Derradji Bakria

Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency, compact structure, and excellent dynamic performance. However, achieving accurate speed control with high robustness under load disturbances and parameter uncertainties remains a significant challenge. Conventional proportional–integral (PI) controllers often suffer from overshoot, slow dynamic response, and sensitivity to nonlinear operating conditions. To address these limitations, this paper proposes an intelligent control strategy that combines third-order sliding mode control (TOSMC) with the Golden Jackal Optimization (GJO) algorithm for optimal PMSM speed regulation. The proposed TOSMC-GJO approach aims to enhance the operational performance, robustness, and reliability of PMSM drives. The control structure consists of an optimized outer-loop speed controller and an inner-loop predictive current controller to improve current quality and eliminate the need for conventional PI tuning. The controller parameters are optimized using a fitness function designed to minimize tracking error, overshoot, settling time, torque ripples, and total harmonic distortion (THD). Simulation results under variable speed and load torque conditions demonstrate that the proposed TOSMC-GJO controller achieves superior performance compared with PI control and TOSMC optimized using Grey Wolf Optimization (GWO). The proposed strategy eliminates speed overshoot and reduces the response time to 0.0052 s, compared with 0.0056 s for TOSMC-GWO and 0.011 s for PI control. In addition, the THD of stator currents is reduced to 6.12%, improving current quality and reducing harmonic distortion. The proposed controller also provides smoother torque response, better disturbance rejection capability, and improved waveform symmetry. These results confirm that integrating high-order nonlinear control with metaheuristic optimization significantly improves the dynamic performance, operational reliability, and robustness of PMSM drive systems under demanding operating conditions.

This paper presents a new method based on Artificial Intelligence (AI), for the optimal control of a bidirectional converter. The goal is to improve the DC bus voltage efficiency of a grid-independent hybrid system. The proposed design consists of a Photovoltaic System (PVS), a Wind Energy Conversion System (WECS), a Battery Storage System (BSS), and a variety of electronic power devices to extract the maximum energy from the generation sources. A bidirectional converter is featured in the use of an Adaptive Network-based Fuzzy Inference System (ANFIS) based controller, to improve the energy quality of the DC bus. The proposed approach provided good results compared to the Proportional Integral (PI) controller and the Fuzzy Logic controller (FLC). The use of this approach reduced the value of oscillations, improved the response time of the system, and contributed to the elimination of the problem of overshoot. This experiment was conducted using Matlab and Simulink.

Integration of nonlinear loads in modern power systems has led to many issues arising mainly due to the generation of harmonic currents and the presence of reactive power, both having adverse effects on power quality and grid stability. Harmonic currents cause increased losses, overheating of equipment, and voltage distortions, while reactive power imbalances result in inefficiencies in power delivery and compromised system performance. To overcome these problems, a Shunt Active Power FIlter design and an optimal control strategy for harmonic mitigation and reactive power compensation are proposed in this paper. The design incorporates an optimized anti-windup PI controller for DC-link voltage regulation and an optimized output filter to enhance the quality of the injected current. This design is formulated as an optimization problem and solved using the Golden Jackal Optimizer. MATLAB/Simulink simulations validate the proposed method under different operating conditions, covering dynamic change of loads and unbalanced grid conditions. The result shows a remarkable reduction in Total Harmonic Distortion (THD) of grid current, and reactive power compensation meanwhile maintaining the stability of the grid.

To quickly identify COVID-19 and stop its spread, Computer Tomography (CT) imaging of the chest is a reliable diagnostic method. In this work we implement a deep neural network based on Convolutional Neural Network (CNN), to effectively distinguish between healthy CT images and COVID-19 CT images. The final architecture allows for 91.6% accuracy, which is a 2% improvement over the first one. The result obtained can be very useful in medical diagnostics, particularly in the Covid-19 case. The implemented CNN model takes 2-3 seconds to run on a medium-end laptop without GPU acceleration

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