Search Results (564)

Open-winding permanent-magnet synchronous motors feature flexible control and a high fault-tolerance capability, making them widely used in high-reliability and high-power scenarios such as military equipment and electric locomotives. To address the issues that traditional model predictive control fails to balance, such as zero-sequence current suppression, system loss optimization and the reliance of weight parameter design on experience (with online optimization consuming excessive resources), this paper proposes an OW-PMSM MPC strategy for loss optimization and a weight design method based on a residual neural network. Specifically, the former strategy adds a zero-sequence current suppression term and a loss quantification term to the MPC cost function, enabling coordinated control of the two objectives; the latter establishes a mapping between weight parameters and motor performance via ResNet (which avoids the gradient vanishing problem in deep networks) and outputs optimal weight parameters offline to save online computing resources. Comparative experiments under two operating conditions show that the improved MPC strategy reduces system loss by 25%, while the ResNet-based weight design improves the performance of the drive system by 30%, fully verifying the effectiveness of the proposed methods. Full article

Advancements in ship propulsion technologies are essential for improving the efficiency and reliability of maritime transportation. This study introduces a comprehensive approach that integrates motor design with sensorless control strategies, specifically focusing on Dual Three-Phase Permanent Magnet Vernier Motors (DTP-PMVM) for ship propulsion. The initial section of the paper explores the design of a 5-MW DTP-PMVM using finite element method (FEM) analysis in dual three-phase configurations. The subsequent section presents a novel sensorless control technique employing a Prescribed-time Sliding Mode Observer (PTSMO) for accurate speed and position estimation of the DTP-PMSM, eliminating the need for physical sensors. The proposed observer convergence time is entirely independent of the initial estimation guess and observer gains, allowing for pre-adjustment of the estimation error settling time. Initially, the observer is designed for a DTP-PMVM with fully known model parameters. It is then adapted to accommodate variations and unknown parameters over time, achieving prescribed-time observation. This is accomplished by using an adaptive observer to estimate the unknown parameters of the DTP-PMVM model and a Neural Network (NN) to compensate for the nonlinear effects caused by the model’s unknown terms. The adaptation laws are innovatively modified to ensure the prescribed time convergence of the entire adaptive observer. MATLAB (R2023b) Simulink simulations demonstrate the superior speed-tracking accuracy and robustness of the speed and position observer against model parameter variations, strongly supporting the application of these strategies in real-world maritime propulsion systems. By integrating these advancements, this research not only proposes a more efficient, reliable, and robust propulsion motor design but also demonstrates an effective control strategy that significantly enhances overall system performance, particularly for maritime propulsion applications. Full article

The electrification of agricultural tractors is a key step toward improving energy efficiency and reducing environmental emissions. However, quantitative evaluation of drivetrain performance remains limited because workload data for electric tractors are scarce, while most available datasets originate from conventional mechanical tractors. In this study, a one-dimensional simulation model was developed to effectively utilize existing workload data by integrating the drivetrain and electrical characteristics of an actual electric tractor. The model combines an electrical subsystem based on field-oriented control (FOC) of a permanent magnet synchronous motor (PMSM) with a vehicle subsystem representing the mechanical drivetrain. Model validation was performed through dynamometer experiments using axle torque as input and motor responses as output, showing strong agreement with measured data. The validated model was applied to field-measured workloads to analyze motor performance, battery state-of-charge behavior, usable operating time, and operating points across various agricultural operations. The proposed simulation model enables quantitative evaluation of electric tractor performance under realistic load conditions and can be extended for co-simulation with higher-level control models. In future studies, the model will be utilized as a platform for testing and developing energy-efficient control algorithms for next-generation electric tractor systems. Full article

Motor eccentricity faults, stemming from the misalignment of the rotor’s center and pivot point, lead to significant vibrations and noise, compromising motor reliability. This study emphasizes the need for an efficient diagnostic system to enable early detection and correction of these faults. Our research proposes a novel Eccentricity Fault Diagnosis Network (E-FDNet), designed for integration into a Motor Eccentricity Fault Diagnosis System (MEFDS), utilizing neural networks for detection. Evaluation tests reveal that a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture is ideal as the internal neural network within the E-FDNet. Key contributions of this research include (1) E-FDNet that stabilizes transition predictions among SEF/DEF/MEF; (2) a steady-state characteristic normalization (SSCN) improving feature consistency under dynamic responses; (3) an integrated physics–FEM–experiment pipeline for controlled analysis and validation; (4) approximately 98.86% accuracy/F1 outperforming classical and deep baselines; and (5) a non-invasive, current-only sensing design suited for deployment. Full article

The permanent magnet synchronous motor (PMSM) is extensively utilized in the power drive systems of Cyber–Physical Systems (CPSs). In scenarios where control signals are subjected to malicious attacks within the network, ensuring that the PMSM achieves its designated speed within a specified timeframe serves as a critical metric for evaluating the efficacy of security control strategies in networked systems. To address practical challenges arising from updates to controlled objects at the physical layer and limitations of control layer algorithms—wherein convergence time for system trajectory tracking errors (TTEors) may extend indefinitely—we have developed a novel resilient control algorithm with predefined-time convergence (PreTC) tailored for uncertain PMSMs susceptible to cyber threats. Firstly, we introduce an innovative Lyapunov stability criterion characterized by an adjustable gain reaching law alongside PreTC. Following this, we design an SMS (SMS) that incorporates PreTC and employ an extreme learning machine (ELM) to facilitate real-time identification of both physical layer models and malicious cyber-attacks. A sliding-mode adaptive resilient controller devoid of explicit physical model information is proposed for CPSs, with Lyapunov stability theory substantiating the system’s predefined-time (PDT) stability. This significantly enhances resilience against malicious cyber-attacks and other uncertainties. Finally, comparative simulations involving four distinct resilient control algorithms demonstrate that our proposed algorithm not only guarantees predetermined convergence times but also exhibits robust resistance to cyber-attacks, parameter perturbations, and external disturbances—notably achieving a motor speed tracking error accuracy of 0.008. These findings validate the superior robustness and effectiveness of our control algorithm against malicious cyber threats. Full article

Permanent Magnet Synchronous Motors (PMSMs) exhibit inherent symmetry in their electromagnetic structure yet behave as nonlinear and strongly coupled systems that are susceptible to internal parameter perturbations and external disturbances, posing challenges to effective control under dynamic operating conditions. To address these issues, this paper proposes a sliding mode control strategy for PMSMs that integrates a Novel Adaptive Reaching Law (NARL) and an Improved Terminal Fuzzy Sliding Mode Disturbance Observer (IFTSMDO), denoted as SMC-NARL-IFTSMDO. The NARL is designed with a state-dependent dynamic gain adjustment mechanism and terminal attractive factor characteristics: it increases the gain to ensure fast convergence when the system state is far from the sliding mode surface, and adaptively attenuates the gain to suppress chattering when approaching the sliding mode surface, thereby balancing the contradiction between convergence speed and chattering in traditional sliding mode control. The IFTSMDO constructs a composite sliding mode surface incorporating error derivatives, terminal power terms, and saturation functions, which enhances the sensitivity of disturbance estimation in the small-error stage, avoids high-frequency chattering caused by sign functions, and provides accurate feedforward compensation for the speed loop controller to improve the system’s anti-disturbance capability. Additionally, the asymptotic stability of the proposed control strategy is strictly proven using the Lyapunov stability theory, laying a solid theoretical foundation for its application. Experiments are conducted on a TMS320F28379D DSP-based platform, and quantitative results show that compared with the traditional sliding mode control (SMC-TRL), the proposed strategy reduces the no-load startup response time by 60%, the steady-state speed fluctuation by 60%, and the speed fluctuation under load disturbance by 81.5%, fully demonstrating its superiority in dynamic response and anti-disturbance performance. Full article

In spoke-type permanent magnet synchronous motors (PMSMs), an asymmetric rib structure has been investigated as a method to reduce cogging torque. However, such rotor asymmetry tends to an increase in the axial force, which limits its applicability in precision and low-vibration systems. To overcome this limitation, this study proposes a new motor design method that introduces a skew factor as an additional design variable. The skew factor, defined as the ratio of the skewed region to the total stack length, enables simultaneous control of cogging torque and axial force by adjusting the degree of asymmetry along the rotor stack. Using parametric modeling and finite element analysis (FEA), the combined effects of the asymmetric rib and skew factor were investigated, and an optimized motor structure achieving balanced cogging torque and axial force characteristics was derived. The proposed design approach provides an effective method for simultaneously considering cogging torque and axial force in spoke-type PMSMs. Full article

In this paper, the inductance of a sensorless PMSM (Permanent Magnet Synchronous Motor) drive system equipped with a periodic load torque compensator based on a wavelet denoising and least-order observer with time-delay compensation is estimated in real-time. In a sensorless PMSM system with constant load torque, the magnetically saturated inductance value remains constant. This constant inductance error causes minor performance degradation, such as a constant rotor position estimation error and non-optimal torque current, but it does not introduce a speed estimation error. Conversely, in a sensorless PMSM motor system subjected to periodic load torque, the magnetically saturated inductance error fluctuates periodically. This fluctuation leads to periodic variations in both the estimated position error and the speed error, ultimately degrading the load torque compensation performance. This paper applies the maximum energy-to-Shannon entropy criterion for the optimal selection of the mother wavelet in the wavelet transform to remove the motor signal noise and achieve more accurate inductance estimation. Additionally, the coherence and correlation theory is proposed to address the time delay in the least-order observer and improve the time delay. A self-saturation compensation method is also proposed to minimize periodic speed fluctuations and improve control accuracy through inductance parameter estimation. Finally, experiments were conducted on a sensorless PMSM drive system to verify the inductance estimation performance and validate the effectiveness of vibration reduction. Full article

This paper employs a four-leg inverter topology to mitigate the high cost and zero-sequence current suppression challenges associated with dual-inverter open-winding permanent magnet synchronous motor (OW-PMSM) systems. Building on this topology, an improved current hysteresis control strategy incorporating a switching-state lookup table is proposed to suppress switching frequency fluctuations and current ripple. The developed system maintains high DC-link utilization and low cost while addressing the modulation complexity of conventional vector control and the switching frequency instability inherent in traditional hysteresis control. The study establishes a mathematical model of the OW-PMSM, analyzes the voltage vector distribution of the four-leg inverter, and designs an enhanced hysteresis control algorithm. By utilizing a predefined switching table to regulate switching logic in real time, the strategy achieves fixed switching frequency and effective harmonic suppression while preserving the fast-response characteristics of conventional hysteresis control. The experimental results demonstrate that the proposed control strategy achieves superior performance, effectively suppressing current ripple and providing ample stability margin, thereby validating its feasibility and effectiveness for practical engineering applications. Full article

This article describes the current status and future development trends of mine hoist control systems. The growing market demand for hoists and the need for stable, uninterrupted operation ensure the practical application of this article. A permanent magnet synchronous motor (PMSM) is used as the primary power source for the mine hoist. A MATLAB model is developed, using PID controllers to control the PMSM’Scheme 10. tons of CO₂ from electricity consumption, this equates to a reduction of 300 to 800 tons per year. Full article

Persistent discrepancies remain in the perceived far-field noise of automotive permanent-magnet synchronous motors (PMSMs) and the predictions of conventional NVH simulations. To bridge this gap, a Tri-source Electromagnetic Coupling NVH Integrated Framework (Tri-ECNVH) is developed, in which air-gap electromagnetic force harmonics, torque ripple, and cogging torque are treated as a coupled excitation system rather than as independent sources. Traditional workflows usually superpose their responses in the power domain, which tends to underestimate the radiating contribution of torque-related excitations and neglect their phase and order coupling with radial electromagnetic forces. In the proposed Tri-ECNVH framework, the three sources are mapped into the order domain, aligned by spatial order, and applied to the stator with phase consistency, so that inter-source coupling and cross terms are explicitly retained along a unified electromagnetic–structural–acoustic chain. Acoustic radiation is evaluated by prescribing the normal velocity on the stator outer surface as a Neumann boundary condition and computing the far-field A-weighted sound pressure level (SPL) using a boundary element method (BEM) model. Numerical results reveal pronounced cooperative amplification of the three sources at critical orders and within perceptually sensitive frequency bands; relative to independent-source modeling with power-domain summation, Tri-ECNVH predicts peak levels that are typically 5–10 dB higher and reproduces the spectral envelope and peak–valley evolution more faithfully. The framework therefore offers a practical, radiation-oriented basis for multi-source noise mitigation in traction PMSMs and helps narrow the gap between simulation and perceived sound quality in automotive applications. Full article

Water pumps consume roughly 20% of global electricity, yet 60–70% of pumps operate below optimal efficiency, leading to substantial energy waste. Improving pump efficiency is therefore critical. A major contributor to these losses is the low efficiency of the driving motor at reduced speeds and the lack of variable-speed capability—especially in single-phase pumps. This paper presents a fuzzy-logic–FOC (field oriented control) permanent magnet synchronous motor (PMSM) pump system that can run on either three-phase or single-phase power. The system maintains high efficiency across a wide speed range and saves energy not only through variable-speed operation but also via an intelligent control strategy termed “constant flow, variable pressure.” To assess performance, we conducted experiments comparing the proposed fuzzy-logic FOC controlled PMSM pump and a conventional AC asynchronous induction motor pump. The results show that the new system overcomes the inherent lack of speed regulation in traditional single-phase pumps and significantly improves efficiency across diverse operating conditions. Moreover, by implementing the “constant flow, variable pressure” strategy, the system achieves average energy savings estimated at 30–50% compared with a conventional AC asynchronous motor-driven pump. Full article

This paper proposes a super-twisting sliding mode observer-based model-free current controller (ST-MFCC) for permanent-magnet synchronous motor (PMSM). First, the mathematical model of the PMSM is established, and the model dependence of the deadbeat predictive current controller which serves as the foundation for the proposed ST-MFCC is analyzed, along with the stability impact of parameter variations on deadbeat predictive current control. Subsequently, the ST-MFCC is designed based on an ultralocal model and the super-twisting algorithm, eliminating dependence on the current model. Additionally, an adaptive method for tuning the key coefficients of the ultralocal model is introduced, enabling controller parameters to be rapidly optimized when deviations from actual system parameters occur. This approach reduces dependency on inductance parameters and aims to achieve high-performance PMSM current control with deadbeat characteristics. Finally, the effectiveness of the ST-MFCC is verified on a 400 W experimental platform. Full article

Sensor sampling errors and inverter dead-time effects introduce significant nonlinear disturbances into dual three-phase permanent magnet synchronous machine (DTP-PMSM) drive systems with sinusoidal excitation, leading to pronounced alternating current (AC) and direct current (DC) disturbances. These disturbances severely compromise the stability and reliability of the current control loop, ultimately degrading the overall driving accuracy of the system. To effectively address this issue, this paper proposes a novel interference suppression strategy based on bi-subspace predictive current control. Specifically, the proposed approach optimizes modulation through two-step virtual-vector-based predictive current control (VVPCC) operation to achieve disturbance decoupling. Building upon this foundation, a model-assisted discrete extended state observer (DESO) is incorporated into the fundamental subspace, whereas a discrete vector resonant controller (DVRC) with pre-distorted Tustin discretization is applied to the secondary subspace. Modeling analysis and experimental results demonstrate that, compared with the classical VVPCC method, the proposed bi-subspace VVPCC method has good steady-state performance and enhanced robustness in the presence of disturbances. Full article

Permanent Magnet Synchronous Motors (PMSMs) have been widely applied across various electrical systems due to their significant advantages, including high power density, high-efficiency conversion, and easy controllability. However, the issue of ‘parameter asymmetry’ (a mismatch between the controller’s preset parameters and the actual system parameters) in PMSMs can lead to performance problems, such as delayed speed response and increased overshoot. The destruction of symmetry, including the asymmetric weight distribution between new and old data in the moment-of-inertia identification algorithm and the asymmetry between “measured values and true values” caused by sampling delay, is the core factor limiting the system’s control performance. All these factors significantly affect the accuracy of parameter identification and the system’s stability. To address this, this study focuses on the mechanical parameter identification of PMSMs with the core goal of “symmetric matching between set values and true values”. Firstly, a current-speed dual closed-loop vector control system model is constructed. The PI parameters are tuned to meet the symmetric tracking requirements of “set value-feedback” in the dual loops, and the influence of the PMSM’s moment of inertia on the loop symmetry is analyzed. Secondly, the symmetry defects of traditional algorithms are highlighted, such as the imbalance between “data weight and working condition characteristics” in the least-squares method and the mismatch between “set inertia and true inertia” caused by data saturation. Finally, a Forgetting Factor Recursive Least Squares (FFRLS) scheme is proposed: the timing asymmetry of signals is corrected via a first-order inertial link, a forgetting factor λ is introduced to balance data weights, and a recursive structure is adopted to avoid data saturation. Simulation results show that when λ = 0.92, the identification accuracy reaches +5% with a convergence time of 0.39 s. Moreover, dynamic symmetry can still be maintained under multiple multiples of inertia, thereby improving identification performance and ensuring symmetry in servo control. Full article