Search Results (1,047)

Due to the higher power density of silicon carbide (SiC) inverters in electric vehicles (EVs), effectively managing conducted electromagnetic interference (EMI) has become a vital aspect of inverter design. The complex power topology of SiC inverters increases the complexity of different types, phenomena, and mechanisms of conducted EMI, making the selection of appropriate suppression methods more challenging. Many studies have examined the mechanisms of conducted EMI and their suppression techniques. However, the fast switching transients of SiC devices can affect an automotive traction inverter at multiple physical levels, ranging from the gate-drive circuit and isolation interface to the external power terminals. These phenomena are closely related through their common switching excitation and parasitic coupling networks, but they should not all be interpreted as equivalent conducted-emission phenomena. To provide a structured engineering perspective, this review organizes the relevant disturbances using a source–path–victim framework and examines three representative paths: gate-loop crosstalk, common-mode (CM) coupling across the isolated gate-drive interface, and system-level CM/DM-conducted emissions. The corresponding mitigation techniques and their applicability to EV traction inverters are subsequently reviewed. Full article

To improve the dc-bus voltage utilization while reducing the commutation burden of an insulated gate bipolar transistor (IGBT)-based open-end winding permanent magnet synchronous motor (OEW-PMSM) drive, this paper proposes an improved alternate sub-hexagonal center PWM (ASHCPWM) strategy. The drive employs two three-phase IGBT inverters sharing a common dc bus. Since the turn-off energy of an IGBT is strongly affected by its switching frequency, ASHCPWM alternately clamps one inverter and reduces the number of switching transitions. The resulting common-mode voltage mismatch, together with the dead-time voltage error required to prevent shoot-through in the IGBT bridge legs, nevertheless produces significant zero-sequence current and additional semiconductor current stress. A mathematical model of the zero-sequence voltage is therefore established, and a common-mode voltage compensation method is combined with an improved Kalman-filter-based harmonic extraction and closed-loop suppression strategy. The switching performance of the modulation is evaluated by counting the switching transitions of the twelve IGBTs and by comparing the zero-sequence current and phase-current spectra. Experiments on a 10 kHz prototype demonstrate that the proposed method substantially reduces zero-sequence harmonics while retaining the reduced-switching characteristic of the original ASHCPWM. Full article

Complete coverage path planning for sowing machinery is a challenging task since sown areas should not be repeatedly traversed, which is a major difference with the planning of cleaning robots. This paper proposes an improved biological neural network (BNN) approach considering an operational mode switching mechanism between sowing and non-sowing movement. Based on surrounding environmental conditions, the next node state is classified as sowing, closed, or transfer node. Guided by the BNN landscape, the machine continues the sowing operation along parallel straight paths at sowing nodes. Once a closed node is detected, the machine switches to non-sowing mode and searches the potentially closed area using a depth-first search algorithm. Then, the machine moves to a new target node along the shortest non-sowing path and restarts sowing. This avoids the sown-path-induced enclosure (SPIE) problem. When a transfer node is detected, the machine also switches to non-sowing mode, then searches and travels to a reasonable new target node to restart sowing. This improves the rationality of the sowing path. Simulations show that the proposed method achieves complete coverage of sowing operations while avoiding repeated traversal of sown areas. Experiments on a differential drive crawler chassis platform verify the feasibility of the proposed method. Full article

As unmanned aerial vehicle (UAV) missions expand from aerial inspection and environmental sensing to physical interaction and autonomous manipulation, rotary-wing UAVs equipped with flexible manipulators offer a promising platform for contact-rich operations in complex environments. Among these tasks, stable near-ground hovering and the grasping of ground targets are particularly challenging because they involve ground-effect aerodynamics, rigid–flexible coupling, and mode transitions caused by contact and load transfer. This review provides a structured, task-oriented critical synthesis of advances in this interdisciplinary field. First, system configurations are classified by aerial-platform architecture, manipulator type, mounting arrangement, and end-effector design, and the suitability of rigid-link, compliant, continuum, and soft manipulation mechanisms for near-ground grasping is assessed. Next, modeling approaches for rotor ground effect, coupled rigid–flexible dynamics, hybrid contact and load-transfer dynamics, model identification, and model reduction are reviewed. Trajectory planning, coordinated stabilization, impedance control, hybrid force/position control, and switching control are then compared across free flight, contact establishment, and payload-carrying hover. Although the reviewed literature provides a substantial theoretical foundation for aerial manipulation, continuum robotics, and multirotor ground effect, direct evidence remains limited for methods that jointly address near-ground aerodynamics, large flexible deformation, and contact-induced load transfer across the complete near-ground grasping sequence with integrated experimental validation. Based on these evidence gaps, this review identifies multiphysics reduced-order modeling and event-driven hybrid control as author-synthesized directions for future investigation. Full article

The rapid development of artificial intelligence (AI) has created new opportunities for improving the performance, robustness, and efficiency of electrical machine drive systems. Among AI-based approaches, fuzzy logic control (FLC) has attracted considerable attention because of its ability to handle nonlinear dynamics, parameter uncertainties, and external disturbances without relying on an accurate mathematical model. This review systematically examines FLC-based control strategies for electrical machine drives, with particular emphasis on induction motors, switched reluctance motors, permanent-magnet synchronous motors, synchronous reluctance motors, and brushless DC motors. The review follows the PRISMA 2020 framework, and the selected studies are analyzed according to machine type, FLC architecture, control strategy, optimization method, implementation platform, and validation approach. The reviewed evidence indicates that FLC-based strategies can improve dynamic response, tracking accuracy, robustness, and torque regulation under the specific conditions reported in the literature. Hybrid approaches combining FLC with field-oriented control, direct torque control, sliding-mode control, model predictive control, neural networks, ANFIS, and optimization algorithms provide additional opportunities for adaptation and parameter tuning. However, the reported performance is strongly dependent on machine topology, controller architecture, tuning methodology, computational requirements, and validation platform. The review also identifies important limitations, including the lack of standardized benchmarking, computational complexity, dependence on expert knowledge, and limited HIL and experimental validation of several advanced approaches. Emerging directions include Type-2 and higher-order fuzzy systems, neuro-fuzzy and hybrid AI controllers, data-driven optimization, digital-twin-assisted control, edge computing, and hardware-oriented implementation. The objective of this review is to provide a structured and critical synthesis of the existing evidence, clarify the evolution and practical applicability of AI-driven FLC approaches, and identify research priorities for reliable, computationally efficient, and experimentally validated electrical machine control. Full article

Fuel cell systems require high-efficiency DC–DC interfaces capable of regulating rapid voltage variations while respecting the operational constraints of proton-exchange membrane fuel cells (PEMFCs). The floating interleaved boost converter (FIBC) is a strong candidate for this purpose due to its reduced current ripple, improved power sharing, and lower component stress. The design of control strategies for FIBCs supplied by PEMFCs remains challenging because explicitly enforcing fuel cell operational constraints under fast converter dynamics is inherently difficult, particularly when detailed fuel cell models are unavailable or undesirable, as reflected in existing approaches such as classical linear regulators and single-model predictive schemes. Therefore, this paper proposes a multi-model finite control set model-based predictive control (MM-FCS-MPC) strategy for FIBC converters supplied by PEMFCs. The method employs multiple discrete prediction models with cost functions defined by the converter switching mode, integrates a fuel cell-aware reference-generation mechanism to ensure nominal and safe PEMFC operation by enforcing current and power constraints within the predictive framework, and enables fast, accurate output-voltage regulation. Detailed modelling of the FIBC, component sizing, and PEMFC characteristics is provided. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance, with a rise time of approximately 4.4 ms, a $\pm 2\%$ settling time of 10.3 ms, a maximum overshoot of only 0.056%, and a phase delay of approximately 4.26°, compared with 9.6° for the conventional PI voltage-tracking baseline. Full article

Retrofitting an internal combustion engine (ICE) dynamometer test bench for electric motor testing introduces significant measurement and control challenges. The two modes differ fundamentally in torque dynamics, communication protocols, and energy flow characteristics. This study presents a dual-mode measurement and control system for a retrofitted 250 kW test bench. The system retains ICE testing capability while adding motor testing through a shared LabVIEW interface. Three key technical problems are addressed: (i) a cross-mode PID controller with gain scheduling and bumpless mode switching; (ii) a wavelet-based dual-sensor torque fusion method using complementary filtering at a 50 Hz crossover frequency; and (iii) active front end (AFE) energy feedback optimization with quadrant-dependent DC bus voltage setpoints. Validation was performed on an 80 kW (240 kW peak) interior permanent magnet synchronous motor (IPMSM) across 823 operating points. The system achieved a peak system efficiency of 94.88%. Energy recovery efficiency improved from 58% to 82% through AFE parameter optimization. The proposed methodology offers a cost-effective template for extending existing ICE test infrastructure to support electric vehicle powertrain development. Full article

Cooperative interception by multiple aircraft requires coordination of physical event times and terminal line-of-sight (LOS) directions under strongly coupled three-dimensional motion. A limitation of predictor-based designs is that agreement of estimated terminal times is often interpreted as simultaneous interception without an explicit predictor-to-event error bound. This paper develops a distributed mixed-power fixed-time guidance framework that comprises point-mass kinematics and ideal inner-loop tracking. An exact spherical-coordinate model retains the azimuth–elevation coupling terms. The radial predicted terminal time (RPTT) is used as the distributed coordination state, and a local bound links RPTT to independently detected interception events. A mixed-power consensus protocol is analyzed for a fixed graph and a finite set of connected undirected switching graphs using a common Lyapunov function. An inertial-frame fixed-time observer estimates the unmeasured target acceleration, while a C²-regularized nonsingular fast terminal sliding-mode law regulates the terminal LOS azimuth and elevation. The analysis distinguishes nominal exact fixed-time consensus, perturbed practical fixed-time reachability, and exponential convergence inside the regularization layer. Four-aircraft simulations produced a 0.435 ms independent event-time spread and terminal LOS errors below 0.046°. Relative to a peak-matched single-power protocol, the RPTT agreement time was reduced by 34.3%, with a 0.58% increase in total control effort. The results support the method’s use as a guidance-layer coordination design within the stated sensing, communication, and inner-loop assumptions. Full article

High-voltage disconnect switches (HVDSs) are crucial for ensuring visible electrical isolation but often operate under challenging environmental conditions. While current monitoring methods can detect faults, simply identifying them is not enough for effective maintenance planning or efficient resource management. Electric utilities need tools or platforms that enable them to identify various failure modes and their impact on the safety and reliability of the system. This paper presents a failure mode and effects analysis of a HVDS and an assessment of its criticality (FMECA). For each failure mode identified by the FMECA, a cause-and-effect analysis is used to further investigate their root causes. Next, the criticality of each failure mode is evaluated using the Risk Priority Number (RPN). Finally, each failure mode is subjected to an analysis to prioritize the sensor used to measure the physical quantity associated with the failure. This sensor prioritization is performed by considering not only the RPN but also the sensor’s utility in terms of failure detection efficiency and ease of installation. The results provide a robust foundation for the development of predictive maintenance strategies customized for HVDS equipment, thereby contributing to enhanced reliability and resilience of electrical power systems. Full article

With the high penetration of renewable energy into power grids, grid-connected converters face severe challenges in adapting to wide-range variations of grid strength and suppressing fault overcurrents. A single grid-following (GFL) or grid-forming (GFM) control mode cannot ensure system stability across weak, moderately weak, and strong grid conditions, while the lack of current limiting measures may lead to damage to power electronic devices. To address these issues, an adaptive seamless switching strategy (ASSS) considering current limiting is proposed in this paper. The ASSS integrates three core modules: harmonic injection-based impedance identification for short-circuit ratio (SCR) calculation, a state variable reset method for seamless mode switching, and a hysteresis switching criterion to avoid frequent mode transitions. Besides ASSS, an adaptive virtual impedance is employed for fault current suppression. Based on MATLAB/Simulink, simulation verifications are conducted under typical working conditions including grid voltage sags and large-scale SCR variations. The results show that the proposed strategy can limit the maximum fault current efficiently, realize seamless switching between GFL and GFM modes with waveform distortion rate less than 5%, and ensure stable system operation across the entire range of grid strength variations. This strategy effectively improves the fault ride-through capability, grid adaptability, and switching dynamic stability of grid-connected converters, providing a reliable control solution for high-penetration renewable energy integration. Full article

Bearing damage caused by electric discharge machining (EDM) is a concern in electric ship propulsion drives, particularly during low-speed operations such as maneuvering and slow steaming. In a modular multilevel converter (MMC) operated with nearest-level modulation (NLM), rounding of the three-phase submodule insertion numbers produces a residual imbalance that appears as common-mode voltage (CMV) and charges the bearing film capacitance. The peak bearing voltage rises from 6.4 V at 60 Hz to 20.0 V at 10 Hz, while the thinning lubricant film lowers the dielectric breakdown threshold. Always-on CMV reduction approaches apply a corrected switching candidate in every control period, including intervals where the bearing voltage stays well below the threshold. This paper proposes a selective NLM correction driven by predicted bearing voltage risk: a reduced-order RC model predicts the bearing voltage the conventional NLM candidate would produce, and a hysteretic controller applies a zero-CMV candidate only when that prediction approaches the insulation threshold. Using a worst-case discharge criterion and thresholds of 5.9–29 V derived from elastohydrodynamic film thickness estimates, simulations at 10 Hz show that the method eliminates EDM events over the full evaluated threshold range. It achieves the same zero-EDM outcome as always-on correction while reducing the correction mode activation ratio from 100% to at most 30.8%, and remains inactive where conventional NLM is already safe. Full article

Background: Imperatorin (IPT) is a natural furanocoumarin featuring robust anti-inflammatory, antifibrotic and antioxidant activities. However, poor aqueous solubility and insufficient oral bioavailability restrict its clinical application. Multiple gastrointestinal barriers, including the mucus barrier, limited epithelial penetration and P-glycoprotein-triggered drug efflux, are major obstacles hindering IPT oral absorption. Methods: N-acetylcysteine (NAC)-functionalized TPGS conjugates were synthesized first. Using Pluronic^® F108 and Lipoid^® S-100 as a matrix, imperatorin@N-acetylcysteine-TPGS/Pluronic^® F108/Lipoid^® S-100 (IPT@NAC-TFS) micelles were fabricated. We characterized their physicochemical features and in vitro release behavior. The Caco-2/HT29-MTX-E12 co-culture cell model was adopted to explore mucus permeation, cellular uptake and transepithelial transport mechanisms. In vivo intestinal distribution and pharmacokinetic tests in rats were carried out to confirm the oral absorption-enhancing effect of micelles. Results: Optimized micelles displayed a uniform shape and favorable encapsulation efficiency. Low CMC maintained structural stability upon gastrointestinal dilution. NAC modification conferred mucus-penetrating capacity on micelles. TPGS simultaneously improved epithelial barrier permeability and inhibited drug efflux, switching IPT transport mode. The micelles effectively cleared intracellular ROS, recovered SOD activity and lowered MDA levels in BLM-impaired MLg fibroblasts. In vivo results revealed enhanced intestinal drug accumulation, with the relative oral bioavailability of IPT increased by 6.07-fold. Conclusions: IPT@NAC-TFS micelles overcome multiple gastrointestinal barriers for oral IPT delivery. Combining mucus penetration, efflux suppression and antioxidative capacity, this system offers a promising strategy to develop oral formulations of poorly soluble antifibrotic natural products. Full article

Distributed-drive electric vehicles (DDEVs) enable independent wheel-torque control, providing flexibility to improve energy efficiency and vehicle stability. However, tire–road adhesion, motor and battery capabilities, and actuator availability constrain these objectives, which may conflict under low-adhesion conditions, high-power acceleration, emergency braking, and combined longitudinal–lateral maneuvers. This paper provides a structured review of coordinated torque-allocation strategies for balancing energy efficiency and stability in DDEVs. Existing research is examined in terms of regenerative braking, tire-slip energy-loss reduction, and stability control under longitudinal, yaw, and combined conditions. Control approaches are classified as rule-based, stability-region-based, mode-switching, multi-objective optimization and predictive control, state-adaptive dynamic-priority coordination, and learning-based safety-hybrid methods. These approaches differ in real-time performance, constraint handling, adaptability, interpretability, and engineering maturity. A hierarchical hybrid architecture integrating rule-based supervision, state assessment, constraint-aware optimization, and learning-based enhancement appears more suitable for practical deployment than a single algorithm or fixed-weighting scheme. Key challenges include dynamic stability-boundary estimation, safety-assured coordination, multi-actuator fault tolerance, real-time implementation, and standardized vehicle-level validation. This review provides guidance for coordinated control-system development and future research on DDEVs. Full article

Partial shading conditions (PSCs) in photovoltaic (PV) systems generate multiple local maximum power points (LMPPs) and a single global maximum power point (GMPP) in the power–voltage (P–V) characteristics, challenging conventional maximum power point tracking (MPPT) methods. This study presents an FPGA-in-the-Loop (FIL) co-simulation of a Systematic-Sequencing Adaptive Particle Swarm Optimization (SS-APSO) algorithm for MPPT under dynamically varying shading conditions. The proposed method combines deterministic particle initialization, adaptive particle reordering, and switching among wide exploration, re-exploration and exploitation modes to enhance global search capability. The controller is implemented on a Xilinx Artix-7 FPGA using fixed-point arithmetic and a finite-state-machine architecture in VHDL and is evaluated through MATLAB/Simulink–FIL co-simulation for two PV configurations: four series-connected modules (4S) and two parallel-connected strings of two series modules (2S2P). The results demonstrate tracking efficiencies generally exceeding 98% under different shading within 0.181 s for both configurations, while in FIL co-simulation, it reaches the GMPP within 0.203. The close agreement between simulation and FIL co-simulation results demonstrates the effectiveness of the proposed SS-APSO-MPPT controller for PV systems. Full article

This paper explores methods to eliminate high-voltage spikes during dead time ($t_{dt}$) in high-frequency inverters for Wireless Power Transfer (WPT) systems, focusing on the transition from traditional Silicon IGBTs to enhancement-mode Gallium Nitride (GaN) HEMTs. At elevated switching frequencies, dead-time parameters strongly govern system efficiency and signal integrity. While IGBTs suffer from reverse-recovery charge ($Q_{rr}$) in antiparallel freewheeling diodes that generates severe voltage spikes, hard-switching GaN systems require precise dead-time minimization to prevent shoot-through while limiting third-quadrant conduction losses. Unlike prior WPT studies bounded by specific hardware setups, this paper presents a baseline technology benchmark that explicitly decouples intrinsic semiconductor commutation physics, specifically $Q_{rr}=0$ versus third-quadrant conduction, from macro-system parameters ($f_{sw}$, power level, and resonant topology). Experimental evaluation of a 130 kHz L-S-tuned GaN full-bridge inverter confirms that primary current commutates via third-quadrant conduction during dead time, completely eliminating reverse-recovery voltage spikes ($I_{rr}=0$). Ultimately, this work demonstrates that GaN’s spike-free operation is an intrinsic device-level property, reframing the dead-time optimization objective from transient overvoltage suppression to third-quadrant conduction loss minimization in next-generation WPT systems. Full article