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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Abdellah, Alhachemi Moulay
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Control & Systems Engineering Electrical & Electronics Engineering

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Experimental study of PID for attitude control of a quadcopter using an ESP32 Moussaoui, Ahmed Khalil; Habbab, Mohamed; Abdeldjebar, Hazzab; Slimane, Hireche; Chandra, Ambrish; Gouabi, Hicham; Abdellah, Alhachemi Moulay
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1426-1434

Abstract

Aerial robotics encompasses intricate kinematics and dynamics that govern the flight of quad-rotor systems. Among the various methods employed for flight control using microcontrollers like the ESP32 developed by ESPRESSIF; the proportional integral derivative (PID) controller stands out as a widely adopted approach. The ESP32 microcontroller offers a superior interface, delivering enhanced performance and response time, particularly in dynamic environments. This article delves into the implementation and viability of the ESP32 platform for communication with MATLAB/Simulink, as well as real-time data acquisition to control the attitude of quadcopter withe chassis F450. The PID controller was designed to specifically work with the ESP32 platform and rigorously tested on an actual quadcopter during flight operations. lastly, a comprehensive analysis of the data gained and empirical results from the physical model demonstrates that the proposed framework is effective.
Fault diagnosis decentralized of manufacturing systems using Boolean models Slimane, Hireche; Habbab, Mohamed; Hazzab, Abdeldjebar; Moussaoui, Ahmed Khalil; Chandra, Ambrish; Gouabi, Hicham; Abdellah, Alhachemi Moulay
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2700-2708

Abstract

This paper introduces an approach decentralized to fault detection and isolation (FDI) in manufacturing systems using a Boolean discrete event model. The method incorporates diverse information sources to create distinct models for plant systems and control. The objective is to enhance the understanding of process operations by employing various representation tools tailored to each information source. It is to reduce the number of explosion problems combinatorial and detect faults in the shortest possible time. This comprehensive representation facilitates the fulfillment of three crucial diagnosis functions: detection, localization, and identification. The approach involves Boolean modeling of each process actuator along with its corresponding sensors, a temporal model based on fuzzy expectations of event occurrences, and a set of if...then rules. The goal of this decentralized approach minimize both the complexity and the manual construction effort required for the model. The paper demonstrates the effectiveness of this approach through an illustrative example involving manufacturing systems.
Application of machine learning for production optimization and predictive maintenance in an iron processing plant Lahcen, Lakhdari; Habbab, Mohamed; Abdellah, Alhachemi Moulay
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp765-776

Abstract

The modern metallurgical industry requires advanced solutions for process optimization, cost reduction, and predictive maintenance. This paper proposes a unified simulation-based framework using machine learning (ML) to jointly address production optimization and maintenance prediction in a virtual iron processing environment. Several ML models, including random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector machine (SVM), and k-nearest neighbors (k-NN), were evaluated on synthetic datasets representing production, maintenance, and transport processes. A reproducible methodology was adopted, including preprocessing, time-aware data splitting, and cross-validation to prevent information leakage. Model performance was assessed using F1-score, area under the receiver operating characteristic curve (AUC), and regression metrics. Tree-based models achieved near-perfect classification performance (AUC ≈ 1, precision and recall > 0.99), while light gradient boosting machine (LightGBM) and CatBoost provided the best regression accuracy. Feature importance analysis using SHapley Additive exPlanations (SHAP) identified vibration and temperature as key maintenance indicators. Although based on simulation, the framework is designed for integration with supervisory control and data acquisition (SCADA) and the Industrial Internet of Things (IIoT), supporting real-time industrial deployment and alignment with operational key performance indicators.
Co-Authors Abdeldjebar, Hazzab Chandra, Ambrish Gouabi, Hicham Habbab, Mohamed Hazzab, Abdeldjebar Lahcen, Lakhdari Moussaoui, Ahmed Khalil Slimane, Hireche