Search Results (927)

To address the mismatch between materials and operational speed in mine scraper conveyors under time-varying load conditions, this paper proposes a methodology for the regulation of speed based on the quantity of coal transported by the scraper conveyor. Furthermore, a vector control strategy for permanent magnet synchronous motors (PMSMs) is presented, underpinned by a global fast terminal sliding mode controller. Firstly, a calculation model for the real-time coal volume of the scraper conveyor was developed based on the double-end oblique cutting coal mining technology in fully mechanized mining operations. This model takes into account the operational condition of the shearer and the scraper conveyor. In addition, a graded speed regulation control method was introduced. Secondly, a global fast terminal controller was developed by integrating the features of linear and terminal sliding mode surfaces. An enhanced sliding mode vector control strategy for the permanent magnet drive motor of the scraper conveyor was subsequently proposed. Finally, a simulation and ground test were subsequently performed on the PMSM experimental bench and SGZ2×1200 scraper conveyor to validate the proposed control strategy. The results indicated that the proposed control strategy not only diminished the overshoot of the rotational speed and decreased the dynamic response time but also improved the anti-interference capabilities of the PMSM relative to the original PI control. Moreover, the ground test validated the feasibility of the suggested speed regulation method. Full article

This study presents the design, modeling, and prototyping of an external rotor permanent magnet synchronous motor (ER-PMSM) specifically for elevator traction systems. The external rotor design aims to surpass the efficiency of conventional inner rotor gearless elevator traction motors. A commercially available 4 kW inner rotor permanent magnet synchronous motor (IR-PMSM) was selected for comparative analysis. Critical parameters, including stator tooth tip thickness, slot tip radius, slot height, stator yoke height, stator tooth thickness, and the number of turns per phase, were optimized to enhance efficiency. The artificial bee colony (ABC) algorithm was utilized for the first time to determine the optimal configuration of an external rotor PMSM. The prototype was fabricated and subjected to rigorous testing using a dedicated electrical motor test setup. Comparative results demonstrated a significant improvement in efficiency for the ER-PMSM over the IR-PMSM, with the efficiency increasing from 72.5% to 84.67% at nominal operating conditions. Full article

To improve the precision of the position and speed estimation in ultra-high-speed (UHS) permanent magnet synchronous motors (PMSM) without position sensors, multiple refinements to the traditional extended electromotive force (EEMF) estimation algorithm are proposed in this paper. The key improvements include discretization compensation, high-frequency harmonic filtering, and the real-time adjustment of the phase-locked loop (PLL) bandwidth. Firstly, a discrete model is introduced to address EMF cross-coupling issues. Secondly, an improved proportional resonant (IPR) controller eliminating static errors is utilized in place of the conventional proportional-integral (PI) controller and low-pass filter (LPF) to enable precise electromotive force extraction, effectively filtering high-frequency harmonics that arise in low carrier ratio conditions. Based on a standard PR design, the IPR controller offers a streamlined calculation for target leading angles in delay compensation schemes to effectively mitigate discretization and delay errors. Additionally, an adaptive phase-locked loop (AQPLL) dynamically adjusts its bandwidth during acceleration to balance noise rejection and phase delay, reducing position estimation errors and optimizing torque. Simulations and experimental analyses on a motor (90,000 rpm, 30 kW) validate the effectiveness of the proposed sensorless driving techniques and demonstrate enhanced performance in position and velocity estimation, compared to the conventional EEMF approach. Full article

In the process of diagnosing the inter-turn short circuit fault of the joint permanent magnet synchronous motor of an industrial robot, due to the small and sparse fault sample data, it is easy to misdiagnose, and it is difficult to quickly and accurately evaluate the fault degree, lock the fault location, and track the fault causes. A multi-task causal knowledge fault diagnosis method for inter-turn short circuits of permanent magnet synchronous motors based on meta-learning is proposed. Firstly, the variation of parameters under the motor’s inter-turn short circuit fault is thoroughly investigated, and the fault characteristic quantity is selected. Comprehensive simulations are conducted using Simulink, Simplorer, and Maxwell to generate data under different inter-turn short circuit fault states; meanwhile, the sample data are accurately labeled. Secondly, the sample data are introduced into the learning network for training, and the multi-task synchronous diagnosis of the fault degree and position of the short circuit between turns is realized. Finally, the Neo4j database based on causality knowledge of motor inter-turn short circuit fault is constructed. Experiments show that this method can diagnose the fault location, fault degree, and fault cause of the motor with different voltage unbalanced degrees. The diagnosis accuracy of fault degree is 99.75 ± 0.25%, and the diagnosis accuracy of fault location and fault degree is 99.45 ± 0.21%. Full article

Aiming at the irreversible demagnetization of permanent magnet synchronous motors (PMSMs) under extreme working conditions, a fault diagnosis method for permanent magnet demagnetization based on multi-parameter estimation is proposed in this paper. This scheme aims to provide technical support for enhancing the safety and reliability of permanent magnet motor drive systems. In the proposed scheme, multiple operating states of the motor are acquired by injecting sinusoidal current signals into the d-axis, ensuring that the parameter estimation equation satisfies the full rank condition. Furthermore, the accurate dq-axis inductance parameters are obtained based on a recursive least square method. Subsequently, a dual extended Kalman filter is employed to acquire real-time permanent magnet flux linkage data of PMSMs, and the estimation data between the two algorithms are transferred to each other to eliminate the bias of permanent magnet flux estimation caused by a parameter mismatch. Finally, accurate evaluation of the remanence level of the rotor permanent magnet and demagnetization fault diagnosis can be achieved based on the obtained permanent magnet flux linkage parameters. The experimental results show that the relative estimation errors of the dq-axis inductance and permanent magnet flux linkage are within 5%, which can realize the effective diagnosis of demagnetization fault and high-precision condition monitoring of a permanent magnet health. Full article

This paper explores the potential of Digital Twin (DT) technology for Permanent Magnet Synchronous Motors (PMSMs) and establishes a foundation for its modeling and applications. While DTs have been widely applied in complex systems and simulation software, their use in electric motors, especially PMSMs, remains limited. This study examines physics-based, data-driven, and hybrid modeling approaches and evaluates their feasibility for real-time simulation, fault detection, and predictive maintenance. It also identifies key challenges such as computational demands, data integration, and the lack of standardized frameworks. By assessing current developments and outlining future directions, this work provides insights into how DTs can be implemented for PMSMs and drive advancements in industrial applications. Full article

To improve the dynamic and steady-state performance of permanent magnet synchronous motors (PMSM), this paper proposes a real-time demanded current observation based sliding mode control strategy. Firstly, based on the mechanism between motor speed and current, a real-time demanded current observer is proposed, which can obtain the demanded current based on the motor speed. Secondly, the demanded current is directly used to calibrate the q-axis current reference in real-time, which can update the current reference value faster than the sliding mode controller. Due to the real-time demanded current feedforward, the anti-interference performance of the PMSM under the proposed control is improved effectively. Compared with the conventional sliding mode control, the proposed control has a better dynamic and steady-state performance. Finally, the effectiveness of the proposed control has been verified through simulation and experiments. Full article

Permanent magnet synchronous motors (PMSMs) are widely used in industrial automation and electric vehicles due to their high efficiency and excellent dynamic performance. However, controlling PMSMs presents challenges such as parameter variations and system nonlinearities. This paper proposes a twin delayed deep deterministic policy gradient (TD3)-based energy-saving optimization control method for PMSM drive systems. The TD3 algorithm uses double networks, target policy smoothing regularization, and delayed actor network updates to improve training stability and accuracy. Simulation experiments under two operating conditions show that the TD3 algorithm outperforms traditional proportional–integral (PI) controllers and linear active disturbance rejection control (LADRC) controllers in terms of reference trajectory tracking, q-axis current regulation, and speed tracking error minimization. The results demonstrate the TD3 algorithm’s effectiveness in enhancing motor efficiency and system robustness, offering a novel approach to PMSM drive system control through deep reinforcement learning. Full article

To enhance the speed-control performance of a permanent magnet synchronous motor (PMSM) drive system, an improved sliding-mode anti-interference control strategy is presented. Firstly, to tackle the speed fluctuation issue caused by cogging torque (a periodic disturbance) and time-varying disturbances at low set speeds in PMSM, an improved sliding-mode control (ISMC) is proposed. It consists of a continuous adaptive fast terminal sliding-mode surface (CAFTSMS) and a new reaching law (NRL). The CAFTSMS boosts the system’s immunity to interference, while the NRL, improved via an adaptive function, enhances the fast transient response and notably reduces speed fluctuations. Secondly, a quasi-proportional resonant (QPR) controller is introduced. It suppresses specific-order system harmonics, significantly reducing the harmonic amplitude and strengthening the system’s ability to handle periodic disturbances. Finally, a cascaded extended state observer (CESO) with a special cascade structure is proposed to solve the observation-delay problem in the traditional cascade structure. Experimental results show that the proposed sliding-mode anti-disturbance control strategy performs excellently in overcoming disturbances. Full article

This paper presents a cost-efficient estimation method, the filtering observer (FOBS), which provides a smooth estimation through prior estimation, enhancing the field-oriented control (FOC) performance of motion control systems by estimating the angular rotor position, angular rotor velocity, and disturbance torque of permanent magnet synchronous motors (PMSMs). The cost-effective FOBS demonstrates characteristics akin to optimal estimating methods and employs arbitrary pole placement, facilitating more straightforward adjustment of the FOBS gain. The non-linear characteristics of low-resolution and low-cost encoders, the computation of angular rotor velocity using traditional techniques, and disturbances over broad frequency ranges in the servo drive system impair the efficacy of the motion control system. As a cost-effective solution, the FOBS minimizes the deficiencies of the low-cost encoder, reduces oscillations and measurement delays in the speed feedback signal, and provides smooth estimation of disturbance torque. Based on the results from experiments, the FOBS was compared against traditional approaches and the performance of the motion control system was examined. Also, the performance of the motion control system was investigated. The results indicate that these enhancements were achieved with low processing power and an easily implementable estimate technique suitable for low-cost industrial systems. Full article

This article proposes an improved algorithm for the parameter identification of permanent magnet synchronous motors (PMSMs). An enhanced raccoon optimization algorithm (EROA) was formed by combining the raccoon optimization algorithm (ROA) with the adaptive exploration radius, raccoon-washing-food-inspired, and escaping-predator strategies. First, using some of the functions in IEEE CEC2015, the EROA solution has a large improvement in convergence speed and solution accuracy compared with other algorithms. Second, the EROA solution is more stable under the same conditions, as demonstrated by MATLAB parameter identification simulation. Finally, EROA is applied to motor parameter identification through motor control experiments. Full article

Several studies have focused on modeling and analyzing the impact of rotor faults in conventional low-pole-count machines, while related research on low-speed high-torque (LSHT) machines with a high pole count remains limited. In these machines, the combination of low speed, high inertia, and high torque levels presents a critical application for advanced diagnosis techniques. The present paper aims to describe and quantify the impact of rotor faults on the performance of LSHT machine types during the design stage. Specifically, 10-pole and 16-pole synchronous reluctance machines (SynRMs), permanent magnet synchronous machines (PMSMs), and squirrel-cage induction machines (SCIMs) are assessed by means of detailed 2D simulations. The effects of eccentricity, broken rotor bars, and partial demagnetization are studied, with a focus on performance variations. The results show that LSHT PMSMs are not significantly affected by the partial demagnetization of a few magnets, and the same holds true for common faults in SynRMs and SCIMs. Nonetheless, a significant increase in torque ripple was observed for all evaluated faults, with different origins and diverse effects on the torque waveform, which could be hard or invasive to analyze. Furthermore, it was concluded that specialized diagnosis techniques are effectively required for detecting the usual faults in LSHT machines, as their effect on major performance indicators is mostly minimal. Full article

This work proposes a nonlinear modeling of a permanent magnet synchronous motor (PMSM) based on state space neural networks. The state space neural network is trained and the state variables (currents in a direct–quadrature frame and the rotational speed) are estimated by considering a robust Unscented Kalman Filter (UKF). Two contributions are presented in this work: the fist one is a nonlinear modeling structure for a PMSM based on a state space neural network that allows real-time parameter identification, and the second one is PMSM neural network training and state estimation based on a robust UKF. The robustness of the UKF is obtained by using a singular value decomposition of the covariance matrix. A comparison analysis is performed over a real PMSM motor by considering the proposed approach and a linear approximation of the nonlinear model where the states and parameters are computed by using an Extended Kalman Filter. The identified model is validated in closed loop by considering a nonlinear control strategy based on state feedback linearization. Full article

To address the impact of load variations, external environmental changes, and the tuning of the parameters on Permanent Magnet Synchronous Motors (PMSMs) used in ships, this study proposes an Active Disturbance Rejection Control (ADRC) strategy for PMSMs, optimized by the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, based on the PMSM model, the study addresses the limited disturbance rejection capability of the traditional fal function in the Extended State Observer (ESO) of conventional ADRC. To improve the accuracy of the state observer, the faln function is introduced as a replacement for the traditional fal function. Second, due to the numerous parameters in ADRC, which are difficult to tune, the QPSO algorithm—known for its strong global search capabilities and fast convergence speed—is utilized for parameter optimization. Additionally, the position update formula within the optimization algorithm is revised and optimized. Finally, simulation experiments are conducted using the Matlab/Simulink platform, where practical conditions, such as load fluctuations and random noise, are incorporated. The simulation results demonstrate that, compared to PSO-ADRC control, IPSO-ADRC control, and ICFO-ADRC control, the proposed method offers a superior dynamic response. Specifically, the speed control accuracy is improved by 46.7%, torque ripple is reduced by 50.8%, and harmonic distortion decreases by 23.1%. These results highlight the significant advantages of this method in enhancing system robustness, dynamic response speed, and steady-state accuracy, making it particularly suitable for PMSM control systems in complex dynamic environments, such as those encountered on ships. Full article

Eccentricity faults are one of the main causes that significantly affect the performance of permanent magnet synchronous machines (PMSMs). Monitoring eccentricity in real time could prevent failures by adapting operation conditions and maintenance schedule when early signs of deterioration are detected. This article proposes making a circuit-type model of a permanent magnet machine with an easily configurable eccentricity for simulations and real-time analysis of signals under different operating conditions. The basis for the construction of the circuit model will be the simulation of the PMSM with 49 different coordinates of the rotor center, using the finite element analysis (FEA). The presence of eccentricity causes a variation in the inductances, the no-load flux and the expansion torque depending on the position of the rotor. The model proposes the use of bilinear interpolation (BI) to estimate the inductance matrix, the no-load flux vector captured by the stator winding and the cogging torque due to the presence of the magnets in the rotor, all of them for each rotor position. The validation is done by comparing the precision in the results of the machine’s self-inductances, the torque and the voltage waveform at the PMSM terminals and the static torque of the PMSM. The circuit model results are validated in two ways: (1) through experimental simulation, comparing the same results obtained using FEA and (2) through practical experimentation, producing a dynamic eccentricity in the machine of 0.3 mm. The results show that the proposed model is capable of accurately reproducing the behavior of the PMSM against eccentricity faults and presents computational time savings close to 99% compared to the response time obtained using FEA. This rapid PMSM model, parameterizable according to the degree of eccentricity, is the basis for the real-time simulation of the main machine waveforms, such as voltage, current and torque. Full article