Search Results (1,385)

As global industry enters the digital era, automation is becoming increasingly pervasive. Due to their superior efficiency and reliability, Permanent Magnet Synchronous Motors (PMSMs) are playing an increasingly prominent role in industrial applications. Sliding Mode Control (SMC) has emerged as a modern control strategy that is widely employed not only in PMSM drive systems, but also across broader power and industrial control domains. This technique effectively mitigates key challenges associated with PMSMs, such as nonlinear behavior and susceptibility to external disturbances, thereby enhancing the precision of speed and torque regulation. This paper provides a thorough review and evaluation of recent advancements in SMC as applied to PMSM control. It outlines the fundamentals of SMC, explores various SMC-based strategies, and introduces integrated approaches that combine SMC with optimization algorithms. Furthermore, it compares these methods, identifying their respective strengths and limitations. This paper concludes by discussing current trends and potential future developments in the application of SMC for PMSM systems. Full article

To address the challenges of insufficient response speed and robustness in optical attitude control systems under highly dynamic disturbances and internal uncertainties, a composite control strategy is proposed in this study. By integrating the proposed piecewise sliding control (P-SMC) with the improved active disturbance rejection control (ADRC), this strategy achieves complementary performance, which can not only suppress the disturbance but also converge to a bounded region fast. Under highly dynamic disturbances, the improved extended state observer (ESO) based on the EKF achieves rapid response with amplified state observations, and the Nonlinear State Error Feedback (NLSEF) generates a compensation signal to actively reject disturbances. Simultaneously, the robust sliding mode control (SMC) suppresses the effects of system nonlinearity and uncertainty. To address chattering and overshoot of the conventional SMC, this study proposes a novel P-SMC law which applies distinct reaching functions across different error bands. Furthermore, the key parameters of the composite scheme are globally optimized using the particle swarm optimization (PSO) algorithm to achieve Pareto-optimal trade-offs between tracking accuracy and disturbance rejection robustness. Finally, MATLAB simulation experiments validate the effectiveness of the proposed strategy under diverse representative disturbances. The results demonstrate improved performance in terms of response speed, overshoot, settling time and control input signals smoothness compared to conventional control algorithms (ADRC, C-ADRC, T-SMC-ADRC). The proposed strategy enhances the stability and robustness of optical attitude control system against internal uncertainties of system and sensor measurement noise. It achieves bounded-error steady-state tracking against random multi-source disturbances while preserving high real-time responsiveness and efficiency. Full article

In order to solve the problem encountered by traditional potato–stem separation devices, that is, they cannot meet the requirements when installed in small-scale harvesters, a new type of vertical differential roller potato–stem separation device was developed. The device features a compact structure and simultaneously possesses both separating and conveying functions. Through the analysis of the separation force between potato and stem, the structure and parameters of the separation device were determined. The simulation and the field test of the potato–stem separation process were carried out with the vertical differential roller speed, the vertical differential roller gap width and the conveyor chain speed as the influencing factors. The simulation test analysed the influence law of different working parameters on the performance of potato–stem separation. The field test revealed the order of the effects of various factors on the impurity rate and skin-breaking rate, concluding that the optimal combination of operational parameters was a vertical differential roller rotational speed of 6 s⁻¹, a vertical differential roller gap width of 7 mm, and a conveyor chain speed of 1.4 m·s⁻¹. This experiment fills the research gap in the study of potato–stem separation devices suitable for small-scale potato harvesters and promotes the development of compact potato harvesters. Full article

As the core component of electrified railway power supply systems, the fatigue performance and reliability of catenary support structures are directly related to the operational safety of high-speed railways. To address the problem of structural fatigue damage caused by increasing train speed and high-frequency operation, this study develops a refined finite element model including a support structure, suspension system and support column, and the dynamic response characteristics and fatigue life evolution law under train operation conditions are systematically analyzed. The results show that under the conditions of 250 km/h speed and 100 times daily traffic, the fatigue lives of the limit locator and positioning support are 43.56 years and 34.48 years, respectively, whereas the transverse cantilever connection and inclined cantilever have infinite life characteristics. When the train speed increases to 400 km/h, the annual fatigue damage of the positioning bearing increases from 0.029 to 0.065, and the service life is shortened by 55.7% to 15.27 years, which proves that high-speed working conditions significantly aggravate the deterioration of fatigue in the structure. The reliability analysis based on Monte Carlo simulation reveals that when the speed is 400 km/h and the daily traffic is 130 times, the structural reliability shows an exponential declining trend with increasing service life. If the daily traffic frequency exceeds 130, the 15-year reliability decreases to 92.5%, the 20-year reliability suddenly decreases to 82.4%, and there is a significant inflection point of failure in the 15–20 years of service. Considering the coupling effect of environmental factors (wind load, temperature and freezing), the actual failure risk may be higher than the theoretical value. On the basis of these findings, engineering suggestions are proposed: for high-speed lines with a daily traffic frequency of more than 130 times, shortening the overhaul cycle of the catenary support structure to 7–10 years and strengthening the periodic inspection and maintenance of positioning support and limit locators are recommended. The research results provide a theoretical basis for the safety assessment and maintenance decision making of high-speed railway catenary systems. Full article

We present a model of superfluidity based on the internal variable theory. We consider a two-component fluid endowed with a scalar internal variable whose gradient is the counterflow velocity. The restrictions imposed by the second law of thermodynamics are obtained by applying a generalized Coleman–Noll procedure. A set of constitutive equations of the Landau type, with entropy, entropy flux and stress tensor depending on the counterflow velocity, is obtained. The propagation of acceleration waves is investigated as well. It is shown that the first-and-second sound waves may propagate along the system with speeds depending on the physical parameters of the two fluids. First sound waves may propagate in the same direction or in the opposite direction of the counterflow velocity, depending on the concentration of normal and superfluid components. The speeds of second sound waves have the same mathematical form of those propagating in dielectric crystals. Full article

The accuracy of numerical model outputs strongly depends on the quality of the initial wind field, yet ground observation data are typically sparse and provide incomplete spatial coverage. More importantly, many current mainstream correction models rely on reanalysis grid datasets like ERA5 as the true value, which relies on interpolation calculation, which directly affects the accuracy of the correction results. To address these issues, we propose a new deep learning model, PPWNet. The model directly uses sparse and discretely distributed observation data as the true value, which integrates observation point positions and a physical consistency term to achieve a high-precision corrected wind field. The model design is inspired by biological intelligence. First, observation point positions are encoded as input and observation values are included in the loss function. Second, a parallel dual-branch DenseInception network is employed to extract multi-scale grid features, simulating the hierarchical processing of the biological visual system. Meanwhile, PPWNet references the PointNet architecture and introduces an attention mechanism to efficiently extract features from sparse and irregular observation positions. This mechanism reflects the selective focus of cognitive functions. Furthermore, this paper incorporates physical knowledge into the model optimization process by adding a learned physical consistency term to the loss function, ensuring that the corrected results not only approximate the observations but also adhere to physical laws. Finally, hyperparameters are automatically tuned using the Bayesian TPE algorithm. Experiments demonstrate that PPWNet outperforms both traditional and existing deep learning methods. It reduces the MAE by 38.65% and the RMSE by 28.93%. The corrected wind field shows better agreement with observations in both wind speed and direction, confirming the effectiveness of incorporating position information and a physics-informed approach into deep learning-based wind field correction. Full article

This research study investigated the composite magnetic fields of underwater vehicles in the presence of ocean waves under varying conductivity, analyzed their spatiotemporal characteristics, attenuation laws, and influence mechanism. We integrated the modeling of three types of magnetic fields to obtain a composite magnetic field: the magnetic anomaly field generated by a ferromagnetic vehicle was simulated with a hybrid ellipsoid–dipole model, the wake magnetic field generated by its motion, and the ocean wave magnetic field generated by wind-driven waves were derived from the velocity fields. Simulation results show that the magnetic anomaly and wake magnetic fields are mainly influenced by vehicle speed, course, and diving depth, while the ocean wave magnetic field is affected by wind speed and direction. The composite magnetic field’s intensity increases with vehicle and wind speed but decreases with the increase in diving depth. This study offers a comprehensive analysis of the composite magnetic fields of underwater vehicles in the presence of ocean waves, emphasizing the significant impact of vehicle motion and marine environmental parameters. These insights are essential to gaining a deeper understanding of the magnetic fields generated by underwater vehicles as they navigate ocean waves. Full article

This paper addresses the path tracking problem of underactuated unmanned surface vessels (USVs) in the presence of unknown external disturbances. A fixed-time adaptive integral sliding mode control (AISMC) method, incorporating a nonlinear disturbance observer (NDO), is proposed. Initially, a three-degree-of-freedom dynamic model of the USV is developed, accounting for external disturbances and model uncertainties. Based on the vessel’s longitudinal and transverse dynamic position errors, a virtual control law is designed to ensure fixed-time convergence, thereby enhancing the position error convergence speed. Next, a fixed-time NDO is introduced to estimate real-time external perturbations, such as wind, waves, and currents. The observed disturbances are fed back into the control system for compensation, thereby improving the system’s disturbance rejection capability. Furthermore, a sliding mode surface is designed using a symbolic function to address the issue of sliding mode surface parameter selection, leading to the development of the adaptive integral sliding mode control strategy. Finally, compared with traditional SMC and PID, the proposed AISMC-NDO offers higher accuracy, faster convergence, and improved robustness in complex marine environments. Full article

Wet clutches are extensively employed in automotive transmission systems due to their benefits of smooth shift and stable operation. However, existing methodologies have not yet thoroughly analyzed the real-time dynamic temperature distribution of wet clutches, and the heating and heat transfer mechanisms during the sliding friction process of friction pairs remain underexplored. To address these gaps, this study proposes a real-time dynamic temperature prediction model for wet clutches and investigates the heat generation and transfer mechanisms in the friction pair sliding process. Specifically, the heat production and exchange dynamics of the wet clutch friction pair are systematically analyzed, followed by an examination of the real-time temperature variation of the separator plate under both high-slip and low-slip speed conditions. In the numerical simulations, the predicted temperature values from the proposed model demonstrate excellent agreement with experimental measurements, with dynamic peak temperature discrepancies remaining within ±2 °C. Furthermore, the validated temperature evolution laws are corroborated by experimental results obtained from a dedicated wet clutch performance test rig, thereby providing comprehensive empirical verification of the proposed real-time dynamic temperature prediction framework for wet clutch separator plates. In summary, the model can accurately capture the temperature variation characteristics of wet clutches under different operating conditions, providing a reliable basis for real-time thermal management of transmission systems. It holds significant practical value for optimizing cooling system design, extending clutch service life, and ensuring shifting quality in vehicles. Full article

Aiming at the control performance degradation of permanent magnet synchronous motor (PMSM) drive systems caused by uncertainties of internal and external disturbances, a robust control algorithm integrating an improved fast terminal sliding mode (IFTSM) surface with a novel adaptive reaching law (NARL) is proposed. A dynamic model of PMSM with disturbances is established, and an improved fast terminal sliding mode surface is designed. By introducing nonlinear terms and error derivative feedback mechanisms, the finite-time rapid convergence of system states is achieved, while solving the singularity problem of traditional terminal sliding mode control. Combined with the novel adaptive reaching law strategy, a state-dependent gain adjustment function is used to dynamically optimize the balance between reaching speed and chattering, enhancing the smoothness of the system′s dynamic response. Through the synergy of the finite-time convergence characteristic of the improved sliding mode surface and the novel adaptive reaching law, the proposed algorithm significantly enhances the system′s anti-interference capability against load mutations and parameter time variations. Experiment results demonstrate that under complex working conditions, the algorithm achieves superior speed tracking accuracy and current stability, providing a control solution with strong anti-interference capability and fast response for PMSM speed control systems. Full article

A physics-based vehicle–track coupled dynamic model embedding a hydraulic electromechanical regenerative damper (HERD) is developed to quantify electrical power recovery and wear depth in high-speed service. The HERD subsystem resolves compressible hydraulics, hydraulic rectification, line losses, a hydraulic motor with a permanent-magnet generator, an accumulator, and a controllable; co-simulation links SIMPACK with MATLAB/Simulink. Wheel–rail contact is computed with Hertz theory and FASTSIM, and wear depth is advanced with the Archard law using a pressure–velocity coefficient map. Both HERD power regeneration and wear depth predictions have been validated against independent measurements of regenerated power and wear degradation in previous studies. Parametric studies over speed, curve radius, mileage and braking show that increasing speed raises input and output power while recovery efficiency remains 49–50%, with instantaneous electrical peaks up to 425 W and weak sensitivity to curvature and mileage. Under braking from 350 to 150 km/h, force transients are bounded and do not change the lateral wear pattern. Installing HERD lowers peak wear in the wheel tread region; combining HERD with flexible wheelsets further reduces wear depth and slows down degradation relative to rigid wheelsets and matches measured wear more closely. The HERD electrical load provides a physically grounded tuning parameter that sets hydraulic back pressure and effective damping, which improves model accuracy and supports calibration and updating of digital twins for maintenance planning. Full article

Pre-extraction gas technology is commonly used in coal mines to extract gas from single coal seams, initial protective layers, and both unprotected and protected coal seams. With the development of drilling equipment, directional long drilling, pre-extraction, coal seam gas technology has been widely applied, and negative pressure extraction is one of the key factors affecting the effectiveness of directional long drilling gas extraction. In order to determine the reasonable length of directional long boreholes, studying the negative pressure distribution and time-varying rules within such boreholes is of great significance for guiding later borehole layout and gas extraction. The COMSOL Multiphysics software v.5.3. was used to couple and solve the dynamic model of temperature, stress, and seepage in coal-containing gas, as well as the mathematical model of negative pressure attenuation in directional long boreholes. The gas pressure distribution in the coal surrounding the directional long borehole and the distribution and time-varying law of negative pressure in the borehole were studied. Then, the distribution and time-varying law of negative pressure in directional long borehole extraction were tested on site. Research has shown that the negative pressure attenuation during directional long drilling has a relatively small impact on the effectiveness of coal gas extraction, while the negative pressure at the hole opening is the key factor affecting the effectiveness of gas extraction. In the early stage of extraction, as the drilling depth increases, the pressure loss inside the hole increases and the negative pressure inside the hole decreases. As the extraction time becomes longer, the pressure loss inside the borehole decreases and the negative pressure inside the borehole gradually returns to the negative pressure value at the orifice. The gas flow velocity inside the extraction borehole gradually increases from the bottom of the hole to the hole opening, and the flow velocity at the bottom of the hole remains basically constant. The gas flow velocity inside the hole gradually decreases with the extension of extraction time, and the smaller the distance from the extraction hole opening, the greater the flow attenuation. The collapse of drilling holes during extraction affects the attenuation of negative pressure inside the hole in the short term. As the extraction time increases, the impact of the collapse on the negative pressure inside the hole is limited. The temperature of coal can significantly affect the negative pressure and gas flow distribution inside the pores. Considering the temperature effect, the gas flow velocity inside the pores is higher and the pressure loss is lower in the short term. On-site tests have determined that the depth of ultra-long directional drilling holes is shallower than 327 m, and the negative pressure changes inside the borehole are not significantly different from the negative pressure at the hole opening. The negative pressure stabilization speed near the hole opening and bottom is fast, usually reaching its peak within 3–10 min. The negative pressure stabilization process from the borehole opening to the hole bottom shows a “fast slow fast” trend. When using double-sided extraction, the time for negative pressure to reach stability is significantly shortened compared to single-sided extraction, and double-sided extraction is beneficial for improving the effectiveness of coalbed methane extraction. Full article

Face recognition technology utilizes unique facial features to analyze and compare individuals for identification and verification purposes. This technology is crucial for several reasons, such as improving security and authentication, effectively verifying identities, providing personalized user experiences, and automating various operations, including attendance monitoring, access management, and law enforcement activities. In this paper, comprehensive evaluations are conducted using different face detection and modality segmentation methods, feature extraction methods, and classifiers to improve system performance. As for face detection, four methods are proposed: OpenCV’s Haar Cascade classifier, Dlib’s HOG + SVM frontal face detector, Dlib’s CNN face detector, and Mediapipe’s face detector. Additionally, two types of feature extraction techniques are proposed: hand-crafted features (traditional methods: global local features) and deep learning features. Three global features were extracted, Scale-Invariant Feature Transform (SIFT), Speeded Robust Features (SURF), and Global Image Structure (GIST). Likewise, the following local feature methods are utilized: Local Binary Pattern (LBP), Weber local descriptor (WLD), and Histogram of Oriented Gradients (HOG). On the other hand, the deep learning-based features fall into two categories: convolutional neural networks (CNNs), including VGG16, VGG19, and VGG-Face, and Siamese neural networks (SNNs), which generate face embeddings. For classification, three methods are employed: Support Vector Machine (SVM), a one-class SVM variant, and Multilayer Perceptron (MLP). The system is evaluated on three datasets: in-house, Labelled Faces in the Wild (LFW), and the Pins dataset (sourced from Pinterest) providing comprehensive benchmark comparisons for facial recognition research. The best performance accuracy for the proposed ten-feature extraction methods applied to the in-house database in the context of the facial recognition task achieved 99.8% accuracy by using the VGG16 model combined with the SVM classifier. Full article

In order to enhance the tracking accuracy and disturbance rejection capability in the speed loop of an optoelectronic tracking servo control system, a parameter self-adjusting disturbance rejection generalized predictive control method (STGPC) based on a continuous-time model is proposed in this paper. First, a dynamic model of the servo turntable system is established, and a linear extended state observer (LESO) is designed to perform real-time estimation of internal and external disturbances in the system. Second, a generalized predictive control law incorporating the predictive model, performance metrics, and rolling optimization is systematically derived, where the reference trajectory is generated by a tracking differentiator and the system state is provided in real time by the LESO. Furthermore, a gradient descent method is innovatively introduced to achieve adaptive adjustment in the predictive time domain, and the stability of the closed-loop system is rigorously proven based on Lyapunov theory. Finally, simulation experiments were conducted to verify the tracking performance, disturbance rejection performance, and time-domain parameter self-adjustment effects of the control method. Simulation results show that compared with PID control and traditional linear generalized predictive control (LGPC), the proposed STGPC method reduces speed tracking residuals by 73.79% and 51.04%, respectively, enhances disturbance suppression capability for speed vibration disturbances by 50.55% and 47.55%, respectively, and enhances compensation capability for friction torque disturbances by 68.03% and 59.33%, respectively. The system demonstrates outstanding velocity tracking accuracy and disturbance rejection while exhibiting good robustness against system parameter perturbations. Full article

This article addresses the problem of singularity-free fixed-time tracking control for multiple unmanned surface vehicles (USVs) with model uncertainties. To compensate for the uncertain nonlinearities in the multi-USV systems, fuzzy logic approximators are employed to estimate unknown hydrodynamic parameters. By integrating adaptive fixed-time control theory with backstepping methodology, a novel singularity-free fixed-time consensus control scheme is developed, incorporating a error switching mechanism to prevent singularities arising from the differentiation of speed control laws. Through rigorous analysis via fixed-time stability theory, the proposed control scheme guarantees that consensus tracking errors reach a small region around zero within fixed-time. Numerical simulations demonstrate the efficacy of the presented method. Full article