Search Results (2,937)

To address the issues of sluggish dynamic response, significant steady-state fluctuations, and poor disturbance rejection associated with traditional proportional–integral (PI) and conventional speed control methods, a novel sensorless sliding-mode speed control strategy for permanent magnet synchronous motors (PMSMs) based on the super-twisting algorithm (STA) is proposed. First, an advanced sliding-mode speed controller is designed by integrating an integral nonsingular fast terminal sliding-mode surface with the STA, thereby enhancing the dynamic response and transient stability of the PMSM under speed variations. Subsequently, to mitigate inherent sliding-mode chattering, a novel load torque observer is developed. This observer continuously feeds forward real-time load estimates to the speed controller, which substantially improves the system’s robustness against external disturbances. Furthermore, to eliminate the reliance on mechanical sensors and ensure reliable operation across diverse scenarios, an improved sliding-mode observer (SMO) incorporating the STA is utilized to achieve more precise rotor position and speed estimation. Finally, an experimental platform is established to conduct comprehensive variable-speed and variable-load tests on the PMSM. Experimental results demonstrate that the proposed method improves the dynamic response and disturbance immunity of the PMSM by 58.33% and 71.75%, respectively, while reducing steady-state fluctuations by 33.33%. These results demonstrate the effectiveness of the proposed sensorless sliding-mode control strategy and show improved speed regulation performance for PMSM drives. Full article

Gadolinium is a rare-earth element that is promising for the field of biomedicine due to its unique properties that enhance image quality, giving it high potential in targeted cancer therapy, antimicrobial treatments, etc. The disadvantage of Gd-containing materials is their high toxicity. In this work, ensembles of Fe and Al₂O₃ nanoparticles were fabricated by the electric explosion of wire and Gd ribbons using rapid quenching techniques. Stable Fe, Fe/Gd and Fe/Gd/Al₂O₃ aqueous suspensions with a Z-potential of about –54 mV were fabricated by the ball-milling mechanosynthesis of Fe (100%), Fe and Gd (70 and 30 wt. % accordingly) and Fe, Al₂O_3, and Gd (69, 30 and 1 wt.% accordingly). Fillers from suspensions were used for the synthesis of epoxy composites mimicking natural tissue with embedded magnetic particles. The concentration range for synthesized epoxy composites (0, 5, 10, and 15 wt.% of the filler) corresponded to the biomedical range of interest. Thin-film magnetoimpedance (MI) elements were prepared by a sputtering technique: conventional [FeNi/Cu]₅/Cu/[Cu/FeNi]₅ (NP) element and [FeNi/Cu]₅/Cu/[Cu/P{FeNi]₅} element with patterned top multilayer (SqP). They showed a maximum MI ratio of about 160% for NP and about 60% for SqP. MI sensor response was affected by the presence of filled magnetic composites in the shape of cylinders (5 mm × 4 mm) situated at about 1 mm due to the stray fields in the filler. MI response showed a linear dependence on the filler concentration for each selected position. These results open the possibility to develop new iron- and gadolinium-containing materials for simultaneous magnetic imaging and detection by magnetic field sensors, extending the functional properties of Fe/Gd materials for biomedical devices and therapies. Full article

Carbon/inorganic hybrid composites have evolved from filler-reinforced materials into design platforms for coupled electromagnetic, thermal, sensing, environmental, protective and energy-related functions. Their distinctive value lies in the possibility of combining a conductive, polarizable or porous carbon phase with an inorganic phase that contributes dielectric, magnetic, catalytic, ionic, thermally conductive or barrier behavior. This review examines carbon/inorganic hybrid multifunctional composites from the viewpoint of structure–property relationships, with emphasis on interfacial design, percolation, anisotropy, hierarchical architecture, processing and metrology. Selected graphitic composite studies are discussed as case studies for broadband dielectric spectroscopy, microwave shielding, high-frequency contact metrology, thermal diffusivity analysis and impedance-monitored graphene filters; these case studies are integrated with the broader international literature on CNT and graphene polymer composites, MXene films and foams, graphene/metal oxide photocatalysts, boron nitride/carbon thermal networks, biochar–graphene adsorbents, smart coatings, sensors, supercapacitors and water remediation systems. The central argument is that credible multifunctionality requires more than measuring several properties on the same material. It requires simultaneous or service-relevant co-optimization under constraints of thickness, density, processability, aging, humidity, corrosive media, regeneration, toxicity, economic feasibility and scalable fabrication. The review concludes with design rules and reporting recommendations intended to help move the field from impressive property demonstrations toward application-ready hybrid material systems. Full article

Traditional permanent magnet synchronous motor (PMSM) control systems rely on mechanical position sensors for high-precision rotor position and speed information, which increases hardware complexity, raises system cost, reduces reliability, and limits adaptability to harsh environments. To overcome the above limitations, this paper proposes a novel high-performance sensorless speed control strategy for PMSMs, which is constructed based on a non-singular terminal sliding mode observer (NTSMO) and a non-singular terminal sliding mode controller (NTSMC). First, an improved fast power reaching law (IFPRL) is proposed, which consists of a variable exponential reaching term and a power reaching term. Specifically, the gain of the exponential reaching term is dynamically adjusted by the absolute value of the sliding mode switching function, enabling the reaching law to operate in two different modes throughout the entire convergence process of the system state. Moreover, the introduction of scaling coefficient c compensates for the performance degradation caused by variations in the range of sliding mode surfaces (SMSs) in different systems. The proposed IFPRL not only effectively mitigates the inherent chattering issue, it also expedites the rate at which the system state converges to its SMS. On this basis, both the NTSMO for rotor position observation and the NTSMC for speed closed-loop control are designed by embedding the proposed IFPRL into the framework of non-singular terminal sliding mode control theory. Finally, the effectiveness of the proposed method is validated through numerical simulations and experimental tests. Experimental results demonstrate that the proposed IFPRL-based NTSMC + NTSMO scheme reduces the root mean square error (RMSE) of speed control by 2.7% relative to the traditional SMC + SMO method. The proposed method realizes reliable sensorless speed control for PMSMs and exhibits superior dynamic response, higher control accuracy, and stronger robustness against disturbances. Full article

The conventional robotic position control is gradually being replaced by force control, which is commonly used in humanoid robot applications that require force interaction with the environment, force transmission, or contact. A high-back-drive-efficiency actuator with a high-torque-density permanent magnet motor connecting the low-ratio planetary reducer is widely applied in interactive robotic systems without a torque/force sensor. This paper proposes a high-torque-density permanent magnet motor with an external rotor structure, which can realize torque enhancement by the increased air-gap diameter and better space utilization by the internal planetary reducer, i.e., the reducer inside the stator. First, the motor topologies with different slot/pole number combinations are introduced. Then, the optimization of a slot/pole number combination is elaborated for the maximum torque and torque mass density. In addition, the influence of the slot/pole number combination on the torque characteristic and overload capability is investigated by the finite element (FE) method. The experimental results of the prototype motor are provided to verify the analysis. Full article

Continuous monitoring of lower-limb kinematics in natural environments is essential for gait analysis and rehabilitation but remains challenging due to the limitations of optical systems and the inaccuracy of sparse inertial sensor methods. To address this, we propose a high-precision, minimalist wearable system utilizing only three inertial measurement units placed on the pelvis and shanks. In the data preprocessing stage, engineering modifications are made based on the traditional gradient descent algorithm to implement adaptive channel adjustment on the acceleration and magnetic data of a single IMU, aiming to alleviate the impact of motion acceleration and external magnetic interference on the temporal feature manifold. Subsequently, a pure Transformer neural network is utilized to capture long-range temporal dependencies, reconstructing full lower-limb kinematics without relying on rigid biomechanical assumptions. The model was optimized and deployed on an STM32N647 microcontroller to achieve real-time edge inference with a low latency of approximately 17 ms. Experimental results demonstrate that the proposed method achieves a mean absolute error of 2.41° for level walking, significantly outperforming traditional constrained Kalman filter approaches. Furthermore, it maintains high tracking robustness during complex nonlinear movements such as squatting and lunging. In conclusion, this edge-computing-enabled framework provides an accurate, comfortable, and real-time solution for unconstrained human motion capture in daily scenarios. Full article

Inertial Measurement Units (IMUs) enable portable, multibody motion capture in diverse environments beyond the laboratory, making them a desirable choice for diagnosing mobility disorders and supporting rehabilitation in clinical or home settings. However, challenges associated with IMU measurements, including magnetic distortions and errors due to integration drift, complicate their broader use for motion capture. In this work, we propose a tightly coupled motion-capture approach that directly integrates IMU measurements with multibody dynamic models via an iterated extended Kalman filter to simultaneously estimate the system’s kinematics and kinetics. By enforcing the complete multibody system dynamics and utilizing only accelerometer and gyroscope data, our method accurately estimates joint kinematics and kinetics. Our algorithm is designed to fuse different sensor data, such as optical motion-capture measurements and joint torque readings, to further enhance estimation accuracy. We validated our approach using highly accurate ground-truth data from a 3-degree-of-freedom pendulum and a 6-degree-of-freedom collaborative robot. We demonstrate a maximum root-mean-square difference of 3.75° in the pendulum’s computed joint angles with respect to the marker motion-capture inverse kinematics. For the robot, we observed a maximum joint angle root-mean-square difference of 3.24° with respect to the joint encoders, while the maximum joint angle root-mean-square difference of the optical motion-capture inverse kinematics with respect to the encoders was 1.16°. With regard to kinetic estimates, we report a maximum joint torque root-mean-square difference of 3.02 Nm in the pendulum with respect to the marker motion-capture inverse dynamics and 4.27 Nm in the robot relative to its joint torque sensors. Full article

This paper introduces a weak-magnetic-field measurement system characterized by a large-scale uniform magnetic field and a low magnetic limit of detection (LOD). The system employs a four-ring coil assembly housed within a multi-layer magnetic shielding cavity, generating a uniform magnetic field region of 120 mm while achieving a minimum LOD of less than 10 pT. The performance of the weak-magnetic-field measurement system is appropriately validated using a bulk magnetic–electric (ME) sensor. The experimental results confirm the system’s dual functionalities in both magnetic sensor calibration and the measurement of weak magnetic parameters. Notably, this methodology is readily applicable to various forms of weak-magnetic-field measurement. Full article

This study investigates the modulation of coercivity and magnetodielectric coupling in heat-treated, nickel-substituted lanthanum ferrite. LaFe_0.7Ni_0.3O₃ samples were synthesized by high-energy ball milling and sintered at temperatures between 1073 and 1473 K. Chemical composition, crystalline structural evolution, surface morphology, magnetic, dielectric, and electrical properties, as well as magnetodielectric coupling, were analyzed. The XPS spectra revealed the presence of adsorbed oxygen, associated with the high oxygen affinity of the material. This behavior is interpreted as a charge-compensation mechanism, related both to the formation of oxygen vacancies and to the partial oxidation of Fe³⁺ to Fe⁴⁺. XRD and Rietveld refinement confirmed a single-phase orthorhombic Pnma structure, and structural simulations revealed progressive octahedral distortions with increasing temperature, affecting the octahedral tilting and electronic bandwidth. Magnetic characterization revealed that thermal processing modifies the magnetic behavior, inducing weak ferromagnetism and a significant increase in coercivity, correlating with progressive densification, greater domain stability, and reduced microstrain. Impedance measurements revealed magnetodielectric coupling, the Maxwell–Wagner interfacial polarization mechanism, and reduced dielectric losses. These findings demonstrate that the coercivity and magnetodielectric response in cationic nickel-substituted lanthanum ferrite can be tuned through thermal processing. A semi-empirical magnetocrystalline anisotropy model is proposed to explain the coercivity evolution and associated multiferroic behaviors, thus contributing to the study of functional ferrites as sustainable alternatives to rare-earth magnetic materials with potential in sensors and memory devices. Full article

Characteristic gas-based detection technology can facilitate the warning of lithium-ion battery thermal runaway with a high accuracy at an early stage. Microelectromechanical system (MEMS) metal–oxide–semiconductor (MOS) gas sensors have advantages of a low cost, a high accuracy, and low power consumption; therefore, they are ideal candidates for the lithium-ion battery thermal-runaway warning. MEMS MOS gas sensors are composed of a micro-hotplate and gas-sensitive materials. The micro-hotplate component strongly influences the device’s mechanical and thermal properties. Initially, we used COMSOL to optimize the micro-hotplate component. Then, we fabricated the device based on the optimal micro-hotplate. Next, gas-sensitive materials made of ZnO and ZnO-Au were deposited on the micro-hotplate by radio-frequency magnetic sputtering. The self-made and commercial MEMS MOS sensors were integrated to form an electronic nose. The as-made electronic nose can classify hydrogen, ethylene, acetylene, methane, carbon monoxide, and ethanol with a maximum accuracy of 99.4% using gas response data acquired over only 20 s. The reported work can provide a solution for an early and accurate lithium-ion battery thermal runaway warning. Full article

Rotor imbalance and abnormal vibration are classical operating conditions in rotating machinery and can often be identified by conventional vibration analysis. However, the development of low-power, self-powered, and distributed sensing nodes remains important for long-term condition monitoring, particularly in scenarios where external power supply, wiring, and maintenance are constrained. Existing vibration sensors, including piezoelectric and capacitive types, are constrained by power consumption and degraded performance under low-frequency and weak excitation. To address this issue, a magnetically repulsive cushion triboelectric nanogenerator (MRCT) is proposed to enable self-powered vibration sensing. The magnetic-repulsion cushion allows the upper friction layer to undergo stable contact–separation motion under a non-contact restoring force, while the microstructured strip electrode array (MSEA) enhances the triboelectric output and signal stability. A hybrid convolutional neural network–gated recurrent unit (CNN-GRU) deep-learning model is employed to extract time-domain and frequency-domain features from the collected signals, enabling real-time identification of rotor vibration amplitude, frequency, and imbalance weight. Experimental results show that the MRCT provides stable output, a high signal-to-noise ratio, and an identification accuracy above 98% for predefined rotor imbalance-weight states under laboratory conditions. In addition, a shaft-misalignment-related abnormal vibration condition was examined on the motor platform. The corresponding time-domain and frequency-domain analyses show that the MRCT voltage signal exhibits distinguishable signal variations under normal and misalignment-related conditions, including spectral changes around the 2× rotational frequency. A laboratory-scale AIoT-oriented demonstration further verifies the feasibility of integrating MRCT signal acquisition, CNN-GRU inference, wireless transmission, and GUI-based visualization. It should be noted that the present work mainly focuses on imbalance-state recognition, while the misalignment-related experiment provides an additional sensor-response verification. Broader validation involving mechanical looseness, bearing defects, variable-speed operation, cross-machine testing, and long-term industrial conditions remains necessary. Full article

In this work, a self-powered vibration sensing system is proposed, based on a spatial magnetic field energy harvester, a duty-cycled circuit module, a piezoresistive graphene-based vibration sensor, and a wireless communication unit. The energy harvester is capable of generating an output power of 729 μW under a magnetic field excitation of 0.11 mT at 50 Hz. The duty-cycled circuit module enables closed-loop self-powered operation of the sensing system by efficient power storage and periodic measurement, and LoRa wireless transmission. The graphene-based sensor exhibits stable low-frequency vibration responses and good linearity and can capture composite vibration signals containing 4 Hz and 50 Hz components. These results indicate the potential of the proposed system for future transmission-line vibration sensing applications. Full article

This research designs a pico-hydro power system utilizing an Archimedes Screw turbine and an 18S16P Permanent Magnet Synchronous Generator (PMSG) for low-head efficiency. The primary focus is on optimizing real-time IoT-based monitoring via the Blynk application to replace inefficient manual observation. The methodology includes Infolytica MagNet simulations, manufacturing, and testing electrical parameters (voltage, current, power, frequency). Results indicate that power output increases linearly with Revolutions Per Minute (RPM), while the PZEM-004t sensor achieves high accuracy with voltage errors as low as 0.04–0.2%. This system successfully integrates permanent magnet technology and digital monitoring as a sustainable, measurable, renewable energy solution. Full article

Modern navigation, sensing, target-tracking, and identification applications require robust, simple, and noise-tolerant methods to reliably localize magnetic targets. Target localization is a non-linear inverse problem that typically requires highly sensitive sensors and dense measurements. Although modern, commercial high-sensitivity total-field magnetometers enable low-noise, compact arrays and very dense data sets, numerical inversion for a dipole’s location remains challenging and often provides non-unique solutions. This paper presents a detailed derivation of the novel closed-form solution for extracting the location of an unknown magnetic dipole target from data collected solely with an array of total-field magnetometers. The proposed solution relates the target location to magnetic data collected and array location, eliminating the need for other measured or a priori information such as local inclination or declination, sensor speed, or orientation. Simulations quantify numerical accuracy and demonstrate localization performance, and show that the localization performance remains reliable under a range of deployment formations, indicating that the approach is feasible for practical target localization scenarios. Full article

Reliable fault detection and diagnosis (FDD) plays a key role in inverter-driven permanent magnet synchronous motor (PMSM) systems, especially in applications where operational continuity cannot be compromised. In this work, a hybrid deep learning framework is developed by combining one-dimensional convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and a multi-head self-attention mechanism. The model targets multi-class fault classification in a three-phase PMSM inverter system. Its effectiveness is evaluated on a publicly available experimental dataset consisting of 10,892 multi-sensor samples collected under nine operating conditions, including normal operation, open-circuit faults, short-circuit faults, and half-bridge overheating scenarios. To avoid temporal data leakage, a block-aware chronological splitting strategy is applied. Model hyperparameters are determined through a validation process involving 24 different configurations. The proposed CNN–BiLSTM–Attention model achieves a macro F1-score of 0.9681 ± 0.0195, accuracy of 0.9810 ± 0.0102, Matthews correlation coefficient (MCC) of 0.9757 ± 0.0130, and ROC-AUC of 0.9996 ± 0.0003 over five independent runs, achieving the highest accuracy and MCC among all evaluated models; although the Random Forest baseline attains a marginally higher macro F1 score (0.9747) by operating on temporally aggregated features without temporal modelling, the proposed model provides superior discrimination across the full confusion matrix structure alongside end-to-end temporal interpretability via SHAP. Model interpretability is provided through SHAP (SHapley Additive exPlanations) GradientExplainer analysis, revealing that temperature-related features dominate fault discrimination, particularly for over-heating conditions, while current imbalance features are critical for distinguishing open- and short-circuit faults. Full article