Search Results (846)

The global transportation sector is rapidly shifting towards electrification, aiming to create more sustainable environments. As a result, there is a significant focus on optimizing performance and increasing the lifespan of batteries in electric vehicles (EVs). To achieve this, the battery pack must operate with constant current charging and discharging modes of operation. Further, in an EV powertrain, maintaining a constant DC link voltage at the input stage of the inverter is crucial for driving the motor load. To satisfy these two conditions simultaneously during the energy transfer, a hybrid energy storage system (HESS) consisting of a lithium–ion battery and a supercapacitor (SC) connected to the semi-active topology of the bidirectional DC–DC converter (SAT-BDC) in this research work. However, generating the duty cycle for the switches to regulate the operation of SAT-BDC is complex due to the simultaneous interaction of the two mentioned constraints: regulating the DC link voltage by tracking the reference and maintaining the battery current at a constant value. Therefore, this research aims to efficiently resolve the issue by incorporating a highly flexible nonlinear model predictive control (NMPC) to control the switches of SAT-BDC. Furthermore, the converter system design is tested for operational performance using MATLAB 2022B with the battery current and the DC link voltage with different priorities. In the NMPC approach, these constraints are carefully evaluated with varying prioritizations, representing a crucial trade-off in optimizing EV powertrain operation. The results demonstrate that battery current prioritization yields better performance than DC link voltage prioritization, extending the lifespan and efficiency of batteries. Thus, this research work further aligns with the conceptual realization of the sustainability goals by minimizing the environmental impact associated with battery production and disposal. Full article

Background/Objectives: Fibromyalgia (FM) is a chronic condition characterized by widespread musculoskeletal pain, fatigue, and impaired motor performance. This study aimed to investigate the effects of transcranial direct current stimulation (tDCS) during virtual reality (VR) tasks on the motor performance of women with FM. Methods: Participants were divided into two groups: Group A received active tDCS for 10 days followed by sham tDCS for 10 days, while Group B received the opposite sequence. Both groups performed VR tasks using MoveHero software (v. 2.4) during the tDCS sessions. Motor performance was assessed by the number of hits (movement with correct timing to reach the targets) and absolute (accuracy measure) and variable (precision measure) errors during VR tasks. Participants were 21 women, aged 30–50 years, and diagnosed with FM. Results: Group A, which received active tDCS first, presented significant improvements in motor performance (number of hits and absolute and variable errors). The benefits of active tDCS persisted into the sham phase, suggesting a lasting neuroplastic effect. Conclusions: tDCS during VR tasks significantly improved motor performance in women with FM, particularly in complex, extensive movements. These findings indicate that tDCS enhances neuroplasticity, leading to sustained motor improvements, making it a promising therapeutic tool in FM rehabilitation. Full article

The direct current (DC) motor is the core part of an electrical vehicle (EV). The unmatched perturbation of load torque is a challenging problem in the control of an EV system driven by a DC motor and hence a deep control concern is required. In this study, the proposed solution is to present two control approaches based on a backstepping control algorithm for speed trajectory tracking of EVs. The first control design is to develop the backstepping controller based on a quasi-sliding mode disturbance observer (BS-QSMDO), and the other controller is to combine the backstepping control with quasi-integral sliding mode control (BS-QISMC). In the sense of Lyapunov-based stability analysis, the ultimate boundedness of the proposed controllers has been detailedly analyzed, assessed, and evaluated in the presence of unmatched perturbation. A modified stability analysis has been presented to determine the ultimate bounds of disturbance estimation error for both controllers. The determination of ultimate bound and region-of-attraction for tracking and estimation errors is the contribution achieved by the proposed control design. The performances of the proposed controllers have been verified via computer simulations and the level of ultimate bounds for the estimation and tracking errors are the key measures for their evaluation. Compared to BS-QISMC, the results showed that a lower level of ultimate boundedness with a higher convergent rate can be reached based on BS-QSMO. However, a higher control effort can be exerted by the BS-QSMO controller as compared to BS-QISMC; and this is the price to be paid by the BS-QSMO controller to achieve lower ultimate boundedness with a faster convergence rate. Full article

In the realm of electric machines, there has been an increasing interest in multiphase (greater than three-phase) and hybrid excited machines. The benefits of multiphase machines include improved power density, efficiency, reliability, and fault tolerance, while for hybrid electric machines, the literature offers a variety of topologies, each with its own advantages and disadvantages. In essence, the term hybrid for electric machines is used when there is more than one source of excitation, e.g., permanent magnet (PM) excitation combined with or assisted by wound field (WF) excitation. This paper presents an extensive review of the latest topologies in hybrid machines. It explores fundamental principles, multiphase winding, and the advantage of multiphase over three-phase, as well as a comparison of ripple in the DC link for different numbers of phase winding. Additionally, this review discusses applications across industries, including automotive, aerospace, marine, and renewable energy systems. This paper later studies the motoric and generator modes of hybrid machines while considering the machine characteristics in both of these modes. Full article

The use of electric automobiles, or EVs, is essential to environmentally conscious transportation. Battery EVs (BEVs) are predicted to become increasingly accepted for passenger vehicle transportation within the next 10 years. Although enthusiasm for EVs for environmentally friendly transportation is on the rise, there remain significant concerns and unanswered research concerns regarding the possible future of EV power transmission. Numerous motor drive control algorithms struggle to deliver efficient management when ripples in torque minimization and improved dependability control approaches in motors are taken into account. Control techniques involving direct torque control (DTC), field orientation control (FOC), sliding mode control (SMC), intelligent control (IC), and model predictive control (MPC) are implemented in electric motor drive control algorithms to successfully deal with this problem. The present study analyses only sophisticated control strategies for frequently utilized EV motors, such as the brushless direct current (BLDC) motor, and possible solutions to reduce torque fluctuations. This study additionally explores the history of EV motors, the operational method between EM and PEC, and EV motor design techniques and development. The future prospects for EV design include a vital selection of motors and control approaches for lowering torque ripple, as well as additional research possibilities to improve EV functionality. Full article

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease worldwide and is characterized by progressive muscle atrophy. There are currently two approved treatments, but they only relieve symptoms briefly and do not cure the disease. The main hindrance to research is the complex cause of ALS, with its pathogenesis not yet fully elucidated. Retinoids (vitamin A derivatives) appear to be essential in neuronal cells and have been implicated in ALS pathogenesis. This study explores 4-[2-(5,5,8,8-tetramethyl-5,6,7,8-tetrahydroquinoxalin-2-yl)ethylnyl]benzoic acid (Ellorarxine, or DC645 or NVG0645), a leading synthetic retinoic acid, discussing its pharmacological mechanisms, neuroprotective properties, and relevance to ALS. The potential therapeutic effect of Ellorarxine was analyzed in vitro using the WT and SOD1G93A NSC-34 cell model of ALS at an administered concentration of 0.3–30 nM. Histological, functional, and biochemical analyses were performed. Elorarxine significantly increased MAP2 expression and neurite length, increased AMPA receptor GluA2 expression and raised intracellular Ca²⁺ baseline, increased level of excitability, and reduced Ca²⁺ spike during depolarization in neurites. Ellorarxine also displayed both antioxidant and anti-inflammatory effects. Overall, these results suggest Ellorarxine shows relevance and promise as a novel therapeutic strategy for treatment of ALS. Full article

The uniformity of the travel speed of electric reel sprinkling irrigation machines is a key factor affecting irrigation quality. However, conventional PID control is susceptible to sudden disturbances under complex farmland conditions, leading to reduced speed uniformity. To enhance the robustness of the control system, it is necessary to investigate new disturbance rejection control algorithms and their effects. Therefore, a kinematic model of the reel sprinkling irrigation machine and a brushless DC (BLDC) motor model were established, and a linear active disturbance rejection control (LADRC) strategy based on improved particle swarm optimization (IPSO) was proposed. The simulation results show that under variable speed conditions, the system exhibits no overshoot, with an adjustment time of 0.064 s; under variable load conditions, the speed vibration amplitude is less than 0.3%. The field test results indicate that at travel speeds of 10 m/h and 30 m/h, the maximum absolute deviation rate under IPSO-LADRC control is reduced by 27.07% and 13.98%, respectively, compared to PID control. The control strategy based on IPSO-LADRC effectively improves the control accuracy and robustness under complex farmland conditions, providing a reference for enhancing the control performance of other electric agricultural machinery. Full article

In this study, a novel approach of designing automatic control systems with the help of AI tools is proposed. Given plant dynamics, expected references, and expected disturbances, the design of an optimal neural network-based controller is performed automatically. Several common reference types are studied including step, square, sine, sawtooth, and trapezoid functions. Expected reference–disturbance pairs are used to train the system for finding optimal neural network controller parameters. A separate test set is used to test the system for unexpected reference–disturbance pairs to show the generalization performance of the proposed system. Parameters of a real DC motor are used to test the proposed approach. The real DC motor’s parameters are estimated using a particle swarm optimization (PSO) algorithm. Initially, a proportional–integral (PI) controller is designed using a PSO algorithm to find the simple controller’s parameters optimally and automatically. Starting with the neural network equivalent of the optimal PI controller, the optimal neural network controller is designed using a PSO algorithm for training again. Simulations are conducted with estimated parameters for a diverse set of training and test patterns. The results are compared with the optimal PI controller’s performance and reported in the corresponding section. Encouraging results are obtained, suggesting further research in the proposed direction. For low-disturbance scenarios, even simple controllers can have acceptable performance, but the real quality of a proposed controller should be shown under high-amplitude and difficult disturbances, which is the case in this study. The proposed controller shows higher performance, especially under high disturbances, with an 8.6% reduction in error rate on average compared with the optimal PI controller, and under high-amplitude disturbances, the performance difference is of more than 2.5 folds. Full article

Background: Transcranial direct current stimulation (tDCS) is a non-invasive technique known to enhance athletic performance metrics such as vertical jump and lower limb strength. However, it remains unclear whether combining tDCS with the post-activation effects of high-load resistance training can further improve lower limb performance. Objective: This study investigated the synergistic effects of tDCS and high-load resistance training, using electroencephalography to explore changes in the motor cortex and vertical jump dynamics. Methods: Four experiments were conducted involving 29 participants. Each experiment included tDCS, high-load resistance training, tDCS combined with high-load resistance training, and a control condition. During the tDCS session, participants received 20 min of central stimulation using a Halo Sport 2 headset, while the high-load resistance training session comprised five repetitions of a 90% one-repetition maximum weighted half squat. No intervention was administered in the control group. Electroencephalography tests were conducted before and after each intervention, along with the vertical jump test. Results: The combination of tDCS and high-load resistance training significantly increased jump height (p < 0.05) compared to tDCS or high-load resistance training alone. As for electroencephalography power, tDCS combined with high-load resistance training significantly impacted the percentage of α-wave power in the frontal lobe area (F3) of the left hemisphere (F = 6.33, p < 0.05). In the temporal lobe area (T3) of the left hemisphere, tDCS combined with high-load resistance training showed a significant interaction effect (F = 6.33, p < 0.05). For β-wave power, tDCS showed a significant main effect in the frontal pole area (Fp1) of the left hemisphere (F = 17.65, p < 0.01). In the frontal lobe area (F3) of the left hemisphere, tDCS combined with high-load resistance training showed a significant interaction effect (F = 7.53, p < 0.05). The tDCS combined with high-load resistance training intervention also resulted in higher β-wave power in the parietal lobe area (P4) and the temporal lobe area (T4) (p < 0.05). Conclusions: The findings suggest that combining transcranial direct current stimulation (tDCS) and high-load resistance training significantly enhances vertical jump performance compared to either intervention alone. This improvement is associated with changes in the α-wave and β-wave power in specific brain regions, such as the frontal and temporal lobes. Further research is needed to explore the mechanisms and long-term effects of this combined intervention. Full article

In the current study, which focuses on the operational safety problem in intelligent three-dimensional garages, an obstacle avoidance measurement and control scheme for the AGV parking robot is proposed. Under the premise of high-precision distance detection using Kalman filtering, a mathematical model of a brushless DC (BLDC) motor with full-speed range hybrid control is established. MATLAB/Simulink (R2022a) is used to build the control model, which has dual closed-loop vector-controlled motors in the low- to medium-speed range, with photoelectric encoders for speed feedback. The simulation results show that, at lower to medium speeds, the maximum overshoot of the output response curve is 1.5%, and the response time is 0.01 s. However, at higher speeds, there is significant jitter in the speed output waveform. Therefore, the speed feedback is switched to a sliding mode observer (SMO) instead of the original speed sensor at high speeds. Experiments show that, based on the SMO, the problem of speed waveform jitter at high motor speeds can be significantly improved, and the BLDC motor system has strong robustness. The above shows that the motor speed under the full-speed range hybrid control system can meet the AGV control and safety requirements. Full article

Brushed DC motors and generators (DCMs) are extensively used in various industrial applications, including the automotive industry, where they are critical for electric vehicles (EVs) due to their high torque, power, and efficiency. Despite their advantages, DCMs are prone to premature failure due to sparking between brushes and commutators, which can lead to significant economic losses. This study proposes two approaches for determining the temporal and frequency evolution of Shannon entropy in armature current and stray flux signals. One approach indirectly achieves this through prior analysis using the Short-Time Fourier Transform (STFT), while the other applies the Stockwell Transform (S-Transform) directly. Experimental results show that increased sparking activity generates significant low-frequency harmonics, which are more pronounced compared to mid and high-frequency ranges, leading to a substantial rise in system entropy. This finding enables the introduction of fault-severity indicators or Key Performance Indicators (KPIs) that relate the current condition of commutation quality to a baseline established under healthy conditions. The proposed technique can be used as a predictive maintenance tool to detect and assess sparking phenomena in DCMs, providing early warnings of component failure and performance degradation, thereby enhancing the reliability and availability of these machines. Full article

Unmanned Aerial Vehicles (UAVs) have become increasingly prevalent in various applications, ranging from surveillance to package delivery. Achieving precise control of UAV position while enhancing robustness against uncertainties and disturbances remains a critical challenge. In this study, we propose a robust nonlinear control system for a UAV and its actuators, focusing on accurately controlling the position reference vector and improving robustness against parameter uncertainties and external disturbances. The control strategy employs two control loops: an outer loop for the UAV frame and an inner loop for the UAV actuators. The outer loop generates the required angular velocities for the actuators to follow the reference position vector using the UAV’s output and the inner loop ensures that the actuators track these angular velocity references. Both control loops utilize PI-like controllers for simplicity. The proposed system incorporates nonlinear control techniques and estimation strategies for disturbances and parameter variations, enabling dynamic adaptation to changing environmental conditions. Numerical simulations were performed using both Simulink^® and the simulated PX4 Autopilot environment, showing the effectiveness of the proposed control system in achieving precise position control and robust performance for both the UAV and its actuators in the presence of uncertainties and disturbances. These results underscore the potential applicability of the control system in other UAV operational scenarios. Full article

This article introduces a novel regenerative suspension system designed for active seat suspension, to reduce vibrations while recovering energy. The system employs a four-quadrant electric actuator operation model and utilizes a brushless DC motor as an actuator and an energy harvester. This motor, a permanent magnet synchronous type, transforms DC into three-phase AC power, serving dual purposes of vibration energy recovery and active power generation. The system’s advanced vibration control is achieved through the switching of MOSFET transistors, ensuring the suspension system meets operational criteria that contrast with traditional vibro-isolation systems, thereby reducing the negative effects of mechanical vibrations on the human body, while also lowering energy consumption. Comparative studies of the regenerative system dynamics against passive and active systems under random vibrations demonstrated its effectiveness. This research assessed the system’s performance through power spectral density and transmissibility functions, highlighting its potential to enhance energy efficiency and the psychophysical well-being of individuals subjected to mechanical vibrations. The effectiveness of the energy regeneration process under the chosen early excitation vibrations was investigated. Measurements of the motor torque in the active mode and during regenerative braking mode, and the corresponding phase currents of the motor, are presented. Full article

Research and development of power factor corrector (PFC) for AC/DC converters of single-phase AC power supply network are discussed within this article. Two-channel bridgeless PFC is proposed in this paper. The proposed converter allows us to lower current DC component generation in the power network and to reduce static and dynamic losses of semiconductor devices. The suggested solution characteristic features are the absence of a diode bridge while using two identical converters operating in different power network voltage half periods. Due to cumulative chokes in each converter, the function setting the consumption current sinusoidal form is realized with the ability of wide-range output voltage regulation. A number of Simulink-models have been developed in order to study operating modes and to test control algorithms of the proposed bridgeless PFC. The input current harmonic content, efficiency coefficient, passive elements’ electrical parameters, and output voltage pulsation coefficient of the proposed bridgeless PFC were researched by Simulink-models. The results obtained show the efficiency of the proposed solutions regarding PFC. The THD value does not exceed 1.3% in steady state mode and is not over 4% during the voltage stabilization process; the minimal value of the output voltage pulsation coefficient is 3.1%. The suggested solutions can be applied in accumulator batteries’ charging sets and DC motors’ reduced-current start. Full article

Transcranial magnetic stimulation (TMS) represents a distinctive technique for non-invasive brain stimulation. Recent advancements in image processing have enabled the enhancement of TMS by integrating magnetic resonance imaging (MRI) modalities with TMS via a neuronavigation system. The aim of this study is to assess the efficacy of navigated TMS for cortical mapping in comparison to surgical mapping using direct electrical stimulation (DES). This study involved 30 neurosurgical procedures for tumors located in or adjacent to the precentral gyrus. The DES points were compared with TMS responses based on the original distances of vectorial modules. There was a notable similarity in the points obtained from the two mapping methods. The distances between the geometric centers of TMS and DCS were 4.85 ± 1.89 mm. A strong correlation was identified between these vectorial points (r = 0.901, p < 0.001). The motor threshold in TMS was highest in the motor cortex adjacent to the tumor compared to the normal cortex (p < 0.001). Patients with deficits exhibited excellent accuracy in both methods. In view of this, TMS demonstrated reliable and precise application in brain mapping, which is a promising method for preoperative functional mapping in motor cortex tumor surgery. Full article