Conventional energy management systems (EMSs) for islanded microgrids (iMGs) are increasingly challenged by the high penetration of renewable energy resources due to limited adaptability to multiobjective optimization and strong dependence on localized data. This article proposes a novel grayscale vision decision-based energy management agent (GVD-EMA) for multienergy iMGs integrating hydrogen energy and PV systems, enabling responsive decision-making and rapid deployment. Within this framework, an EfficientNet model is employed as the feature extraction component to learn optimal decision logic from the offline optimization repository, in which the temporal-spatial states of the iMG are described as grayscale images, enhancing the logical depth of the decision agent. Transfer learning (TL) is used to migrate the training process to virtual environments, thereby reducing dependence on large historical datasets. A defuzzification algorithm is designed to decode decisions into reliable control signals. The proposed GVD-EMA is compatible with most computer vision models, providing an innovative technical routing to interface AI models to energy optimization tasks. A comprehensive experimental validation demonstrates that the proposed GVD-EMA can stably interface with primary converter controllers and operate the energy flow for the iMG for more than 216 h. The results indicate that the GVD-EMA outperforms the mainstream online EMS approaches on system stability. Compared with theoretical optimum decisions, the proposed GVD-EMA can achieve 95.3% of the economic performance regarding hydrogen consumption.
Vernier machines with dual-sided magnets are impressive candidates for torque density improvement, due to their enhanced magnetic-gearing effect, i.e., the speed-reduction and torque-multiplication effect. However, they generally suffer from significant core magnetic saturation and, consequently, high core loss, which limits the achievable torque increase. In this article, a double-sided trapezoidal consequent-pole vernier machine (DTCPVM) with multicolumn stator-tooth magnets is proposed to overcome above drawbacks. The trapezoidal magnet poles on stator-rotor sides, rather than traditional curve-shaped magnets, are employed to reduce the core magnetic saturation. More importantly, the air-gap magnetic-field modulation effect and forward working harmonics can be further improved through the multicolumn trapezoidal PMs that are distributed and embedded within the DTCPVM stator teeth. In this case, higher torque output capability, lower core loss and higher power factor can be obtained simultaneously with the DTCPVM design concept. First, the structure characteristics and operating principles of the DTCPVM are presented. And an analytical model for the back-EMF harmonics of DTCPVMs with arbitrary numbers of stator slots, stator-tooth PMs, and rotor pole pairs is established based on the air-gap flux-modulation mechanism. Accordingly, a generalized stator-rotor matching theory is developed, and the influence of the column of stator-side trapezoidal PMs on the electromagnetic performance of the DTCPVM is investigated thoroughly. Then, a DTCPVM machine is designed utilizing a multi-objective optimization algorithm. Subsequently, to confirm its advantages, the electromagnetic performance of the proposed DTCPVM is compared to conventional designs with optimal structural parameters. Finally, a DTCPVM prototype is manufactured, and several experiments on output performance are conducted to validate the DTCPVM design concept and corresponding analytical and numerical results.
Wireless power transfer (WPT) is promising for autonomous charging of unmanned aerial vehicles (UAVs), but its practical deployment is constrained by landing misalignment and strict onboard space and weight limitations. This letter proposes a high-performance magnetic coupler integrating a V-shaped transmitter with a compact orthogonal hybrid receiver. The V-shaped transmitter reshapes the magnetic field into multidirectional flux components while providing a passive geometric-guidance interface designed to direct compatible landing gear toward the charging center and relax the required Y-axis landing accuracy. The compact orthogonal receiver enhances multidirectional flux interception within limited onboard space. An 85-kHz prototype delivers 1034 W with a peak DC–DC efficiency of 92.5%, demonstrating superior power-transfer performance compared with existing UAV WPT systems. The system maintains stable transfer efficiency over the tested X-axis offset range of 0–100 mm. Based on the combined mass of its coils and magnetic cores, the receiver magnetic coupler achieves a gravimetric power density of 2.09 kW/kg. These results highlight the advantages of the proposed design in magnetic-field shaping, kilowatt-level high-efficiency power transfer, and receiver-side gravimetric performance.
Resonant drive is a pivotal technology for enhancing the power density of piezoelectric actuators. However, its implementation in piezoelectric electro-hydraulic actuator (P-EHA)—a promising solution for aerospace and industrial actuation—remains challenging. This article first elucidates the fundamental mechanism responsible for this limitation: the volumetric variation of the cylinder during motion significantly alters the acoustic impedance distribution of the system, leading to severe detuning. Based on this discovery, we propose a novel impedance decoupling strategy by introducing a massive acoustic compliance into the hydraulic circuit. This architecture physically isolates the variable impedance of the cylinder from the main system. Comprehensive acoustic modeling, structural design, finite element simulations, and experimental validations are presented. Experimental results demonstrate that this strategy successfully maintains resonance across the full stroke range, achieving a velocity 1.67 times and an output power 2.75 times that of the conventional design under 400 $\text{V}_{\text{p-p}}$. Ultimately, the prototype achieves a record-breaking output power of 35.7 W under 800 $\text{V}_{\text{p-p}}$ at 1300 Hz. By unveiling the root cause of resonance failure and providing a compact solution, this work establishes a new design paradigm for high-power P-EHAs.
This article focuses on a commutation error auto-compensation method for a position sensorless brushless DC motor, which is implemented via DC-link current reconstruction. First, the correlation between commutation error and DC-link current is analyzed. Based on the correlation, a reconstruction circuit is designed to divide the DC-link current into two independent currents, which are reconstructed to indicate commutation error. Then, the DC component of the reconstructed current signal, being positively correlated with the commutation error, is obtained using a low pass filter. It is utilized as the feedback quantity for a closed-loop compensator, which generates and automatically regulates the required compensation angle for the commutation error based on a proportional-integral (PI) algorithm. Finally, the proposed method is verified on a magnetically suspended control moment gyro prototype.
The zero sequence circulating current (ZSCC), comprising both low-frequency (LFCC) and high-frequency (HFCC) components, significantly degrades the efficiency and reliability of grid-connected parallel inverter systems. However, constrained by fixed switching sequences, conventional communication-free methods fail to simultaneously suppress such multifrequency disturbances. To address this issue, this article proposes a communication-free switching sequence reorganization-based model predictive circulating current suppression (SSR-MPCCS) strategy. First, the generation mechanisms of ZSCC are analyzed mathematically, revealing that LFCC stems from the monotonic accumulation induced by the consistent polarity of average common-mode voltage (CMV) differences over multiple switching cycles, whereas HFCC originates from instantaneous CMV differences within a switching period. Second, to suppress LFCC, a dynamic sequence reorganization scheme is proposed. By reconfiguring the vector arrangement according to the ZSCC polarity, the monotonic accumulation trend is effectively disrupted. Finally, for HFCC suppression, a duty cycle redistribution scheme is proposed. This scheme dynamically adjusts the duration of zero vectors u 0 and u 7 based on estimated maximum fluctuations, thereby preventing spikes triggered by excessive instantaneous CMV differences. The effectiveness of the proposed method is demonstrated by the experimental results.
For electromagnetic levitation (EML) systems, precise suspension gap control is challenged by electromagnetic noise, unmodeled nonlinearities, and limited onboard computing resources. To address these challenges, this article proposes a direct data-driven predictive control framework with an extended state observer (ESO). Specifically, the control framework bypasses system identification by directly exploiting input–output data, while the ESO actively estimates and compensates for the effect of unmodeled dynamics, parameter uncertainties, and external disturbances in real time to enhance robustness. The proposed method requires no parametric model and is well suited to hardware-constrained platforms. Hardware experiments on a physical EML system demonstrate that the proposed control method ensures safe, high-precision gap regulation, and satisfies a demanding control bandwidth.
Accurate motor parameter identification is critical for improving the dynamic performance of induction motors (IMs). To achieve precise identification of the complete set of electrical parameters without load decoupling, this article proposes a static signal injection-based parameter identification method for IMs. Compared with conventional schemes, the proposed method adopts a sawtooth-wave excitation signal, which simplifies the process of signal superposition. For solving the identification equations, an enhanced secretary bird optimization algorithm (SBOA) is developed, incorporating Singer chaotic mapping, a random migration strategy, and an adaptive step-size strategy. These improvements substantially enhance the algorithm’s performance, leading to higher identification precision and faster convergence compared with traditional intelligent algorithms. Furthermore, a sparse sampling identification framework is established to overcome the drawbacks of conventional intelligent algorithms, such as high computational complexity, long processing time, and difficulty in deployment on embedded platforms. The proposed method is implemented on a motor control chip and experimentally validated using the Texas Instruments (TI) TMDSHVMTRPFCKIT motor control platform. Experimental results confirm that the method achieves high identification precision and exhibits strong practical feasibility, demonstrating its broad application potential in motor control systems.
Accurate current sharing and voltage regulation are fundamental control objectives in DC microgrids. Conventional droop control can achieve current sharing; however, this is typically realized at the expense of steady-state DC bus voltage deviations. While secondary control strategies can compensate for these limitations, existing centralized and distributed approaches generally rely on communication networks for information exchange, which may not be available or reliable in practical DC microgrids. To address this challenge, this article proposes a novel decentralized control strategy that simultaneously achieves desired current sharing and voltage restoration. By eliminating communication requirements, the proposed strategy is simpler and easier to implement in practical DC microgrids. Experimental results validate the effectiveness of the proposed approach.
This article presents a cost-effective approach for wireless power transfer (WPT) systems, enabling wide-range power regulation and zero-voltage switching (ZVS) under varying load conditions. Despite extensive research on achieving wide-range ZVS, current techniques, such as variable compensation and active rectification, typically increase hardware complexity. At the same time, frequency-tuning methods are prone to the bifurcation phenomenon. To this end, this article introduces an optimal detuning design (ODD) methodology to enable ZVS. Furthermore, a hybrid mode control (HMC) strategy is incorporated to facilitate ZVS while minimizing the detuning degree. By strategically detuning circuit parameters and adjusting inverter modes, the proposed approach achieves wide-range power regulation and ZVS without the need for additional circuits, frequency tuning, and active rectification. Compared with existing solutions, this method lowers hardware costs and enables lightweight receiver designs, making it particularly suitable for cost-effective WPT systems. To demonstrate the effectiveness of the proposed approach, a WPT prototype was constructed. Experimental results confirm that the proposed approach effectively ensures full-range ZVS, maintaining DC–DC efficiency between 93.96% and 95.32% from 20% to 100% rated power and sustaining 90.89% efficiency even at 10% rated power.
To enable optical focusing in strong magnetic-field environments, a magnetically compatible piezoelectric optical focusing mechanism (POFM) with dual driving modes has been proposed. The POFM is driven by an inverted Y-shaped piezoelectric stator. By coupling two out-of-plane bending modes, the stator generates elliptical motion at its driving foot. Coarse focusing motion (CFM) is achieved under continuous excitation signals, while fine focusing motion (FFM) is realized under pulsed excitation. The structure of the POFM is designed, and the working principle of the piezoelectric stator is analyzed. Finite element analysis is conducted to optimize the stator’s resonant frequencies of the two bending modes. The prototypes of the piezoelectric stator and the POFM are fabricated for performance evaluation. Experimental results demonstrate that under CFM, the POFM achieves a speed of 37.2 mm/s, a thrust of 6.1 N, and response times of 3.25 ms (startup) and 2.85 ms (stop), enabling coarse focusing. Under FFM, a minimum step resolution of 0.55 $\boldsymbol{\mu}$ m is obtained for fine focusing. The POFM maintains stable output performance under a 1.5 T magnetic field without noticeable magnetic interference. This study offers valuable guidance in the design of piezoelectrically driven optical focusing systems for high-magnetic-field applications.
Partial shading reduces energy harvest in photovoltaic (PV) systems by creating multiple peaks on the power-voltage (P-V) curve. Conventional maximum power point tracking (MPPT) methods perform a full curve scan to find the global maximum power point (GMPP), resulting in slow convergence and high computational cost. This work proposes a novel algorithm that exploits the predictable patterns of peak formation under varying irradiance and temperature. The mathematical model uses a strategic three-test-point mechanism, with consideration for temperature extremes, to define voltage variation boundaries. At a defined boundary, the simultaneous string current comparison technique is also introduced to significantly reduce scan points under non-severe shading. By combining temperature-dependent boundary identification with current comparison, the algorithm efficiently narrows the search to a critical fraction of the open-circuit voltage ( $\boldsymbol{V}_{\boldsymbol{oc}}$ ) range affected by the shaded cell. This enables rapid computation of a highly accurate tentative GMPP tracking. A final, minimized local search then confirms the true global peak, ensuring fast convergence within 1 s and a tracking efficiency exceeding 99%. The algorithm’s efficacy is validated by hardware implementation using a buck-boost converter, demonstrating robust performance across diverse irradiation and temperature conditions.
This work investigates tracking performance of output voltage under set-point transitions in single inductor dual output (SIDO) buck/boost converters, where the primary control challenge lies in mitigating cross-regulation across the output voltages. It also addresses disturbance rejection arising from input-voltage fluctuations and load variations. Active disturbance rejection control (ADRC) employing a linear extended state observer (LESO) offers a simple yet robust paradigm for control of uncertain systems under diverse disturbances. This work presents an equivalent proportional-integral (PI) controller derived from the ADRC framework to suppress cross-regulation and enhance the dynamic performance of SIDO converters. In addition, a maximum sensitivity constrained region (MSCR)-based graphical approach, termed graphical linear ADRC (GLADRC), is proposed for systematic tuning of ADRC parameters, simultaneously accounting for the control-to-output dynamics of both channels in SIDO converters while disregarding the inter-channel control-to-coupling paths. Stability of the designed control strategy is shown using Lyapunov theory. Effectiveness and scalability of the proposed method are demonstrated through comprehensive experimental validation on SIDO buck and SIDO boost converter prototypes with rated powers of 3.3 W and 10.45 W, respectively, using a Texas Instruments C2000 F28379D microcontroller. The experimental results are benchmarked against recent works under reference tracking and disturbance rejection scenarios.
Solid-state transformer (SST) technology research has gained popularity due to advancements in power electronics. This article focuses on two major concerns in SST: Semiconductor losses and power quality together at medium voltage (MV) stage of SST. It proposes an integrated closed-loop control architecture for three-stage (MV/DC–DC/LV) SST with a hybrid control technique for the MV stage. It offers better performance than existing solutions in terms of reduced switching losses ( $\boldsymbol{\approx}$ 50 $\boldsymbol{\%}$ ), current total harmonic distortion (THD) ( $\boldsymbol{\approx}$ 2 $\boldsymbol{\%}$ ), and control simplicity at MV stage. The proposed hybrid modulation offers a cost effective solution to improve the semiconductor losses and power quality by allowing the use of Si-insulated gate bipolar transistors (IGBTs) and SiC MOSFETs at MV stage. The MV stage of the SST is realized using Si-IGBT-based multiple cascaded H-bridge (CHB) cells terminated with a SiC-based two-level voltage source inverter (2L-VSI). The CHB structure is operated in current or voltage control mode and switched at grid frequency, resulting in negligible switching frequency losses. On the other hand, the 2L-VSI is switched using hysteresis current control mode, ensuring sinusoidal MV grid currents even at light loads and offers a high effective switching frequency. To avoid unequal power sharing among the cells, a pulse width modulation (PWM) circulation logic is also proposed. The proposed scheme is experimentally validated using a developed downscale 600(MVAC)/200V low voltage DC (LVDC)/100V(LVAC), 5 kW SST prototype setup.
Saturated-core fault current limiters (SCFCLs) can effectively suppress the rise rate of fault currents in high-voltage flexible DC systems. However, their relatively large fault impedance tends to slow down the fault current decay process, which is detrimental to fault clearing. To address this issue, this article proposes a novel FCL with impedance adaptive variation (IAFCL). By means of autonomous topological transformation, the proposed IAFCL reduces the equivalent inductance and diverts part of the fault current into internal circulating paths to accelerate current decay. The whole process is achieved adaptively without any external detection or triggering devices. First, the topology and principle of the IAFCL are introduced. Next, the electromagnetic parameter design requirements of IAFCL are derived through mathematical analysis. Then, working feasibility is verified by combining finite element magnetic circuit simulations with system circuit simulations. Finally, a prototype is built for validation tests. The results demonstrate that proposed IAFCL significantly enhances the current decay speed while maintaining stable current limiting performance.
Dynamic reliability assessment of collaborative robot servo motor systems is challenging under nonstationary operating conditions and limited degradation data. The main difficulties arise from the need to unify heterogeneous degradation processes and to ensure robust state propagation under time variation and sparse transition statistics. This article proposes a dynamic reliability assessment method based on a multistate time-varying transition framework. First, multitemperature accelerated degradation data are used to construct a unified discrete state space with consistent health semantics. Second, a hierarchical Dirichlet shrinkage scheme is introduced to obtain robust one-step transition matrices for different temperature groups. Third, a time-varying propagation kernel driven by operational temperature history is established for recursive updating of component state probabilities and system-level dynamic reliability. Experimental validation shows that the proposed method improves sample-level discrimination and reduces monotonicity violations, with an estimated mean time to failure (MTTF) closer to the product-level reference value than representative comparison methods.
Traditional hierarchical planning-control pipelines often generate trajectories incompatible with the vehicle’s tracking capabilities, forcing either aggressive corrections or unduly conservative driving. To eliminate this mismatch, this article introduces a novel integrated planning and control framework. The strategy refines trajectory generation via control error feedback, balancing the trajectory with vehicle tracking capabilities to enhance safety and efficiency. Specifically, the planning module incorporates fuzzy logic into the model predictive control structure, leveraging control error feedback for curvature and velocity regulation. A refined artificial potential field is also formulated for dynamic obstacle avoidance and lane keeping, effectively preventing aggressive maneuvers in multilane scenarios. Simultaneously, the synthesis of a sliding mode controller using the supertwisting algorithm and an extended state observer achieves robust trajectory tracking while mitigating chattering effects. Both simulation and field experiments corroborate the effectiveness and robustness of this approach.
Existing studies on the armature magnetic field (AMF) harmonic characteristics (HCs) of novel unipolar winding (UW) motors remain fragmented. Crucially, owing to the structural constraints of the classical winding function (WF), a systematic analytical framework capable of uniformly analyzing both UWs and traditional bipolar windings (BWs) is absent. To address these gaps, this article first proposes a unified analytical framework. Establishing the polarized conductor distribution magnetic potential function (PCDMPF) as the fundamental decoupled starting point, this framework effectively overcomes the zero-net-ampere-turn constraints of classical WF-based frameworks, enabling the unified analysis of novel UWs and traditional BWs. Furthermore, it introduces unified winding factors (UWFs), a unified harmonic current function (UHCF), and a unified modulation operator (UMO) as analytical bridges, achieving broader topological universality. Second, based entirely on this framework, a unified analytical model (AM) accommodating diverse UW/BW, arbitrary current, and flexible stator/rotor topologies is established for the primitive magnetomotive force (MMF) and flux density HCs. The results are validated against finite-element simulations, showing fundamental amplitude errors below 5%. Finally, dual-rotor axial-flux motor prototypes with three-phase concentrated UWs and BWs are fabricated and tested, confirming the accuracy of the proposed unified AM.
To address the scarcity of fault samples in ship electric propulsion systems (SEPSs), which limits the diagnostic accuracy of fault diagnosis models, a data augmentation method based on a dual-discriminator adversarially guided conditional diffusion model (DD-AGCDM) is proposed in this article. First, utilizing the denoising diffusion probabilistic model (DDPM) as the generative backbone, a joint conditional embedding mechanism is designed to fuse discrete fault category labels with continuous deep feature vectors, enabling the model to generate samples possessing both correct fault patterns and rich intraclass diversity. Second, a dual-discriminator adversarial supervision architecture, comprising a fidelity discriminator and a fault pattern discriminator, is introduced to impose multidimensional gradient guidance on the diffusion reverse process. This enables the synthesized signals to achieve high fidelity in both microscopic textures and macroscopic semantic distributions. Furthermore, an adaptive two-stage noise scheduling strategy is proposed to dynamically adjust the learning focus between structural recovery and detail refinement, thereby accelerating convergence and enhancing generation quality. Experimental results show that the samples generated by DD-AGCDM can better approximate the manifold of real fault data and improve the downstream diagnostic accuracy to 98.2% under extremely imbalanced scenarios.
