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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Public Health Science (IJPHS)
Mohamed Cherkaoui, Mohamed
Mohammadia Engineering School
Control & Systems Engineering Electrical & Electronics Engineering Health Professions

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Nonlinear robust control applied to the six-phase induction machine Ahmed, Yarba; Oumar, Aichetoune; Cherkaoui, Mohamed
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.pp2089-2096

Abstract

This paper proposes a nonlinear robust control for the six-phase induction machine (SPIM). It is based on the super twisting sliding mode (STSM) to ensure good decoupling and robust performance. This paper proposes also a comparative study between STSM and active disturbance rejection control (ADRC). We apply the rotor field-oriented control to ensure the decoupling between the magnitudes of the SPIM. Then we study the STSM control to regulate the rotor speed, the stator currents, and the rotor flux of the machine. The STSM ensures the same performance as the classical sliding mode control with the advantage of reducing the phenomenon of chattering. At the same time, the STSM command is compared to the ADRC command. The ADRC is one of the robust commands that makes it possible to estimate and eliminate internal or external disturbances. To test the robustness of the STSM and the ADRC, we implanted them in Simulink/MATLAB and the simulation results show the effectiveness of the STSM control compared to the ADRC control.
Artificial intelligence model for the prediction of cannabis addiction Elhachimi, Abdelilah; Eddabbah, Mohamed; Benksim, Abdelhafid; Ibannid, Hamid; Cherkaoui, Mohamed
International Journal of Public Health Science (IJPHS) Vol 14, No 2: June 2025
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v14i2.25786

Abstract

A novel approach for predicting cannabis addiction has been introduced by integrating combined machine learning (ML) algorithms, specifically K-means clustering and linear regression (LR). The study, conducted in Marrakech, Morocco, at a center linked to the National Association for drug-risk reduction (DRR), involved 146 participants. Among those with prior cannabis use, one subgroup included passive users, while another exhibited cannabis dependence. The research utilized features derived from patient data, emphasizing psycho-cognitive state, addiction status, and socio-demographic factors. The goal was to evaluate the effectiveness of the combined ML algorithms (K-means + LR) in distinguishing between addicted and non-addicted individuals using real-world data from a primary care addiction center. The findings indicate that the proposed method delivers balanced results, achieving an overall accuracy of 70%, a sensitivity of 65%, and a specificity of 86%. These results are particularly noteworthy when compared to other ML studies in addiction research. The combined algorithm demonstrates promising potential with competitive accuracy and high specificity. Further efforts to improve sensitivity and validate the model in diverse settings will be essential for advancing predictive modeling in this field. Our findings contribute to existing research by developing simple and effective tools for early detection of cannabis addiction, potentially aiding in the creation of preventive and therapeutic strategies to reduce its prevalence.
Co-Authors Ahmed, Yarba Benksim, Abdelhafid Eddabbah, Mohamed Elhachimi, Abdelilah Ibannid, Hamid Oumar, Aichetoune