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Ahmad Dani
Universitas Pembangunan Panca Budi Medan, North Sumatera, Indonesia
Computer Science & IT

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BLDC Motor Speed Control System for Drone Applications Using Tuned PID Control by Particle Swarm Optimization (PSO) Jekli Boy Nainggolan; Beni Satria; Ahmad Dani
INFOKUM Vol. 14 No. 03 (2026): Infokum, May - June 2026
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v14i03.3120

Abstract

This study addresses the challenge of optimal speed control for Brushless DC (BLDC) motors in drone applications, where precise and responsive control is critical for stability and maneuverability. While the Proportional-Integral-Derivative (PID) controller is standard, manual tuning for dynamic drone operations is often suboptimal. This research proposes and investigates the implementation of a PID controller whose parameters are automatically tuned using the Particle Swarm Optimization (PSO) algorithm. The primary objective is to design a superior control system that enhances transient response and robustness against disturbances common in drone flight. The methodology employs a simulation-based experimental design using MATLAB/Simulink. The PSO algorithm optimizes the PID parameters (Kp, Ki, Kd) by minimizing the Integral Time Absolute Error (ITAE) as the fitness function. The performance of the PSO-tuned PID is then compared against a conventionally tuned (Ziegler-Nichols) PID controller under scenarios including step response and load disturbance. The findings demonstrate that the PSO-PID controller significantly outperforms the conventional PID, achieving a 73% reduction in overshoot (from 14.2% to 3.8%), a 25% faster settling time, and a 28.9% quicker recovery from load disturbances. Statistical t-test analysis confirms the significant superiority of the PSO-optimized system (p < 0.001). The implications of this research are substantial, offering an efficient offline tuning method that can be deployed on flight controllers to enhance drone stability, performance, and operational reliability without requiring additional real-time computational power, thereby advancing control system design for unmanned aerial vehicles.
Analysis of Oxygen Sensor Data Stream and Short Term Fuel Trim (STFT)for Detecting Injection System Fault Symptoms in Four-Wheeled Vehicles Firza Ardana; Hamdani Hamdani; Ahmad Dani
INFOKUM Vol. 14 No. 05 (2026): Infokum, 2026
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v14i05.3151

Abstract

The modern EFI system has become the backbone of engine performance control, yet diagnosing faults within it remains a challenge when no Diagnostic Trouble Code (DTC) has been triggered. This study investigates the diagnostic potential of oxygen (O₂) sensor waveforms and Short Term Fuel Trim (STFT) data streams acquired via the OBD-II protocol on a Toyota Innova 1TR-FE. Six experimental conditions were tested—normal idle, normal at approximately 2,500 rpm, injector fault at idle, injector fault at 2,500 rpm, vacuum leak at idle, and vacuum leak at 2,500 rpm—using Toyota Global TechStream (GTS+) as the data acquisition interface. The analysis employed a residual generation approach combined with rule-based decision-level data fusion to distinguish between the three operating states. Results reveal that vacuum leaks produce a distinctive STFT elevation of up to +19.70%, while injector faults cause O₂ sensor voltage to fall to extremely low levels (0.011–0.042 V) with a complete cessation of switching activity (0 Hz). Integrating both parameters through decision-level data fusion enabled unambiguous differentiation among normal, vacuum leak, and injector fault conditions across all tested scenarios. The proposed method achieved a classification accuracy of 100%, surpassing the minimum target of 85%. These findings suggest that combined O₂ sensor and STFT data streams offer a practical and effective foundation for early-stage OBD-II-based fault diagnosis in EFI vehicles.
Development of IoT-Based MPPT System Using Perturb and Observe Algorithm on Solar Panels Irham Purba; Beni Satria; Ahmad Dani
INFOKUM Vol. 14 No. 05 (2026): Infokum, 2026
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v14i05.3164

Abstract

This study successfully designed and implemented an IoT-based Maximum Power Point Tracking (MPPT) system using the Perturb and Observe (P&O) algorithm for a 100 Wp polycrystalline solar panel. The research aims to provide an integrated, cost-effective solution for optimizing solar energy extraction in Indonesia's tropical climate. The methods employed include mathematical modeling, system simulation in MATLAB/Simulink, and hardware implementation using an ESP32 microcontroller with sensors for voltage, current, irradiance, and temperature, integrated with the ThingSpeak IoT platform for real-time monitoring. System validation showed high accuracy with a Root Mean Square Error of 0.83 W. The hardware system achieved an average MPPT tracking efficiency of 95.36% with a response time of 2.05 seconds and power oscillation of 3.17%, contributing to a significant 24.8% increase in daily energy yield compared to a non-MPPT system. IoT performance metrics showed a data transmission success rate of 97.08%, average latency of 2.82 seconds, and system uptime of 99.12%. This integrated MPPT-IoT system offers a practical and affordable technological solution, contributing empirical baseline data for tropical conditions and supporting Indonesia's national energy transition and net-zero emission targets through improved data-driven decision-making.
Co-Authors Beni Satria Firza Ardana Hamdani Hamdani Irham Purba Jekli Boy Nainggolan