teknologi, Universitas Muhammadiyah Sidoarjo, Indonesia
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Abstract:

General Background Autoclave sterilization relies on pressurized steam, making stable pressure and temperature important process parameters. Specific Background A previous Arduino and Internet of Things autoclave used binary on-off heater regulation, which may generate repeated switching around the setpoint because stored thermal energy can continue raising pressure after the heater is switched off. Knowledge Gap The existing system therefore requires a nonbinary regulation structure capable of adjusting heating intensity according to pressure conditions rather than relying solely on on-off switching. Aims This study develops a Mamdani fuzzy logic controller using normalized pressure error and change of error as inputs and heater duty cycle as the output obtained through Center of Gravity defuzzification. Results Five linguistic sets for each variable generated 25 fuzzy rules. Static inference at zero change of error produced approximately 2.67–3.04% output above the setpoint, 20% at the setpoint, and 32.59–75.00% below the setpoint, demonstrating progressively adjusted heating action. Novelty The proposed configuration extends the existing Arduino and Internet of Things autoclave architecture by integrating normalized error, Mamdani inference, Center of Gravity defuzzification, and time-proportional heater operation. Implications The structure provides a basis for smoother pressure regulation and traceable monitoring, but hardware testing remains necessary before claims regarding overshoot, settling time, relay switching, or energy consumption can be established.


Highlights:



  • Twenty five IF THEN rules map normalized pressure error and error change to duty cycle.

  • Above setpoint conditions yielded COG outputs near 2.7–3.0%, whereas lower pressures required 32.59–75.00%.

  • Dynamic hardware validation remains necessary before overshoot, settling time, switching, or energy claims.


Keywords: Autoclave, Center of Gravity, Fuzzy Logic Control, Internet of Things, Pressure Control

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References

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