Mohammed Obaid Mustafa

In this paper, modeling and control of a six-phase interleaved buck converter (IBC) are presented for fast charging of ultra-fast electric vehicle (EV) batteries. The primary difficulty faced is the need to operate at high power charging rates with stable and efficient performance, while minimizing output ripple and current sharing errors. To solve these problems, the cascaded multi-loop control method is proposed using a proportional-integral (PI) controller. By implementing a reduced number of control loops (four coordinated loops) from conventional configurations, the control structure is simplified, while stable operation and effective current sharing are maintained. The proposed system is implemented in a constant current/constant voltage (CC-CV) charging method, which guarantees safe and efficient battery charging. The proposed system is validated by using MATLAB/Simulink simulation under steady-state and transient conditions. The results show that with a very small amount of ripple, a battery voltage of about 820 V and a charging current of 330 A are obtained. The dynamic response has a settling time of about 100 ms and an overshoot less than 5%, which is suitable for disturbances. Furthermore, in the SIMO configuration, it is noted that a disturbance or fault on the output of one of the cascaded control loops has minimal effect on the output of the other control loop, which illustrates good decoupling between cascaded control loops. This is a sign of better robustness and reliability for multi-output EV charging applications. Moreover, the stability margins of the proposed control method are verified by frequency-domain analysis.

Autonomous operation of Unmanned Aerial Vehicles (UAVs) in complex low-altitude environments requires navigation strategies that ensure safety, adaptability, and reliability under dense obstacle conditions. This paper proposes an end-to-end intelligent navigation framework that integrates a real-time object detection network (YOLO) with an adaptive control policy based on Proximal Policy Optimization (PPO) to achieve robust, collision-free obstacle avoidance. The approach is validated in a simulated low-altitude 10×10×10 m ³ three-dimensional workspace containing 25 static obstacles arranged in a deterministicstochastic distribution, representing highly cluttered operational scenarios. Perception inputs derived from YOLO detections are transformed into a structured observation vector and used to guide continuous control actions learned via a shaped reward function that accounts for goal convergence, altitude stability, motion efficiency, and safe obstacle standoff distances. To better reflect realistic sensing conditions, perceptual uncertainty is modeled by injecting Gaussian noise and false-positive detections into the perception pipeline during training. Experimental results over 500,000 training timesteps demonstrate stable policy convergence, achieving a 90% success rate with zero collisions across multiple test episodes and maintaining consistent performance under varying noise levels. The resulting trajectories show improved safety-aware maneuvering and reduced path length, highlighting the frameworks potential for low-altitude engineering applications requiring dependable autonomous navigation under imperfect perception.

Integrating Programmable Logic Controllers (PLCs) with Node-RED and IoT/IIoT has emerged as a transformative technique for developing intelligent industrial systems as industrial automation improves. This narrative review brings together more than 70 research sources on industrial PLC, Node-RED, IoT, and IIoT applications. Case studies, experimental implementations, and industry reports are used to discover trends, challenges, and opportunities. The review focuses on three main topics: PLCs in modern industrial systems and their evolution and integration with IoT/IIoT; Node-RED as a middleware for industrial automation and its ability to connect PLCs to cloud and edge computing; and IIoT and smart manufacturing and how it improves data acquisition, processing, and analytics for optimized operations. This study review elucidates the technical interplay of PLCs, Node-RED, and the Industrial Internet of Things (IIoT) in smart industrial systems. It delineates essential integration trends, emphasizes current problems, and specifies prospective trajectories for the advancement of automation, connection, and system intelligence.

The wide spectrum of electromagnetism that explains current and voltage at specific time and location in a power system is referred to as power quality. Alternative energies are becoming more popular due to concerns about power quality, safety, and the environment, as well as commercial incentives. Moreover, photovoltaic (PV) energy is one of the most well-known renewable resources since it is free to gather, unlimited, and considerably cleaner. Active power filter (APF) is an effective means to dynamically suppress harmonics and solve power quality problems caused by the DC side voltage fluctuation. Therefore, this paper describes a substantial advancement in the harmonic suppression compensation algorithm, as well as the cascaded active power filter. Also, this paper focuses on compensating the error of photovoltaic grid-connected generation based on optimized H-bridge cascaded APF. The details of the working principle and topological structure of the APF used as the compensation device are analyzed. The H-bridge cascaded APF is optimized using the segmented variable step-length conductance increment (SVSLCI) algorithm. The overall cascaded APF control strategy is designed and simulated using MatLab/Simulink environment. By the simulation results comparing the existing traction network power quality control measures, before and after compensation, the effectiveness of the proposed control strategy is verified. The proposed controller strengthens the compensation of specific odd harmonics to improve the system work models and criteria to improve power quality. Moreover, the proposed algorithm showed positive significance for optimizing the quality of photovoltaic grid-connected power, reducing the current harmonic, and improving the equipment utilization of photovoltaic inverters.

The growing demand for dependable manufacturing techniques has sped up research into condition monitoring and fault diagnosis of critical motor parts. On the other hand, in modern industry, machine maintenance is becoming increasingly necessary. An insufficient maintenance strategy can result in unnecessarily high downtime or accidental machine failure, resulting in significant financial and even human life losses. Downtime and repair costs rise as a result of failure. Furthermore, developing an online condition monitoring method may be one solution to come up for the problem. Early detection of faults is very vital since they grow quickly and can cause further problems to the motor. This paper proposes an effective strategy for the classification of broken rotor bars (BRBs) for induction motors (IMs) that uses a new approach based on Artificial Neural Network (ANN) and stator current envelope. The stator current envelope is extracted using the cubic spline interpolation process. This is based on the idea that the amplitude-modulated motor current signal can be revealed using the motor current envelope. The stator current envelope is used to select seven features, which will be used as input for the neural network. Five IM conditions were experimentally used in this study, including a part of BRB, 1 BRB, 2 BRBs and 3 BRBs. The new feature extraction and selection approach achieves a higher level of accuracy than the conventional method for motor fault classification, according to the experimental results. Indeed, the results are impressive, and it is capable of detecting the exact number of broken rotor bars under full load conditions

A significant problem in the control field is the adjustment of PID controller parameters. Because of its high nonlinearity property, control of the DC motor system is difficult and mathematically repetitive. The particle swarm optimization PSO solution is a great optimization technique and a promising approach to address the problem of optimum PID controller results. In this paper, a modified particle swarm optimization PSO method with four inertia weight functions is suggested to find the global optimum parameters of the PID controller for speed and position control of the DC motor. Benchmark studies of inertia weight functions are described. Two scenarios have been suggested in order to modify PSO including the first scenario called M1-PSO and the second scenario called M2-PSO, as well as classical PSO algorithms. For the first scenario, the modification of the PSO was done based on changing the four inertia weight functions, social and personal acceleration coefficient, while in the second scenario, the four inertia weight functions have been changed but the social and personal acceleration coefficient stayed constant during the algorithm implementation. The comparison between the presented scenarios and traditional PID was carried out and satisfied simulation results have shown that the first scenario has rapid search speeds, and very effective and fast implementation compared to the second scenario and classical PSO and even improved PSO technique. Moreover, the proposed approach has a fast searching speed compared to classical PSO. However, it has been found that the classical PSO algorithm has a premature, inaccurate and local convergence process when solving complex optimization issues. The presented algorithm is proposed to increase the search speed of the original PSO.