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Smart Bird Cage Based on STM32 for Turtledove Bird Using Solar Panel Hakim, Oktafian Sultan; Rifadil, Mochammad Machmud; Putra, Putu Agus Mahadi
Interdisciplinary Social Studies Vol. 1 No. 3 (2021): Reguler Issue
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/iss.v1i3.43

Abstract

Background: One type of bird that is widely bred and maintained is the turtledove. At turtledove breeding, bird cage maintenance is still done manually for cage lighting, feeding and drinking. Manual maintenance of the cage will make it difficult for farmers who have a fairly busy schedule or often leave the house. Aim: For that reason, the authors decided to make a Smart Bird Cage. Method: In this study, the authors used STM32F4 Discovery and ESP8266 as control centers via smartphones. The sensors that will be used are the temperature sensor and the Light Dependent Sensor (LDR). The outputs are 5 volt dc motor, 5V dc pump motor, buzzer and exhaust fan using on/off control with internet of things (IoT). While the control system for heating the cage uses the PWM control method on the AC module control for the brightness level of incandescent lamps. Findings: The results of this study are the temperature in the bird cage is controlled with a heating lamp that turns on when the temperature is below 30 oC and with the IoT system used by farmers, they do not have to worry about managing bird cage consumption when outside the city.
Design of an EBNN-PID based adaptive charge controller for variable DC charging applications Rifadil, Mochammad Machmud; Putra, Putu Agus Mahadi; Muklis, Amalia
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2634-2644

Abstract

This paper presents an adaptive charging system for lithium-ion batteries using an Elman backpropagation neural network (EBNN) integrated with a PID controller and a ZETA converter. The system dynamically identifies the battery type and adjusts the charging voltage accordingly. The EBNN model was trained using 1441 samples of initial current and voltage data, achieving a mean squared error (MSE) of 7.64×10⁻¹⁴. A ZETA converter enables both step-up and step-down voltage regulation, while the PID controller ensures stability toward the predicted setpoint. Simulations in Simulink were conducted on four lithium-ion battery types with setpoints of 4.4 V, 8.8 V, 14.4 V, and 21.6 V. The results show that the PID-regulated output voltage closely matches the target with a maximum deviation of ±0.05V and an average voltage error of 0.1725%. The system achieves fast response times between 0.015 and 0.033 seconds. Extended testing through 24 randomized trials confirmed consistent identification and regulation across varying battery types. These findings validate the proposed EBNN-PID-based charging system as a highly accurate, flexible, and efficient solution for managing lithium-ion battery charging in real-time embedded applications.