This study investigates the use of chaotic speed control, based on the Sprott-A chaotic system, for improving the performance and stability of DC motor-driven robotic end-effector mixers. The chaotic differential equations were implemented and numerically solved in MATLAB/Simulink using the fourth-order Runge–Kutta method, and the resulting time series were analyzed. Among the variables generated, the X_t signal was selected for pulse-width modulation (PWM) due to its smooth dynamic characteristics. This signal was scaled to match the 0–100% duty cycle range and applied to the motor driver as a control input. The chaotic control system was realized both through analog circuit simulation in OrCAD and experimentally using an STM32F407 microcontroller. Time series, phase portraits, and oscilloscope outputs confirmed the consistency between simulation and hardware implementations. Compared to chaotic Y_t and Z_t signals, the chaotic X_t based PWM control reduced motor vibrations and provided more stable speed regulation. These results demonstrate the feasibility and effectiveness of chaotic dynamics for real-time motor control in robotic mixing applications, offering a robust alternative to traditional deterministic methods.

Electric motors are actively used in various industries. Acoustic noise is crucial in electric motors, and specific standards are depending on their application areas. Despite better performance, including acoustic noise, than brushed motors, brushless motors still generate acoustic noise due to their mechanical, electrical, and electronic components. This study investigated the impact of varying the switching frequency through the driver on the acoustic noise of an external rotor brushless DC motor. Tests were conducted on a surface-mounted magnet brushless motor with different switching frequencies, and detailed information about the sections of the control board governing the brushless motor was provided. The study includes measurements of motor speed, current, switching frequency, phase signal, and acoustic noise measured at two different locations in decibels. It was observed that the acoustic noise increased at specific switching frequencies. Furthermore, the variation in switching frequency also affected the heating due to losses in the switching elements. Increasing the switching frequency in the 12-28 kHz range reduced motor speed and the measured acoustic noise, while temperature increases were observed in various frequency ranges.

The rapid development of artificial intelligence (AI) and machine learning (ML) has significantly transformed the capabilities of unmanned aerial vehicle (UAV) detection systems. This study presents an AI- and ML-based approach for drone detection using the YOLOv11 algorithm, a state-of-the-art deep learning model designed for real-time object recognition. A custom dataset, consisting of 1450 drone images collected under diverse environmental and lighting conditions, was used to train and evaluate the model. The training process employed the YOLOv11 variants (n, s, m, l, x) on the PyTorch framework, with performance metrics including Precision, Recall, F1-Score, and mAP50–95. The results demonstrated exceptional detection accuracy, achieving up to 99% precision and 98% recall, with an overall mAP50 of 0.99 and mAP50–95 of 0.70. Loss function analyses indicated consistent convergence, while confusion matrix and confidence curve evaluations confirmed the model’s robustness in differentiating drone objects from background scenes. This research highlights the effectiveness of integrating deep learning architectures within AI-driven vision systems for UAV detection. The findings confirm that YOLOv11 offers a highly reliable and efficient solution for real-time drone identification, with strong potential for implementation in security, surveillance, and autonomous navigation applications.

This study presents a stator-focused electromagnetic optimization of a 350 W, 27-slot, 30-pole outer-rotor brushless direct current (BLDC) motor developed for electric scooter applications. Unlike conventional redesign approaches that modify rotor topology or overall motor dimensions, the proposed methodology preserves the rotor structure and external geometry of a commercially validated reference motor and improves performance primarily through targeted stator geometric refinement, with minor adjustments in the winding configuration. A two-stage optimization strategy combining parametric analysis and genetic algorithm (GA)-based multi-objective optimization is implemented to minimize cogging torque and torque ripple while maximizing efficiency. Finite element analyses (FEA) were conducted to evaluate back electromotive force (back-EMF) characteristics, magnetic flux density distribution, torque behavior, and current density. Experimental validation confirms a 54.86% reduction in cogging torque (from 257 mNm to 116 mNm), a 19.6% decrease in torque ripple, a 6.17% reduction in maximum current density, and a 2–3% improvement in efficiency within the nominal load range (5.2–6.45 Nm), reaching 85.69% efficiency at 350 W output power. The results demonstrate that systematic stator geometry optimization, supported by minor winding modifications, can significantly enhance efficiency, torque smoothness, and thermal margin without increasing motor size, rated power, or manufacturing complexity. This work provides a practical and manufacturable design pathway for high-performance outer rotor BLDC motors in light electric vehicle (LEV) propulsion systems.

This study investigates the influence of rotor design on the performance of interior permanent magnet brushless DC (IPM-BLDC) motors used in axial fan applications, which span from industrial cooling systems to automotive technologies. Two alternative rotor topologies with a 12/8 slot–pole configuration were analyzed using finite element analysis (FEA). The evaluation focused on key performance metrics, including torque ripple, cogging torque, and efficiency. The optimized flux-barrier rotor demonstrated a 55.36% reduction in cogging torque, a 1.23% improvement in efficiency, and a 2.23% decrease in torque ripple relative to the baseline design. Based on these results, the superior rotor geometry was prototyped, and the numerical findings were experimentally validated through performance testing. The outcomes confirm that rotor flux-barrier optimization enhances the overall efficiency and operational stability of IPM-BLDC motors in axial fan applications.

Generating chaotic flows on digital hardware is a broader research problem than simply moving a differential equation solver onto an embedded processor. When a state variable is observed on a screen, the visible trace is shaped jointly by the chosen numerical method, finite precision, the sampling rate, analog conversion and, if present, feedback. The aim of this review is to evaluate, within a common critical framework, experimental studies on implementing continuous-time chaotic models on digital signal processors (DSPs) and related embedded hardware, on observing them with an oscilloscope through a digital-to-analog converter (DAC), and on changing the control gain during operation. The initial literature pool was complemented by a documented search of the Directory of Open Access Journals (135 records, 118 screened, 79 eligible), and the implementation parameters of representative studies were extracted into a common table. Integer-order and fractional-order flows, microcontroller and field-programmable gate array (FPGA) examples, and different chaos control objectives are treated separately. The reviewed studies show that numerical attractors can be produced as analog signals and that, in some set-ups, stabilization or synchronization is achieved in hardware. However, the integration step, the physical update period and the number format are seldom reported together, validation is mostly visual, and only a few studies close the loop around a physical analog chaotic circuit. In addition, an attractor on the oscilloscope does not, on its own, prove that the state update is completed on time at every sample, or that a feedback gain changed by an operator remains stabilizing during the transition; this question should be framed with the existing theory of switched, slowly varying and gain-scheduled systems. Apparent methodological differences between studies cannot be treated as direct performance contradictions when the same model, integration step and validation criterion are not used. The main conclusion of the review is that hardware studies of chaos provide different levels of evidence about numerical dynamics, timing, analog observation and control behavior, and that these can only be compared once they are clearly separated.