Search Results (124)

This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional–integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors. Full article

Aiming at the problems of low thermal analysis efficiency and high computational cost of traditional computational fluid dynamics (CFD) methods for modular fault-tolerant permanent magnet synchronous motors (MFT-PMSMs) under complex working conditions, this paper proposes a fast modeling and calculation method of motor temperature field based on a high-efficiency and high-precision network algorithm. In this method, the physical structure of the motor is equivalent to a parameterized network model, and the computational efficiency is significantly improved by model partitioning and Fourth-order Runge Kutta method. The temperature change of the cooling medium is further considered, and the temperature rise change of the motor at different spatial positions is effectively considered. Based on the finite element method (FEM), the space loss distribution under rated, single-phase open circuit and overload conditions is obtained and mapped to the thermal network nodes. Through the transient thermal network solution, the rapid calculation of the temperature rise law of key components such as windings and permanent magnets is realized. The accuracy of the thermal network model was verified by using fluid-structure coupling simulation and prototype test for temperature analysis. This method provides an efficient tool for thermal safety assessment and optimization in the motor fault-tolerant design stage, especially for heat capacity check under extreme conditions and fault modes. Full article

Diagnostic systems for drive with AC motors of key importance for machine safety require the use of limitations related to the processing of measurement information. These limitations result in significant difficulties in assessing the technical condition of the object’s components. The article proposes the use of a combination of artificial intelligence techniques in the form of shallow and convolutional structures in the diagnostics of stator winding damage from an induction motor. The proposed approach ensures a high level of defect detection efficiency while using information preserved in samples from three periods of current signals. The research presents the possibility of combining the data classification capabilities of self-organizing maps (SOMs) with the automatic feature extraction of a convolutional neural network (CNN). The system was verified in steady and transient operating states on a test stand with a 1.5 kW motor. Remarkably, this approach achieves a high detection precision of 97.92% using only 600 samples, demonstrating that this reduced data acquisition does not compromise performance. On the contrary, this efficiency facilitates effective fault detection even in transient operating states, a challenge for traditional methods, and surpasses the 97.22% effectiveness of a reference system utilizing a full 6 s signal. Full article

Permanent-magnet losses in interior permanent-magnet (IPM) motors can result in high magnet temperatures and potential demagnetization. This study investigates using partially segmented magnets as an alternative to traditional segmented magnets to reduce these losses. Partial segmentation involves cutting slots into the magnet to redirect the eddy current path and reduce losses. The research explores analytical and finite element modeling of eddy current losses in partially segmented magnets in IPM machines. Various configurations and orientations of partial segmentation were examined to assess their impact on eddy current losses. Axial slots for the partially segmented magnets were found to be the most effective slotting direction for the baseline IPM motor’s aspect ratio. This study also explores several methods for measuring permanent-magnet loss in IPM machines. A locked rotor test fixture was designed to measure losses induced by switching harmonics. AC loss measurements for the test fixture were conducted to compare magnets with and without partial segmentation. The results showed a significant reduction in permanent-magnet loss for the partially segmented magnets, particularly at higher currents and across all the tested switching frequencies and phase angles. Additionally, the transient temperature of the partially segmented magnets was found to be 12 °C lower than without partial segmentation after a 30 min test. Full article

The piperazine derivative N-(2,6-difluorophenyl)-2-(4-phenylpiperazin-1-yl)propanamide (cmp2) has emerged as a potential transient receptor potential cation channel, subfamily C, member 6 (TRPC6) modulator, offering a promising pathway for Alzheimer’s disease (AD) therapy. Our recent findings identify cmp2 as a novel compound with synaptoprotective effects in primary hippocampal cultures and effective blood–brain barrier (BBB) penetration. In vivo studies demonstrate that cmp2 (10 mg/kg, intraperitoneally) restores synaptic plasticity deficits in 5xFAD mice. This study further shows cmp2’s selectivity towards tetrameric TRPC6 channel in silico. Acute administration of cmp2 is non-toxic, with no indications of chronic toxicity, and Ames testing confirms its lack of mutagenicity. Behavioral assays reveal that cmp2 improves cognitive functions in 5xFAD mice, including increased novel object recognition, better passing of the Morris water maze, and improved fear memory, as well as upregulation of motor function in beam walking tests. These findings suggest that cmp2 holds promise as a candidate for AD treatment. Full article

Most control system implementations rely on single structures optimized for specific performance criteria through rigorous derivation. While effective for their intended purpose, such controllers often underperform in areas outside their primary optimization focus and involve performance trade-offs. A notable example is the Internal Model Principle (IMP) controller, renowned for its robustness and precision in reference tracking under periodic disturbances. However, IMP controllers exhibit poor transient-state performance, characterized by significant overshoot and oscillatory responses, which remains a persistent challenge. To address this limitation, this paper proposes a reinforcement learning (RL)-based hybrid control scheme that overcomes the trade-off in IMP controllers between achieving zero steady-state tracking error and a fast transient response. The proposed method integrates a cascade control structure, optimized for transient-state performance, with an IMP controller, optimized for robust reference tracking under sinusoidal disturbances, through switching logic governed by a Deep Q-Network model. Smooth transitions between control modes are ensured using an internal state update mechanism. The proposed approach is validated through simulations and experimental tests on a direct current (DC) motor position control system. The results demonstrate that the hybrid structure effectively resolves the trade-off associated with IMP controllers, yielding improved performance metrics, such as rapid convergence to the reference, reduced transient overshoot, and enhanced nominal performance recovery against disturbances. Full article

Background: Resection or damage of the supplementary motor area (SMA) is associated with the development of a transient negative motor response defined as SMA syndrome. The risk of neurological deficits after resection in the SMA has been reported to vary widely from 23% to 100%. Various influencing factors can be involved. However, since tumors in the SMA are relatively infrequent, most of the evidence for surgical treatment of these lesions is based on small, retrospective, single-center case series. Furthermore, previous studies focused only on a few variables, and our knowledge regarding the outcome of these patients is still limited. Objective: To better define the risk of neurological deficits after brain tumor resection in the SMA. Methods: We retrospectively reviewed 66 surgeries that involved the SMA for gliomas and metastasis in 53 patients from two separate centers. Out of those, 13 cases were recurrence of the disease. We carefully evaluated various clinical factors, preoperative neuroimaging, intraoperative neurophysiology monitoring, and anatomical factors. By using Fisher’s exact probability test, we examined the relationship between these factors and the occurrence of postoperative neurological deficits. Statistical significance was considered at a p-value of less than 0.05. Results: In 28 cases, patients experienced neurological deficits after surgery. Among those cases, 26 experienced partial SMA syndrome, one experienced complete SMA syndrome, and one experienced a permanent neurological deficit. The research found that the patient’s past medical history (p = 0.005), lack of intraoperative language mapping (p = 0.044), and extent of resection (p = 0.040) significantly influenced the occurrence of language deficits. Additionally, the proximity between the corticospinal tract and the tumor (p = 0.005) and fMRI activation of the SMA in response to motor tasks (p = 0.044) were found to correlate with the development of motor deficits. However, there was no correlation found between the lack of intraoperative monitoring of motor-evoked potentials (MEPs) and the development of motor deficits (p > 0.05). Conclusions: Certain pre-existing medical conditions may increase the risk of postoperative language deficits. Intraoperative language mapping can help prevent these deficits. The extent of resection, along with the anatomical characteristics of the resection cavity, correlates with postoperative outcomes. Tractography and fMRI can assist in predicting the risk of motor deficits. Although intraoperative MEP monitoring can help prevent permanent motor deficits, it does not appear to prevent the transient deficits characteristic of SMA syndrome. Further intraoperative studies are needed to refine mapping and monitoring strategies for tumors involving the SMA and pre-SMA. Full article

This paper proposes a dual-mode thermal analysis framework for the composite cage rotor bearingless induction motor (CCR-BIM), which combines lumped parameter thermal network (LPTN) and finite element thermal model (FETM) methods with experimental verification. The CCR-BIM, an advanced motor design combining torque and suspension windings within a single stator core, offers significant advantages in high-speed and high-precision applications. However, accurate thermal management remains a critical challenge due to its complex structure and increased losses. An LPTN model tailored to the unique thermal characteristics of the CCR-BIM is proposed, and detailed FETM simulations and experimental tests are validated. The LPTN model employs a meshing method to discretize the motor into orthogonal thermal nodes, enabling the rapid and accurate calculation of steady-state temperatures. The FETM further verifies the LPTN results by simulating the transient and steady-state temperature fields. Experimental validation using a 2 kW CCR-BIM test platform confirms the effectiveness of both models, with temperature predictions closely matching measured values. This study provides a reliable thermal analysis method for CCR-BIM. Full article

Enhancing the dynamic response of Flux-Switching Permanent Magnet Synchronous Machines (FSPMSMs) is crucial for high-performance applications such as electric vehicles, renewable energy systems, and industrial automation. Conventional Proportional Integral (PI) controllers within model predictive current control (MPCC) frameworks often struggle to meet the demands of rapid transient response and precise speed tracking, particularly under dynamic operating conditions. To address these challenges, this paper presents a hybrid control strategy that integrates Sliding Mode Control (SMC) into the speed loop of MPCC, aiming to significantly improve the dynamic response and control robustness of FSPMSMs. The feasibility and effectiveness of the proposed approach are validated through high-fidelity real-time simulations using OPAL-RT Technologies’ OP5707XG simulator. Two control schemes are compared: MPCC with a PI controller in the speed loop (MPCC-PI) and MPCC with SMC in the speed loop (MPCC-SMC). Testing was conducted under various operating scenarios, including starting tests, load variations, speed ramping, and speed reversals. The results demonstrate that the MPCC-SMC strategy achieves superior dynamic performance, faster settling times, smoother transitions, and enhanced steady-state precision compared to the MPCC-PI scheme. The comparative results confirm that the MPCC-SMC method outperforms conventional MPCC strategies, making it a compelling solution for advanced motor drive applications requiring enhanced dynamic control. Full article

In CNC machines, the temperature field analysis of spindle servo permanent magnet motors (SSPMMs) under rated load, overload, and weak magnetic conditions is critical for ensuring stable operation and machining accuracy. This paper proposes a temperature calculation method for SSPMMs based on the thermal network method, which is used to quickly evaluate the temperature performance of SSPMMs under different operating conditions during design. This method can calculate the steady-state or transient temperature rise under different operating conditions. First, the electromagnetic performance and heat sources of the SSPMMs were analyzed. Then, based on the thermal network method, the equivalent thermal resistances and equivalent heat dissipation coefficients of the motor components were calculated. By iterating the heat balance equation or solving the heat conduction equation for different operating conditions, the temperature distribution of SSPMMs under different operating conditions was obtained. The accuracy of the thermal network model was validated through temperature analysis using fluid–structure interaction simulations and prototype testing. The results show that the relative error between the winding temperature calculated by the proposed equivalent thermal network model and the measured temperature under different operating conditions is less than 5%. This paper provides a theoretical basis for the thermal management of SSPMM, which can quickly and accurately evaluate the temperature rise in the motor during design. Full article

With their cost-effective manufacturing process, hybrid stepper motors (HSMs) are a popular choice for position control in low-power industrial applications. These versatile motors offer a compelling solution for reducing system costs and size since at standstill/low speeds, HSMs typically have higher torque density with respect to low-power permanent magnet (PM) motors. This higher torque density determines a reduced use of rare-earth PMs and, therefore, a lower environmental footprint. In practical applications, the commonly used microstepping control faces low efficiency, low dynamic performance, vibrations, and a variable maximum continuous torque depending on the working point. In this paper, the operating region of an HSM is extended in the field-weakening (FW) region, showing how field-oriented control (FOC) with FW allows one to strongly increase the drive performance with a slight cost increase thanks to the availability of low-cost magnetic encoders. Due to the fact that FOC provides only the requested current, the HSM faces lower temperatures, lower insulation degradation, and lower permanent magnet demagnetization issues. An experimental evaluation comparing the commonly used microstepping and the proposed FOC with FW is performed on four commercial HSMs with different DC voltage power supplies using an industrial test bench. In particular, the experimental campaign has a focus on steady-state conditions in the case of the maximum continuous torque, showing the advantages of FOC with FW because the advantages in transient conditions are well known. Full article

Stroke affects over 12 million people annually, leading to high mortality, long-term disability, and substantial healthcare costs. Although East Asian herbal medicines are widely used for stroke treatment, the pathways of operation they use remain poorly understood. Our study investigates the neuroprotective properties of Astragalus mongholicus (AM) in acute ischemic stroke using photothrombotic (PTB) and transient middle cerebral artery occlusion (tMCAO) mouse models, as well as an in vitro oxygen-glucose deprivation (OGD) model. Post-OGD treatment with AM improved cell viability in mouse neuroblastoma cells, likely by reducing reactive oxygen species (ROS). Mice received short-term (0–2 days) or long-term (0–27 days) AM treatment post-stroke. Infarct size was assessed using a 2,3,5-triphenyl tetrazolium chloride (TTC) staining procedure alongside magnetic resonance imaging (MRI). Neuroprotective metabolites including inositol (Ins), glycerophosphocholine+phosphocholine (GPc+ PCh), N-acetylaspartate+N-acetylaspartylglutamate (NAA+NAAG), creatine + phosphocreatine (Cr+PCr), and glutamine+glutamate (Glx) were analyzed via magnetic resonance spectroscopy (MRS). Gliosis was assessed using GFAP and Iba-1 immunohistochemical markers, while neurological deficits were quantified with modified neurological severity scores (mNSS). Motor and cognitive functions were assessed using cylinder, rotarod, and novel object recognition (NOR) tests. AM treatment significantly reduced ischemic damage and improved neurological outcomes in both acute and chronic stages of PTB and tMCAO models. Additionally, AM increased neuroprotective metabolites levels, reduced gliosis, and decreased oxidative stress, as evidenced by reduced inducible nitric oxide synthase (iNOS). These findings highlight the antioxidant properties of AM and its strong therapeutic potential for promoting recovery after ischemic stroke by alleviating neurological deficits, reducing gliosis, and mitigating oxidative stress. Full article

Ischemic stroke, caused by blocked cerebral blood flow, requires prompt intervention to prevent severe motor and cognitive impairments. Despite extensive drug development efforts, the failure rate of clinical trials remains high, highlighting the need for novel therapeutic approaches. This study investigated the therapeutic potential of a natural herbal extract mixture of Astragalus mongholicus Bunge (AM) and Scutellaria baicalensis Georgi (SB), traditionally used in Eastern Asian herbal medicine (EAHM) for ischemic stroke treatment. Using transient middle cerebral artery occlusion (tMCAO) and photothrombotic (PTB) mouse models, oral administration of the AM-SB mixture was evaluated during both acute and chronic phases. Results showed that AM-SB significantly reduced infarction volume, inflammation (IL-1β, TNF-α), and pyroptosis-related markers (NLRP3, GSDMD, ASC, Caspase-1), while decreasing gliosis and improving cerebral metabolites. Behavioral assessments revealed that early and sustained AM-SB intervention enhanced motor and cognitive functions, as measured by mNSS, Rotarod, Novel Object Recognition, and Passive Avoidance tests. These findings suggest that AM-SB extract is a promising alternative therapy for ischemic stroke management. Full article

The objective of this study is to evaluate the anticonvulsant efficacy of carbamazepine (CBZ) following acute and chronic administration across four treatment protocols in a murine model of maximal electroshock-induced seizures. A single dose of the drug was utilized as a control. The neurotoxic effects were evaluated in the chimney test and the passive avoidance task. Furthermore, plasma and brain concentrations of CBZ were quantified across all treatment protocols. The subchronic administration of CBZ (7 × 2 protocol) resulted in an attenuation of its antielectroshock effect. In the three remaining treatment regimens (7 × 1, 14 × 1, and 14 × 2) the median effective doses of CBZ were comparable to the control. Neither acute nor chronic treatment with CBZ resulted in a discernible impact on motor coordination or long-term memory. The plasma and brain concentrations of CBZ were significantly lower in most chronic protocols when compared to a single-dose application. This may explain the transient attenuation of CBZ effectiveness in the 7 × 2 protocol, but not the return to the previous level. The anticonvulsant and neurotoxic profiles of CBZ did not differ after single and chronic administration. Therefore, experimental chronic studies with CBZ are not prerequisites for concluding and possibly translating results to clinical conditions. Full article

Traditional tactile brain–computer interfaces (BCIs), particularly those based on steady-state somatosensory–evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users’ selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory–motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value. Full article