Search Results (1,186)

Given the growing need for high-performance operation of 4WID-UGVs, coordinated optimization of trajectory tracking, vehicle stability, and energy efficiency poses a challenge. Existing control strategies often fail to effectively balance these multiple objectives, particularly in integrating energy-saving goals while ensuring precise trajectory following and stable vehicle motion. Thus, a hierarchical control architecture based on Model Predictive Control (MPC) is proposed. The upper-level controller focuses on trajectory tracking accuracy and computes the optimal longitudinal acceleration and additional yaw moment using a receding horizon optimization scheme. The lower-level controller formulates a multi-objective allocation model that integrates vehicle stability, energy consumption, and wheel utilization, translating the upper-level outputs into precise steering angles and torque commands for each wheel. This work innovatively integrates multi-objective optimization more comprehensively within the intelligent vehicle context. To validate the proposed approach, simulation experiments were conducted on S-shaped and circular paths. The results show that the proposed method can keep the average lateral and longitudinal tracking errors at about 0.2 m, while keeping the average efficiency of the wheel hub motor above 85%. This study provides a feasible and effective control strategy for achieving high-performance, energy-saving autonomous driving of distributed drive vehicles. Full article

Sustainable product development demands components that last longer, consume less energy, and can be refurbished within circular supply chains. This study introduces a digital replica-based predictive prototyping workflow for industrial crusher blades that meets these goals. Six commercially used blade geometries (A–F) were recreated as high-fidelity finite-element models and subjected to an identical 5 kN cutting load. Comparative simulations revealed that a triple-edged hooked profile (Blade A) reduced peak von Mises stress by 53% and total deformation by 71% compared with a conventional flat blade, indicating lower drive-motor power and slower wear. To enable fast virtual prototyping and condition-based maintenance, deformation was subsequently predicted using a data-efficient machine-learning model. Multi-view image augmentation enlarged the experimental dataset from 6 to 60 samples, and an XGBoost regressor, trained on computer-vision geometry features and engineering parameters, achieved R² = 0.996 and MAE = 0.005 mm in five-fold cross-validation. Feature-importance analysis highlighted applied stress, safety factor, and edge design as the dominant predictors. The integrated method reduces development cycles, reduces material loss via iteration, extends the life of blades, and facilitates refurbishment decisions, providing a foundation for future integration into digital twin systems to support sustainable product development and predictive maintenance in heavy-duty manufacturing. Full article

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra (SN), pathological accumulation of alpha-synuclein, and chronic neuroinflammation. The aim of this study is to evaluate the serum levels of systemic inflammatory markers such as neutrophil–lymphocyte ratio (NLR), neutrophil-HDL ratio (NHR), monocyte-HDL ratio (MHR), platelet–lymphocyte ratio (PLR), IL-6, IGF-1, systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) in patients with PD, and to analyze the relationship between these markers and the clinical stage of the disease as well as its motor and non-motor symptoms. Methods: Fifty-one patients diagnosed with PD and forty-nine HC matched for age and sex were evaluated prospectively. Results: NLR, NHR, and IGF-1 levels were found to be significantly higher in the PD group compared to the HC group (p < 0.05). There was no significant difference between the two groups in terms of PLR, MHR, SII, and SIRI. No significant relationship was found between the inflammatory markers and disease duration, clinical scales, or symptoms. Conclusions: These findings support the role of systemic inflammation in the pathophysiology of PD. Further multi-center, long-term follow-up studies—including simultaneous measurements of central nervous system inflammation markers—are needed for translation into clinical practice. Full article

This paper addresses the problem of recognising and classifying hand movements to control an upper-limb exoskeleton. To solve this problem, a multisensory system based on the fusion of data from electromyography (EMG) sensors, inertial measurement units (IMUs), and virtual reality (VR) trackers is proposed, which provides highly accurate detection of users’ movements. Signal preprocessing (noise filtering, segmentation, normalisation) and feature extraction were performed to generate input data for regression and classification models. Various machine learning algorithms are used to recognise motor activity, ranging from classical algorithms (logistic regression, k-nearest neighbors, decision trees) and ensemble methods (random forest, AdaBoost, eXtreme Gradient Boosting, stacking, voting) to deep neural networks, including convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformers. The algorithm for integrating machine learning models into the exoskeleton control system is considered. In experiments aimed at abandoning proprietary tracking systems (VR trackers), absolute position regression was performed using data from IMU sensors with 14 regression algorithms: The random forest ensemble provided the best accuracy (mean absolute error = 0.0022 metres). The task of classifying activity categories out of nine types is considered below. Ablation analysis showed that IMU and VR trackers produce a sufficient informative minimum, while adding EMG also introduces noise, which degrades the performance of simpler models but is successfully compensated for by deep networks. In the classification task using all signals, the maximum result (99.2%) was obtained on Transformer; the fully connected neural network generated slightly worse results (98.4%). When using only IMU data, fully connected neural network, Transformer, and CNN–GRU networks provide 100% accuracy. Experimental results confirm the effectiveness of the proposed architectures for motor activity classification, as well as the use of a multi-sensor approach that allows one to compensate for the limitations of individual types of sensors. The obtained results make it possible to continue research in this direction towards the creation of control systems for upper exoskeletons, including those used in rehabilitation and virtual simulation systems. Full article

This paper presents a method for calculating radial forces in switched reluctance motors (SRMs) under radial and tilted air gap eccentricity states. Firstly, the Fourier series method is used to establish a nonlinear model of a switched reluctance motor, which calculates the air gap length at different positions around the motor circumference and applies the Radial Electromagnetic Force (REF) equation to compute the radial force values at various positions under the air gap eccentricity states. Secondly, the finite element method is employed to analyze the factors influencing radial forces in switched reluctance motors under air gap eccentricity states, considering different winding phases and structural parameters as influencing factors. Finally, a measurement platform for radial forces under multiple types of air gap eccentricity states is established to validate the effectiveness of the analytical results for radial forces under radial and tilted air gap eccentricity states. Full article

A wearable closed-loop transcutaneous electrical nerve stimulation (TENS) platform has been developed to address the limitations of conventional open-loop neuromodulation systems. Unlike existing systems such as CLoSES—which targets intracranial stimulation—and electromyography-triggered functional electrical stimulation (EMG-FES) platforms primarily used for motor rehabilitation, the proposed device uniquely integrates low-latency surface electromyography (sEMG)-driven control with six-channel current stimulation in a fully wearable, non-invasive format aimed at ambulatory pain modulation. The system combines real-time sEMG acquisition, adaptive signal processing, a programmable multi-channel stimulation engine, and a high-voltage, boost-regulated power supply within a compact, battery-powered architecture. Bench-top evaluations demonstrate rapid response to EMG events and stable biphasic output (±22 mA) across all channels with high electrical isolation. A human-subject protocol using the Cold Pressor Test (CPT), heart rate variability (HRV), and galvanic skin response (GSR) has been designed to evaluate analgesic efficacy. While institutional review board (IRB) approval is pending, the system establishes a robust foundation for future personalized, mobile neuromodulation therapies. Full article

Background: Efficient decoding of motor imagery (MI) electroencephalogram (EEG) signals is essential for the precise control and practical deployment of brain-computer interface (BCI) systems. Owing to the complex nonlinear characteristics of EEG signals across spatial, spectral, and temporal dimensions, efficiently extracting multidimensional discriminative features remains a key challenge to improving MI-EEG decoding performance. Methods: To address the challenge of capturing complex spatial, spectral, and temporal features in MI-EEG signals, this study proposes a multi-branch deep neural network, which jointly models these dimensions to enhance classification performance. The network takes as inputs both a three-dimensional power spectral density tensor and two-dimensional time-domain EEG signals and incorporates four complementary feature extraction branches to capture spatial, spectral, spatial-spectral joint, and temporal dynamic features, thereby enabling unified multidimensional modeling. The model was comprehensively evaluated on two widely used public MI-EEG datasets: EEG Motor Movement/Imagery Database (EEGMMIDB) and BCI Competition IV Dataset 2a (BCIIV2A). To further assess interpretability, gradient-weighted class activation mapping (Grad-CAM) was employed to visualize the spatial and spectral features prioritized by the model. Results: On the EEGMMIDB dataset, it achieved an average classification accuracy of 86.34% and a kappa coefficient of 0.829 in the five-class task. On the BCIIV2A dataset, it reached an accuracy of 83.43% and a kappa coefficient of 0.779 in the four-class task. Conclusions: These results demonstrate that the network outperforms existing state-of-the-art methods in classification performance. Furthermore, Grad-CAM visualizations identified the key spatial channels and frequency bands attended to by the model, supporting its neurophysiological interpretability. Full article

Borehole trajectory control is a fundamental task for directional well engineers. Now that there are inevitable errors about single-stand control in the field situation, it is difficult to deal with the complex underground problems in real time. In order to improve the efficiency of directional operation and the accuracy of wellbore trajectory control, this paper presents an improved Sparrow Search algorithm by integrating the multi-strategy model and Constant-Toolface models to calculate the single-stand control scheme for single-bent-housing motors in directional drilling. To evaluate the performance of the algorithm, the Particle Swarm algorithm, the Sparrow Search algorithm, and the improved Sparrow Search algorithm (LCSSA) are used to optimize the process parameters for each drilling, respectively. Numerical tests based on drilling data show that all three algorithms can predict the drilling parameters. In contrast, the LCSSA exhibits the fastest convergence and the smallest error after optimizing single-stand control, attaining an average convergence time of 0.08 s. It accurately back-calculated theoretical model parameters with high accuracy and met engineering requirements when applied to actual drilling data. In field applications, the LCSSA reduces the deviation from the planned trajectory by over 25%, restricting the deviation to within 0.005 m per stand; additionally the total drilling time was reduced by at least 18% compared to previous methods. The integration of the LCSSA with the drilling system significantly enhances drilling operations by optimizing trajectory accuracy and boosting efficiency and serves as an advanced tool for designing process parameters. Full article

To address the issue of key component degradation in hybrid electric commercial vehicles under complex driving cycles negatively impacting system economy and durability, this paper proposes a model predictive control (MPC)-based energy management co-optimization strategy. Firstly, dynamic degradation models for the key components are established, enabling high-fidelity characterization of component health status. Secondly, a system-level model incorporating vehicle dynamics, power battery, and electric drive motor is developed, with the degradation feedback mechanism deeply integrated. Building on this foundation, an MPC-based energy management strategy for multi-objective optimization is designed. Its core functionality lies in the cooperative allocation of power sources within a rolling horizon framework: by integrating component degradation status as critical feedback into the control loop, the strategy proactively coordinates the optimization objectives between operational economy (minimization of equivalent energy consumption) and key component durability (degradation mitigation). Simulation results demonstrate that, compared to traditional energy management strategies, the proposed strategy significantly enhances system performance while ensuring vehicle drivability: equivalent energy efficiency improves by approximately 3.5%, component degradation is reduced by up to 87%, and superior state of charge (SOC) regulation capability for the battery is achieved. This strategy provides an effective control method for achieving intelligent, long-life, and high-efficiency operation of hybrid electric commercial vehicles. Full article

Background/Objectives: Early diagnosis of Parkinson’s Disease (PD) is essential for initiating interventions that may slow its progression and enhance patient quality of life. Gait analysis provides a non-invasive means of capturing subtle motor disturbances, enabling the prediction of both disease presence and severity. This study evaluates and contrasts Bayesian-optimized convolutional neural network (CNN) and long short-term memory (LSTM) models applied directly to Vertical Ground Reaction Force (VGRF) signals for Parkinson’s disease detection and staging. Methods: VGRF recordings were segmented into fixed-length windows of 5, 10, 15, 20, and 25 s. Each segment was normalized and supplied as input to CNN and LSTM network. Hyperparameters for both architectures were optimized via Bayesian optimization using five-fold cross-validation. Results: The Bayesian-optimized LSTM achieved a peak binary classification accuracy of 99.42% with an AUC of 1.000 for PD versus control at the 10-s window, and 98.24% accuracy with an AUC of 0.999 for Hoehn–Yahr (HY) staging at the 5-s window. The CNN model reached up to 98.46% accuracy (AUC = 0.998) for binary classification and 96.62% accuracy (AUC = 0.998) for multi-class severity assessment. Conclusions: Bayesian-optimized CNN and LSTM models trained on VGRF data both achieved high accuracy in Parkinson’s disease detection and staging, with the LSTM exhibiting a slight edge in capturing temporal patterns while the CNN delivered comparable performance with reduced computational demands. These results underscore the promise of end-to-end deep learning for non-invasive, gait-based assessment in Parkinson’s disease. Full article

As a core component of both the ship propulsion system and mission-critical equipment, shipboard motors are undergoing a technological transition from traditional fault diagnosis to multi-physical-field collaborative modeling and integrated intelligent maintenance systems. This paper provides a systematic review of recent advances in shipboard motor fault monitoring, with a focus on key technical challenges under complex service environments, and offers several innovative insights and analyses in the following aspects. First, regarding the fault evolution under electromagnetic–thermal–mechanical coupling, this study summarizes the typical fault mechanisms, such as bearing electrical erosion, rotor eccentricity, permanent magnet demagnetization, and insulation aging, and analyzes their modeling approaches and multi-physics coupling evolution paths. Second, in response to the problem of multi-source signal fusion, the applicability and limitations of feature extraction methods—including current analysis, vibration demodulation, infrared thermography, and Dempster–Shafer (D-S) evidence theory—are evaluated, providing a basis for designing subsequent signal fusion strategies. With respect to intelligent diagnostic models, this paper compares model-driven and data-driven approaches in terms of their suitability for different scenarios, highlighting their complementarity and integration potential in the complex operating conditions of shipboard motors. Finally, considering practical deployment needs, the key aspects of monitoring platform implementation under shipborne edge computing environments are discussed. The study also identifies current research gaps and proposes future directions, such as digital twin-driven intelligent maintenance, fleet-level PHM collaborative management, and standardized health data transmission. In summary, this paper offers a comprehensive analysis in the areas of fault mechanism modeling, feature extraction method evaluation, and system deployment frameworks, aiming to provide a theoretical reference and engineering insights for the advancement of shipboard motor health management technologies. Full article

Direct ink writing (DIW) has emerged as a powerful technique for functional-structure fabrication. However, its application to materials with heterogeneous or time-dependent rheology remains limited. This study introduces dual-mode electropneumatic extrusion, supported by a real-time digital twin. This platform integrates a motorized pneumatic cylinder with an electropneumatic pressure regulator, enabling continuous blending of pressure and displacement control. System performance is evaluated across five material characteristics: homogeneity, heterogeneity, time-dependent rheology, self-curing ability, and thermoplasticity. The results demonstrate that feedback current control reduces the linewidth variability to ≈2% and settling time to <250 ms, even under four-fold increases in viscosity. Adaptive pressure ramps restore variability to ≤4% throughout material curing, while hybrid velocity–pressure operation maintains variability at ≤4% and a pore geometry error below 4% over 20 layers. These findings establish a robust framework for rheology-adaptive DIW and offer practical guidelines for implementing dual-mode control in high-throughput, multi-nozzle applications. Full article

In practical applications, precise tuning of current controllers is essential for achieving desirable dynamic performance and stability margins. Traditional tuning techniques rely heavily on accurate plant parameter identification. However, this process is often challenged by inherent nonlinearities and unmodeled dynamics in motor systems. To address this issue, this paper proposes a current loop parameter tuning algorithm based on open-loop frequency sweeping. As the swept Bode diagram reveals nonlinear factors typically neglected during modeling, it provides a basis for control parameter correction. A pulse-sine voltage injection method is first introduced to identify motor parameters, serving as initial values for the controller. By analyzing the magnitude and phase characteristics of the open-loop transfer function, the delay time constant in the high-frequency range can be accurately identified, and mismatched parameters in the low-to-mid frequency range can be corrected. This method does not rely on complex model structures or extensive online adaptation mechanisms. Experimental results on a mechanical test platform demonstrate that the proposed tuning strategy significantly enhances the current loop’s closed-loop bandwidth and dynamic performance. Full article

Micro-motors are essential for power drive systems, and efficient fault diagnosis is crucial to reduce safety risks and economic losses caused by failures. However, the fault signals from micro-motors typically exhibit weak and unclear characteristics. To address this challenge, this paper proposes a novel fault diagnosis method that integrates jump plus AM-FM mode decomposition (JMD), symmetrized dot pattern (SDP) visualization, and an improved convolutional neural network (ICNN). Firstly, we employed the jump plus AM-FM mode decomposition technique to decompose the mixed fault signals, addressing the problem of mode mixing in traditional decomposition methods. Then, the intrinsic mode functions (IMFs) decomposed by JMD serve as the multi-channel inputs for symmetrized dot pattern, constructing a two-dimensional polar coordinate petal image. This process achieves both signal reconstruction and visual enhancement of fault features simultaneously. Finally, this paper designed an ICNN method with LeakyReLU activation function to address the vanishing gradient problem and enhance classification accuracy and training efficiency for fault diagnosis. Experimental results indicate that the proposed JMD-SDP-ICNN method outperforms traditional methods with a significantly superior fault classification accuracy of up to 99.2381%. It can offer a potential solution for the monitoring of electromechanical structures under complex conditions. Full article

In order to improve mathematical model accuracy of electromechanical actuator (EMA) and solve the problems of low-frequency response and large overshoot for nonlinear systems by using traditional proportional integral derivative (PID) algorithm, a fuzzy single neuron (FSN)-PID algorithm is proposed. Firstly, a complete multi-nonlinear dynamic model of EMA is constructed, which introduces internal friction and current limiter of brushless direct current motors (BLDCMs), dead zone backlash of gear trains, and LuGre friction between output shaft and fin. Secondly, a FSN-PID controller is introduced into the automatic position regulator (APR) of EMA control system, where the gain coefficient K of SN algorithm is adjusted by fuzzy control, and the stability of the controller is proved. In addition, simulations are conducted on the response effect of different fin positions under different algorithms for the analysis of the 6° fin position response; it can be concluded that the rise time with FSN-PID algorithm can be reduced by about 4.561% compared to PID, about 1.954% compared to fuzzy (F)-PID, about 0.875% compared to single neuron (SN)-PID, and about 0.380% compared to back propagation (BP)-PID. For the 4°-2 Hz sine position tracking analysis, it can be concluded that the minimum phase error of FSN-PID algorithm is about 0.4705 ms, which is about 74.44% smaller than PID, about 73.43% smaller than F-PID, about 17.24% smaller than SN-PID, and about 10.81% smaller than BP-PID. Finally, wind tunnel experiments investigate the actual high dynamic flight environment and verify the excellent position tracking ability of FSN-PID algorithm. Full article