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All Journal Faktor Exacta International Journal of Economics, Business and Accounting Research (IJEBAR) Jurnal Ilmiah Edunomika (JIE) Budimas : Jurnal Pengabdian Masyarakat Jurnal Bumigora Information Technology (BITe) International Journal of Computer and Information System (IJCIS) Penamas: Journal of Community Service Journal Of Information System And Artificial Intelligence Journal of Soft Computing Exploration
Efendi, Tino Feri
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Journal : Journal of Soft Computing Exploration

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Lightweight discrete q-learning for self-tuning PID on ESP32: Robustness evaluation and cross-volume adaptation in egg incubators Efendi, Tino Feri; Zainal Arifin
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.8

Abstract

Temperature stability is the most crucial factor in the success of the egg incubation process. The use of conventional Proportional–Integral–Derivative (PID) control with static Ziegler–Nichols tuning often fails to adapt to external disturbances and thermal dynamics, leading to temperature overshoot that can be fatal to embryo survival. This study proposes the implementation of an adaptive PID controller using a Discrete Q-Learning method based on Edge-AI on an ESP32 microcontroller. Experimental results under standard conditions show that the Q-Learning method successfully reduces overshoot by up to 81.8%, limiting the temperature spike to only 0.2°C above the target of 38.0°C, and accelerating the stabilization time by 76.9% with a reduction in IAE of 52.5%. In the dynamic disturbance rejection test, the adaptive system validated the algorithm's robustness against dynamic disturbances. Furthermore, cross-environment adaptation evaluation by reducing the incubator volume by 50% demonstrates the agent’s autonomous adaptation capability, eliminating overshoot entirely (0.000°C) without parameter recalibration and reducing IAE by 55.1% compared to static PID. This study concludes that the implementation of Q-Learning on low-cost hardware produces a robust, precise, and autonomously adaptive thermal control system for agricultural technology applications.
Co-Authors Akkapin, Supaphorn Budiyono Budiyono Dewi Muliasari Indra Lila Kusuma, Indra Lila M. Gunawan Setyadi Maya Widyana Dewi Moch. Bagoes Pakarti, Moch. Bagoes mubarok, wahib Muftisany, Hafidz Muliasari, Dewi Muqorobin, Muqorobin Purwati, Ika Diah Rais, Nendy Akbar Rozaq Rozaq Rais, Nendy Akbar Saputra, Aditya Aji Setiawan, Ferry Illham Siti Rokhmah SUMADI SUMADI Suprihati - Tho’in, Muhammad Wikan Budi Utami Zainal Arifin