Search Results (1,493)

With the increasing demand for high speed and high-precision motion control in CNC machine tools, permanent magnet linear synchronous motors (PMLSMs) have been widely adopted in feed drive systems due to their excellent dynamic performance and positioning accuracy. However, existing model predictive current control (MPCC) variants still face challenges regarding high computational overhead and strong dependency on accurate motor parameters, which limit their industrial applicability. To address these issues, this paper proposes an adaptive recursive MPCC for PMLSM drives based on the Cartesian distance minimization principle. An adaptive recursive prediction scheme that is inspired by the feedback structure of recurrent architectures is first introduced. By cyclically utilizing the previously sampled current to predict the next period’s state, the strategy effectively decouples the control law from inductance variations. The dependence on resistance is further mitigated by analyzing the correlation between the ideal current vector and voltage vector deviations. Second, the selection of the optimal voltage vector is transformed into a geometric problem: minimizing the Cartesian distance between the reference voltage and 19 candidate deviations within a proposed virtual voltage vector hexagon. To minimize the computational burden, the vector space is partitioned into eight regions, allowing the optimal candidate to be selected from only two pre-derived deviations. The experimental results demonstrate that the proposed method significantly outperforms existing MPCC benchmarks. Specifically, the execution time is reduced by 63.6%. Under severe parameter mismatch, the current THD is reduced from 14.82% to 6.35%, and the thrust ripple is improved from 12.06 N to 5.25 N, validating its superior robustness and efficiency. Full article

This study presents a systematic topological progression and multi-objective optimization of a Permanent Magnet-assisted Non-overlapping Winding Line-Start Synchronous Reluctance Motor (PMaNWLS-SynRM) for industrial applications. To explicitly highlight the core contribution, the research establishes a rigorous comparative framework evaluating the transition from a conventional 4-pole/36-slot distributed winding (DW) to a 2 × 12-slot non-overlapping winding (NW) architecture. Baseline results demonstrate that the NW configuration shortens end-turns, successfully reducing total electromagnetic losses from 417 W to 349 W and improving steady-state efficiency from 93.7% to 95.1%. To overcome the inherent starting limitations of pure synchronous reluctance machines, an aluminum squirrel-cage is integrated to enable robust direct-on-line (DOL) synchronization, while NdFeB permanent magnets are embedded within the rotor flux barriers to mitigate asynchronous spatial harmonics and elevate torque density. Finite element analysis (FEA) confirms this magnetic assistance raises the average synchronous torque to 65.8 Nm while suppressing absolute torque ripple to 1.37 Nm. Finally, an evolutionary genetic algorithm is deployed across 440 iterative configurations to resolve geometric multi-physics conflicts. The finalized optimized design achieves a 13.2 kW output power at 1800 rpm, maximizing average torque to 70.12 Nm and strictly dampening absolute torque ripple to an industry-acceptable 1.04 Nm. Operating with an aggregated total loss of 1382 W, the optimized PMaNWLS-SynRM yields a 90.5% operational efficiency, definitively validating its suitability as an ultra-premium IE4/IE5 alternative to conventional induction motors. Full article

This study investigates the primary electromagnetic sources of acoustic noise in single-phase induction motors and proposes design-oriented strategies for noise reduction. A 370 W, four-pole, 80-frame single-phase induction motor was designed, analyzed, and experimentally validated. Finite Element Method (FEM) simulations were conducted using Ansys Maxwell 2D to examine the effects of magnetic field distortion, magnetic saturation, and rotor eccentricity on torque ripple and inductance variation. The results demonstrate that these factors significantly increase electromagnetic force harmonics acting on the stator teeth and frame, leading to vibration and acoustic noise generation. In addition, inductance fluctuations caused by interphase magnetic coupling and air-gap harmonics were found to increase current harmonic content and potentially excite structural resonances. The influence of capacitor selection and winding configuration on magnetic saturation, phase displacement, and torque ripple was systematically evaluated. Prototype motors were manufactured and acoustic noise measurements were performed to experimentally validate the simulation results. Unlike previous studies that often investigate these parameters separately, this work presents a coupled analysis that explicitly links capacitor selection, winding configuration, and rotor eccentricity to inductance variation, torque ripple, and acoustic noise generation. The findings provide practical design guidelines for the development of low-noise single-phase induction motors and contribute to reducing electromagnetic vibration and acoustic emissions in electric machine design. Full article

Synchronous Reluctance Motors (SynRM) have attracted much attention due to their advantages of simple structure and low cost. However, due to factors such as magnetic saturation and temperature changes, the parameters of SynRM exhibit nonlinear characteristics. Existing Maximum Torque per Ampere (MTPA) control strategies often do not fully consider the impact of nonlinear changes in motor parameters, making it difficult to achieve accurate MTPA control and resulting in reduced motor efficiency. This article analyzes the control errors caused by the nonlinear changes in inductance of SynRM and proposes an error compensation strategy based on virtual DC signal injection MTPA control. The error expression is reconstructed to achieve error compensation and improve the accuracy of MTPA control. The effectiveness of the proposed control strategy is verified by building a simulation model and a motor experimental platform. The experimental results show that the control strategy proposed in this paper can achieve a maximum current optimization rate of 5.01% while ensuring fast system responsiveness. Full article

Medium voltage induction motors (MVIM) are a key component of numerous industries, such as water treatment plants, sewage discharge stations, and chilled water systems. The starting process for these MV motors is critical as it is associated with a major impact on both motor lifetime and power grid quality. In this article, a proposed modified and comprehensive starting scheme of MV three-phase induction motors driving pumps for water stations is introduced. Firstly, the starting performance and its impact on power grid quality will be discussed when all motors are normally started with direct on line connection (DOL), which is already the normal established status. A modified starting scheme based on an optimized coordination of motor starting methods in addition to variable voltage variable frequency drive (VVVFD) drive and control implementation will be discussed. A transition between the starting of variant MV induction motors as well as the starting event coordination principle will be discussed to improve the power quality relative to the obligatory time shift required for the operation. The coordination is based on an algorithm implementation which is achieved using different optimization concepts based on artificial intelligence techniques, properly conducting the transition time in addition to the power delivered by the inverter unit rather than determining the number of DOL and VVVF-implemented motors. A comparison between using the optimized VVVFD soft-starting and the proposed modified scheme is performed, focusing on the power quality improvement rather than optimizing the cost function. The modified scheme is simulated using ETAP power station for brief analysis and study of load flow rather than the complete inspection and power quality assessment. Full article

Lack of lubrication in rolling-element bearings is a leading root cause of premature failure in induction motors and other electromechanical drives. This study proposes novel vibration-based technologies for diagnosing a lack of lubrication in bearings of induction motors. Two technologies are proposed: the Filter-less spectral kurtosis (FLSK), which quantifies impulsive energy generated by a lack of bearing lubrication, and the fundamental rotational harmonic technology, which captures an increase in the fundamental rotational harmonic magnitude, also induced by a lack of bearing lubrication. Comprehensive experimental trials are performed on a Siemens induction gearmotor, used in airport baggage handling conveyor systems. The experimental results show that both technologies exhibit effective diagnostics. Full article

To address the insufficient accuracy of conventional permanent magnet synchronous motor (PMSM) models caused by neglecting magnetic saturation nonlinearity and periodic parameter disturbances, a nonlinear motor system model integrating a Physics-Informed Neural Network (PINN) is developed. By exploiting the differential relationships among incremental inductance, flux linkage, and magnetic energy, the voltage and torque equations considering rotor position variation are derived, and analytical expressions for the derivatives of incremental inductances are obtained. To reduce the computational burden of PINN in system-level simulations, linear and nonlinear approximation strategies based on incremental inductances and their derivatives are proposed, which significantly reduce the frequency of PINN calls while maintaining model accuracy. CPU/GPU collaborative computation and cross-frequency-domain scheduling are further implemented to improve simulation efficiency. Considering the influence of the test bench mechanical dynamics, an electromechanical–magnetic coupled simulation model is established. The accuracy of the proposed nonlinear motor model is validated through two-phase short-circuit tests as well as simulations and test bench experiments under sinusoidal and non-sinusoidal excitations. The results demonstrate that the proposed model accurately captures the nonlinear electromagnetic characteristics of PMSMs while significantly improving system simulation efficiency. Full article

Developing electric motors with higher efficiencies for energy savings and environmental protection is crucial. The efficiency of grid-connected induction motors can be measured using various approaches; the preferred method is the indirect approach, which evaluates the separate losses from the additional losses due to residual losses. This approach follows the traditional approach to efficiency determination, introducing experimental procedures to assess additional losses by measuring the torque delivered by the motors. As noted in previous articles, the procedure is complex and requires numerous direct measurements. One area of interest is the determination of measurement uncertainty. This work aims to quantify the sensitivity of the combined uncertainties of losses and efficiency to variations in directly measured input variables: power frequency, rotational speed, torque, power, current, voltage, resistance, coolant temperature, and cold frame temperature. The results presented here help select measurement instrumentation, depending on whether the tests are aimed solely at determining efficiency or whether it is necessary to analyze the trend of the various types of loss, as occurs in optimization and experimental verification processes with high-performance materials, based on a comprehensive analysis of all standard and combined uncertainties, and with experimental data to assign a realistic value to the uncertainties themselves. Full article

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects both motor and non-motor functioning, leading to increasing dependency and long-term psychosocial consequences. As the disease progresses, partners often assume caregiving roles, resulting in shifts in responsibilities, communication patterns, and emotional dynamics within marital relationships. The aim of this study was therefore to explore the impact of Parkinson’s disease on marital relationships. Methods: A qualitative interview study with a retrospective design was conducted. Six couples were recruited through a movement disorders clinic and a lay organization in Sweden. Semi-structured, face-to-face interviews were conducted separately with each partner. Interviews were transcribed verbatim and analyzed using conventional content analysis with an inductive design. Results: Four main themes emerged: managing the disease together in partnership, nurturing the relationship, facing marital hardship, and planning an uncertain future. Couples who adopted a positive and pragmatic outlook, shared responsibilities, and maintained open communication seemed to be better able to manage the disease. Engaging in joint activities and reciprocal communication strengthened emotional closeness. In contrast, changes in roles, emotional distress, loss of intimacy, and communication avoidance challenged relationships. Thinking about the future evoked feelings of ambivalence, as couples balanced uncertainty with a need for security. Conclusions: Parkinson’s disease affects marital relationships, reshaping roles, emotional bonds, and future perspectives. The ability of nurses to address both partners’ needs and promote communication and shared coping strategies is essential to strengthening couples’ well-being. Full article

Pulse Width Modulated (PWM) voltage source inverters are widely used to power induction motors in industrial applications. However, they generate common-mode voltage (CMV), which induces high shaft voltages and bearing currents, leading to premature motor failures. This paper proposes a novel active cancellation method to compensate for the CMV in high-voltage induction motor drives. The method utilizes Y-configured resistors for CMV detection and a push–pull amplifier with MOSFETs to generate reproduced CMV (RCMV). The RCMV is applied to the motor frame via an isolation transformer, effectively reducing the CMV-induced common-mode current (CMC). The proposed method achieves a significant reduction in the CMC, from 1.5 A to 4 mA peak-to-peak in a simulation and from 2.7 A to 57 mA peak in experiments with a 1.1 kW, 415 V/60 Hz motor. This cost-effective approach enhances motor drive reliability and mitigates electromagnetic interference (EMI), making it suitable for high-voltage applications. Full article

Loss of myelinating oligodendrocytes and myelin impairs motor and cognitive functions. Transplantation of autologous oligodendrocyte precursors (OPCs) holds promise for treatment of such diseases, but a protocol to derive human OPCs from a safe, ethical and accessible cell source with the rapidity required to catch the therapeutic window remains to be found. Although we previously generated myelinating glia from rat bone marrow stromal cells (BMSCs), it remains unknown if clinically sourced human BMSCs (hBMSCs) share the same potential. Moreover, whether the multipotency of BMSCs results from diverse progenitors preexisting in the bone marrow or from a single multipotent progenitor population remains unaddressed. Single-cell RNA sequencing data revealed a CD90^hiEGFR⁺PDGFRA⁺ pre-OPC-like subpopulation within hBMSCs. With a small-molecule-based (virus-free and supporting-cell-free) two-step induction protocol designed to expand this pre-OPC population, we generated functional OPCs with high purity in eight days. These derived OPCs showed phenotypic transcriptomes and immunoprofiles. They were also capable of myelinating naked axons when transplanted into myelin-deficient shiverer mice. Results highlight how targeted enrichment and maturation of specific progenitor subpopulations within hBMSCs allows rapid induction of desired cell types. These results place hBMSCs as a robust source of OPCs, unlocking the possibility for cell transplantation therapy for myelin deficiency in the central nervous system. Full article

Hyperekplexia is a neurologic disorder of marked perinatal significance. Affected neonates display generalized hypertonia and exaggerated startle reflex in response to innocuous stimuli, potentially leading to life-threatening apneic episodes. Although symptom severity typically diminishes during the first year of life, affected individuals often continue to exhibit disabling motor dysfunction and frequent unprotected falls throughout adulthood. Currently, no targeted therapeutic interventions are available. The pathophysiology involves partial or complete disruption of inhibitory glycinergic neurotransmission. Mutations in the gene encoding the neuronal glycine transporter GlyT2 (SLC6A5) represent the second-most frequent genetic etiology of human hyperekplexia. To investigate the mechanistic basis for the heightened severity of symptoms during the perinatal period, we examined the role of GlyT2 in neuronal differentiation using the PC12 cell model. Pharmacological induction of differentiation demonstrated that clones stably expressing GFP-GlyT2 exhibit increased expression of neuronal differentiation markers and enhanced neurite outgrowth—both in number and length—relative to parental PC12 cells. These clones also displayed elevated cytosolic calcium levels, which were attenuated by calmodulin overexpression, subsequently downregulating differentiation marker expression. We hereby proved that GlyT2 is clearly implicated in growth cone progression and differentiation of PC12 cells into neurons by increasing internal calcium and binding to growth cone proteins. Finally, our results were validated in primary neurons. Full article

Fault-induced delayed voltage recovery (FIDVR) poses a serious threat to modern power grid operation, where stalled induction motors following a fault can sustain dangerously low bus voltages and potentially trigger cascading failures. While deep reinforcement learning (DRL) has shown promise for emergency load shedding control, existing centralized DRL approaches require extensive communication infrastructure and large neural network models that are computationally prohibitive to train at scale. Fully decentralized approaches, on the other hand, lack inter-agent information sharing and coordination, often resulting in inadequate voltage recovery across area boundaries. To address these limitations, we propose a Cloud–Edge Collaborative DRL framework that combines lightweight, area-specific edge agents for local load shedding control with a supervisory cloud agent that coordinates their actions globally, achieving scalable training and system-wide voltage recovery simultaneously. Training is further accelerated through a modified Guided Surrogate-gradient-based Evolutionary Random Search (GSERS) algorithm. Validation on the IEEE 300-bus system demonstrates that the proposed framework reduces training time by approximately 90% compared to the fully centralized approach, while achieving comparable voltage recovery performance to the centralized method and approximately 80% better reward performance than the fully decentralized approach, confirming the critical benefit of the cloud-level coordination mechanism. Full article

This paper presents an efficient stand-alone photovoltaic water pumping system (PVWPS) intended for agricultural irrigation applications, operating without energy storage. The system employs a three-phase induction motor supplied by a three-level neutral point clamped (NPC) inverter. The proposed control strategy integrates the advantages of two distinct controllers to enhance both energy extraction and drive performance. On the photovoltaic side, a fuzzy logic-based maximum power point tracking (MPPT) algorithm is implemented to ensure continuous operation at the global maximum power point under rapidly varying irradiance conditions. On the motor drive side, a direct torque control (DTC) scheme is combined with the multilevel NPC inverter to regulate electromagnetic torque and stator flux. The use of a multilevel inverter significantly mitigates the inherent drawbacks of conventional DTC, notably torque and flux ripples, as well as stator current harmonic distortion. The overall control architecture maximizes power transfer from the photovoltaic generator to the pumping system, resulting in improved dynamic response and energy efficiency. The proposed system is validated through detailed MATLAB/Simulink simulations under abrupt irradiance variations and a realistic daily solar profile corresponding to August conditions in Kairouan, Tunisia. Simulation results demonstrate substantial performance improvements, including an 88% reduction in torque ripples, a 50% decrease in flux ripple, a 77.9% reduction in stator current THD, and a 33.3% enhancement in speed transient response compared to conventional DTC-based systems. Full article

This paper proposes a new fuzzy logic-based rotor flux model reference adaptive system (FLC-MRAS) for rotor speed estimation in induction motor drives, replacing the constant-gain PI controller used in conventional MRAS schemes. The proposed observer simultaneously incorporates both rotor flux and electromagnetic torque errors to enhance estimation accuracy and robustness against load torque disturbances. A nonlinear Mamdani-type fuzzy logic controller (FLC) with two inputs and one output, employing triangular membership functions and seven fuzzy sets, is adopted. The effectiveness and useful operational performance of the proposed approach is examined through extensive simulation cases under various vehicle speed driving profiles and load torque conditions using an indirect vector-controlled induction motor drive. In order to investigate the effective operational performance of a speed estimator, different cases are prepared according to the vehicle requirements. To examine the robustness of the proposed observer under realistic operating conditions, rotor resistance variation is incorporated into the simulation framework. This approach allows assessment of MRAS performance under practical nonlinearities and parameter uncertainties encountered in real applications. Comparative results demonstrate superior speed regulation and speed tracking, reduced estimation error, and faster convergence of the adaptive tuning signal for better speed estimation compared to the PI-MRAS observer. The proposed scheme provides the suitable choice of traction drive adoption for electric vehicle (EV) applications. Full article