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Volume 52,Issue 5,2025 Table of Contents
2025, 52(5):453-465.
DOI: 10.12177/emca.2025.035
Abstract:
[Objective] This study aims to address the issues of insufficient trajectory tracking accuracy and significant high-frequency chattering in permanent magnet direct current torque motor (PMDCTM) servo systems of laser trackers, which are caused by parameter variations, external disturbances, and nonlinear friction in conventional proportional-integral-derivative (PID) control. An nonsingular terminal sliding mode control (NTSMC) method with adaptive switching gain is proposed for PMDCTM position loop. [Methods] Firstly, a state-space representation based on angular error was developed using the electromechanical model of the azimuth axis motor in the laser tracker. Next, a fractional-order saturated function was selected as the sliding mode surface, and an adaptive switching gain law was designed by integrating adaptive control theory. This design effectively mitigated system uncertainties while avoiding singularities. Finally, the finite-time convergence of the control system was verified using Lyapunov stability theory, and the relationship between the designed sliding mode surface parameters and system convergence speed was analyzed. [Results] Comparative simulations and experiments were conducted using Matlab/Simulink software and enhanced programmable multi-axis controller platform. The results showed that, in the step response characterizing system rapidity, the PMDCTM position loop based on adaptive NTSMC (ANTSMC) reduced the convergence time by 41.2% compared to NTSMC and by 66.3% compared to PID control. In the application scenario of commutation of the tracker's azimuth axis motor, the ANTSMC-based PMDCTM position loop demonstrated a 37.2% reduction in peak transient tracking error compared to NTSMC and 76.9% reduction compared to PID control. Furthermore, the steady-state chattering was significantly reduced, thereby enhancing the system's ability to resist external disturbances. [Conclusion] The proposed ANTSMC method effectively improves the control accuracy of PMDCTM position loop, enhances the dynamic response performance of laser tracker servo systems, mitigates chattering phenomena, and demonstrates excellent robustness.
[Objective] This study aims to address the issues of insufficient trajectory tracking accuracy and significant high-frequency chattering in permanent magnet direct current torque motor (PMDCTM) servo systems of laser trackers, which are caused by parameter variations, external disturbances, and nonlinear friction in conventional proportional-integral-derivative (PID) control. An nonsingular terminal sliding mode control (NTSMC) method with adaptive switching gain is proposed for PMDCTM position loop. [Methods] Firstly, a state-space representation based on angular error was developed using the electromechanical model of the azimuth axis motor in the laser tracker. Next, a fractional-order saturated function was selected as the sliding mode surface, and an adaptive switching gain law was designed by integrating adaptive control theory. This design effectively mitigated system uncertainties while avoiding singularities. Finally, the finite-time convergence of the control system was verified using Lyapunov stability theory, and the relationship between the designed sliding mode surface parameters and system convergence speed was analyzed. [Results] Comparative simulations and experiments were conducted using Matlab/Simulink software and enhanced programmable multi-axis controller platform. The results showed that, in the step response characterizing system rapidity, the PMDCTM position loop based on adaptive NTSMC (ANTSMC) reduced the convergence time by 41.2% compared to NTSMC and by 66.3% compared to PID control. In the application scenario of commutation of the tracker's azimuth axis motor, the ANTSMC-based PMDCTM position loop demonstrated a 37.2% reduction in peak transient tracking error compared to NTSMC and 76.9% reduction compared to PID control. Furthermore, the steady-state chattering was significantly reduced, thereby enhancing the system's ability to resist external disturbances. [Conclusion] The proposed ANTSMC method effectively improves the control accuracy of PMDCTM position loop, enhances the dynamic response performance of laser tracker servo systems, mitigates chattering phenomena, and demonstrates excellent robustness.
2025, 52(5):466-476.
DOI: 10.12177/emca.2025.030
Abstract:
[Objective] The integration of high-penetration photovoltaics and high-power electric vehicles has altered the operating characteristics of power systems, resulting in frequent wide-range voltage fluctuations. This imposes stricter requirements on the voltage regulation speed, range, and capacity of distribution transformers. Traditional distribution transformers, limited by the high-voltage side, exhibit small tap steps and slow voltage regulation speeds, making wide-range voltage regulation impossible. To address this issue, this study proposes a novel hybrid wide-range voltage regulation method on low-voltage side based on active inversion, which connects the tap changer to the low-voltage side of the distribution transformer to achieve wide-range voltage regulation. [Methods] To address the challenges of high switching currents and large conduction losses on the low-voltage side, an inverter power source was incorporated into the power electronic commutation branch. A voltage-current dual-loop competition strategy was employed to regulate the control signals of the inverter circuit, offsetting the forward voltage of electronic components and enabling zero voltage across the mechanical branch. Meanwhile, the inverter circuit was activated to generate load current, forcing current commutation from the mechanical branch to the power electronic commutation branch, thus achieving zero current in the mechanical branch. This ensured true zero-voltage and zero-current operation of the mechanical switch, solving the problem of incomplete arc extinction in traditional hybrid switches. [Results] The simulation results showed that the inverter power supply provided zero-voltage and zero-current conditions for the mechanical switch, enabling arc-free operation. Compared to traditional hybrid switches, the proposed novel hybrid wide-range voltage regulation method on the low-voltage side reduced the voltage regulation time by at least four times. Moreover, it generated significantly lower arc energy during switching. After 300 000 switching operations, the mechanical contacts had a remaining service life index of 97.2%, demonstrating relatively long service life, while traditional hybrid switches exhibited a service life index of only 37.9%. [Conclusion] The proposed novel hybrid wide-range voltage regulation method on the low-voltage side achieves rapid, wide-range, and low-loss voltage regulation.
[Objective] The integration of high-penetration photovoltaics and high-power electric vehicles has altered the operating characteristics of power systems, resulting in frequent wide-range voltage fluctuations. This imposes stricter requirements on the voltage regulation speed, range, and capacity of distribution transformers. Traditional distribution transformers, limited by the high-voltage side, exhibit small tap steps and slow voltage regulation speeds, making wide-range voltage regulation impossible. To address this issue, this study proposes a novel hybrid wide-range voltage regulation method on low-voltage side based on active inversion, which connects the tap changer to the low-voltage side of the distribution transformer to achieve wide-range voltage regulation. [Methods] To address the challenges of high switching currents and large conduction losses on the low-voltage side, an inverter power source was incorporated into the power electronic commutation branch. A voltage-current dual-loop competition strategy was employed to regulate the control signals of the inverter circuit, offsetting the forward voltage of electronic components and enabling zero voltage across the mechanical branch. Meanwhile, the inverter circuit was activated to generate load current, forcing current commutation from the mechanical branch to the power electronic commutation branch, thus achieving zero current in the mechanical branch. This ensured true zero-voltage and zero-current operation of the mechanical switch, solving the problem of incomplete arc extinction in traditional hybrid switches. [Results] The simulation results showed that the inverter power supply provided zero-voltage and zero-current conditions for the mechanical switch, enabling arc-free operation. Compared to traditional hybrid switches, the proposed novel hybrid wide-range voltage regulation method on the low-voltage side reduced the voltage regulation time by at least four times. Moreover, it generated significantly lower arc energy during switching. After 300 000 switching operations, the mechanical contacts had a remaining service life index of 97.2%, demonstrating relatively long service life, while traditional hybrid switches exhibited a service life index of only 37.9%. [Conclusion] The proposed novel hybrid wide-range voltage regulation method on the low-voltage side achieves rapid, wide-range, and low-loss voltage regulation.
2025, 52(5):477-486.
DOI: 10.12177/emca.2025.023
Abstract:
[Objective] To address the issue of reduced transfer efficiency caused by cross-coupling among receiver coils in multi-load wireless power transfer (WPT) systems, this study proposes a multi-load WPT system based on Halbach-type magnetic couplers. [Methods] By controlling the excitation current direction of constituent coils in the transmitter unit and utilizing the combined magnetic effects of these coils, the Halbach-type magnetic coupler achieved dual regulation effects of magnetic field focusing and attenuation in target areas. Through finite element simulation modeling and optimized design of the magnetic coupler, the mutual inductance and magnetic field characteristics between the transmitter unit and receiver unit were analyzed. The principles of magnetic field focusing and attenuation were explained from the perspective of magnetic flux. Additionally, an SS-type equivalent circuit topology was constructed to derive the output characteristics of the system. Finally, an experimental platform was built to analyze and validate the variation patterns in mutual inductance, output characteristics, and magnetic attenuation performance of the system. [Results] Experimental results showed that the Halbach-type magnetic coupler exhibited the capabilities of bidirectional magnetic field focusing and attenuation. The distribution of magnetic field focusing and attenuation regions were spatially staggered, which effectively suppressed cross-coupling among receiver coils and reduced magnetic leakage in the system. When two receiver units were positioned 20 mm from the transmitter unit and both loads were 20 Ω, the system achieved a maximum overall transfer efficiency of 80.84%. [Conclusion] The Halbach-type magnetic coupler proposed in this study successfully addresses the issue of cross-coupling between receiver coils in multi-load WPT systems through asymmetric magnetic field regulation technology. Due to its modular unit structure, the system demonstrates excellent scalability. This modular design, characterized by its simplicity and flexibility, shows promising potential for broader applications in multi-load WPT systems.
[Objective] To address the issue of reduced transfer efficiency caused by cross-coupling among receiver coils in multi-load wireless power transfer (WPT) systems, this study proposes a multi-load WPT system based on Halbach-type magnetic couplers. [Methods] By controlling the excitation current direction of constituent coils in the transmitter unit and utilizing the combined magnetic effects of these coils, the Halbach-type magnetic coupler achieved dual regulation effects of magnetic field focusing and attenuation in target areas. Through finite element simulation modeling and optimized design of the magnetic coupler, the mutual inductance and magnetic field characteristics between the transmitter unit and receiver unit were analyzed. The principles of magnetic field focusing and attenuation were explained from the perspective of magnetic flux. Additionally, an SS-type equivalent circuit topology was constructed to derive the output characteristics of the system. Finally, an experimental platform was built to analyze and validate the variation patterns in mutual inductance, output characteristics, and magnetic attenuation performance of the system. [Results] Experimental results showed that the Halbach-type magnetic coupler exhibited the capabilities of bidirectional magnetic field focusing and attenuation. The distribution of magnetic field focusing and attenuation regions were spatially staggered, which effectively suppressed cross-coupling among receiver coils and reduced magnetic leakage in the system. When two receiver units were positioned 20 mm from the transmitter unit and both loads were 20 Ω, the system achieved a maximum overall transfer efficiency of 80.84%. [Conclusion] The Halbach-type magnetic coupler proposed in this study successfully addresses the issue of cross-coupling between receiver coils in multi-load WPT systems through asymmetric magnetic field regulation technology. Due to its modular unit structure, the system demonstrates excellent scalability. This modular design, characterized by its simplicity and flexibility, shows promising potential for broader applications in multi-load WPT systems.
2025, 52(5):487-500.
DOI: 10.12177/emca.2025.037
Abstract:
[Objective] Hall position sensors are prone to faults under harsh environments, mechanical vibrations, and electrical stress. In addition, installation deviations can significantly affect the motor’s control accuracy and system stability. To enhance the reliability and fault tolerance of permanent magnet synchronous motor (PMSM) drive systems based on Hall position sensors, this study investigates fault diagnosis methods and fault-tolerant control strategies, aiming to address the limitations of traditional approaches in fault diagnostic efficiency and fault-tolerant control accuracy. [Methods] To overcome the low efficiency and misjudgment issues of traditional feature-sequence-based fault detection methods, a novel fast fault diagnosis method based on pseudo-acceleration variation thresholds was proposed. This method compared the change in pseudo-acceleration method with a preset threshold at the Hall transition signal to rapidly detect faults. Furthermore, an improved fault-tolerant control method that integrated traditional fault-tolerant interpolation method with the fast diagnosis method was proposed to minimize the effect of Hall installation deviation. The method was combined with the designed adaptive notch angle observer to reduce the second-harmonic errors caused by Hall sensor misalignment, thereby improving fault-tolerant performance. [Results] Experimental results showed that the proposed fast fault diagnosis method accurately identified both single-phase and dual-phase Hall sensor faults, significantly reducing diagnostic time and avoiding the delays and misjudgments of traditional detection methods. The improved fault-tolerant interpolation method, combined with the adaptive notch angle observer, accurately estimated rotor position even in the presence of Hall sensor faults, effectively enhancing system stability and control precision. The method maintained excellent control performance particularly under different operating conditions. [Conclusion] The proposed fast fault diagnosis method based on pseudo-acceleration variation thresholds, along with the improved fault-tolerant control strategy, outperforms traditional methods in the case of single-phase Hall sensor faults, dual-phase Hall sensor faults, and under different operating conditions. It provides reliable technical support for the stable operation of motors under Hall sensor faults.
[Objective] Hall position sensors are prone to faults under harsh environments, mechanical vibrations, and electrical stress. In addition, installation deviations can significantly affect the motor’s control accuracy and system stability. To enhance the reliability and fault tolerance of permanent magnet synchronous motor (PMSM) drive systems based on Hall position sensors, this study investigates fault diagnosis methods and fault-tolerant control strategies, aiming to address the limitations of traditional approaches in fault diagnostic efficiency and fault-tolerant control accuracy. [Methods] To overcome the low efficiency and misjudgment issues of traditional feature-sequence-based fault detection methods, a novel fast fault diagnosis method based on pseudo-acceleration variation thresholds was proposed. This method compared the change in pseudo-acceleration method with a preset threshold at the Hall transition signal to rapidly detect faults. Furthermore, an improved fault-tolerant control method that integrated traditional fault-tolerant interpolation method with the fast diagnosis method was proposed to minimize the effect of Hall installation deviation. The method was combined with the designed adaptive notch angle observer to reduce the second-harmonic errors caused by Hall sensor misalignment, thereby improving fault-tolerant performance. [Results] Experimental results showed that the proposed fast fault diagnosis method accurately identified both single-phase and dual-phase Hall sensor faults, significantly reducing diagnostic time and avoiding the delays and misjudgments of traditional detection methods. The improved fault-tolerant interpolation method, combined with the adaptive notch angle observer, accurately estimated rotor position even in the presence of Hall sensor faults, effectively enhancing system stability and control precision. The method maintained excellent control performance particularly under different operating conditions. [Conclusion] The proposed fast fault diagnosis method based on pseudo-acceleration variation thresholds, along with the improved fault-tolerant control strategy, outperforms traditional methods in the case of single-phase Hall sensor faults, dual-phase Hall sensor faults, and under different operating conditions. It provides reliable technical support for the stable operation of motors under Hall sensor faults.
2025, 52(5):501-512.
DOI: 10.12177/emca.2025.031
Abstract:
[Objective] The system architecture of single-inverter multiple permanent magnet synchronous motor (PMSM) in parallel operation can significantly reduce hardware costs, minimize system size, and improve power density. It has become a highly cost-effective technical solution. However, traditional control strategies often adopt average models or multiple coordinate system models, which not only increase the complexity of system modeling, but may also lead to reduced control accuracy and degraded dynamic response. To address this issue, this study proposes a control strategy for a single-inverter multi-PMSM parallel system based on a unified coordinate system, aiming to simplify the control structure and enhance system performance. [Methods] First, the electromagnetic and mechanical characteristics of the system were investigated using frequency domain analysis. Building upon this, a vector control strategy based on a unified coordinate system was proposed. By employing a mathematical model in the unified coordinate system, the control complexity of the multi-motor system was reduced. Finally, a time-domain simulation model was established to validate the effectiveness of the proposed strategy. The system operation under different operating conditions was analyzed, and the dynamic response and steady-state accuracy of the control system were comprehensively tested. [Results] The simulation results showed that when the load torques between the two PMSMs differed or underwent dynamic variations, the designed control system was able to rapidly and accurately regulate motor speeds to closely follow the given speed commands. This verified the effectiveness and practicality of the proposed control strategy. [Conclusion] The control strategy for single-inverter multi-PMSM parallel systems based on a unified coordinate system proposed in this study not only simplifies the control structure of traditional multi-motor systems, but also significantly enhances both dynamic performance and steady-state accuracy. It provides new insights for control optimization in single-inverter parallel PMSM systems.
[Objective] The system architecture of single-inverter multiple permanent magnet synchronous motor (PMSM) in parallel operation can significantly reduce hardware costs, minimize system size, and improve power density. It has become a highly cost-effective technical solution. However, traditional control strategies often adopt average models or multiple coordinate system models, which not only increase the complexity of system modeling, but may also lead to reduced control accuracy and degraded dynamic response. To address this issue, this study proposes a control strategy for a single-inverter multi-PMSM parallel system based on a unified coordinate system, aiming to simplify the control structure and enhance system performance. [Methods] First, the electromagnetic and mechanical characteristics of the system were investigated using frequency domain analysis. Building upon this, a vector control strategy based on a unified coordinate system was proposed. By employing a mathematical model in the unified coordinate system, the control complexity of the multi-motor system was reduced. Finally, a time-domain simulation model was established to validate the effectiveness of the proposed strategy. The system operation under different operating conditions was analyzed, and the dynamic response and steady-state accuracy of the control system were comprehensively tested. [Results] The simulation results showed that when the load torques between the two PMSMs differed or underwent dynamic variations, the designed control system was able to rapidly and accurately regulate motor speeds to closely follow the given speed commands. This verified the effectiveness and practicality of the proposed control strategy. [Conclusion] The control strategy for single-inverter multi-PMSM parallel systems based on a unified coordinate system proposed in this study not only simplifies the control structure of traditional multi-motor systems, but also significantly enhances both dynamic performance and steady-state accuracy. It provides new insights for control optimization in single-inverter parallel PMSM systems.
2025, 52(5):513-526.
DOI: 10.12177/emca.2025.026
Abstract:
[Objective] Deep learning models are widely used in transformer fault diagnosis due to their ability to learn underlying data patterns and construct hierarchical feature representations. However, their massive number of parameters, complex network topology, and high calculation and storage costs limit their practical application in fault diagnosis of power transformers. [Methods] To address the above issues, this study proposed a transformer fault diagnosis method based on multi-level sparse MobileNetV2. First, spindle-shaped and hourglass-shaped blocks were used to compactly improve the inverted residual blocks of the MobileNetV2 model, reducing parameter number and computational complexity from the model structure itself to achieve preliminary model sparsity. Second, a group-level pruning method based on dependency graph model was proposed. The coupled parameters in the model were grouped, and a group-level pruning optimization strategy based on L2 norm was designed to perform sparse training and pruning fine-tuning. This process removed redundant structures and parameters in the model, further reducing parameter number and computational complexity and enhancing model sparsity. Finally, an 8-bit symmetric uniform quantization and quantization-aware training method was proposed. The 32-bit high-resolution floating-point parameters in the model were quantized into 8-bit low-resolution integer parameters. Building on this, model inference was performed to further reduce the computational complexity and achieve multi-level model sparsity. [Results] The results of numerical experiments and performance evaluations showed that compared with the original MobileNetV2 model, the improved multi-level sparse model proposed in this study achieved a fault identification accuracy of 95.2%, while reducing the parameter number, computational complexity, and model size by approximately 73.5%, 96.9%, and 68.8%, respectively. Moreover, the inference time for identifying 1 000 images was only 0.66 seconds. [Conclusion] The proposed method in this study effectively combines three types of individual sparsity methods: compact model improvement, model pruning, and parameter quantization. It achieves multi-level sparsity of deep learning models while maintaining high accuracy, effectively addressing the issue of over-parameterization caused by limited sample data in power transformer fault diagnosis and eliminating its adverse effects.
[Objective] Deep learning models are widely used in transformer fault diagnosis due to their ability to learn underlying data patterns and construct hierarchical feature representations. However, their massive number of parameters, complex network topology, and high calculation and storage costs limit their practical application in fault diagnosis of power transformers. [Methods] To address the above issues, this study proposed a transformer fault diagnosis method based on multi-level sparse MobileNetV2. First, spindle-shaped and hourglass-shaped blocks were used to compactly improve the inverted residual blocks of the MobileNetV2 model, reducing parameter number and computational complexity from the model structure itself to achieve preliminary model sparsity. Second, a group-level pruning method based on dependency graph model was proposed. The coupled parameters in the model were grouped, and a group-level pruning optimization strategy based on L2 norm was designed to perform sparse training and pruning fine-tuning. This process removed redundant structures and parameters in the model, further reducing parameter number and computational complexity and enhancing model sparsity. Finally, an 8-bit symmetric uniform quantization and quantization-aware training method was proposed. The 32-bit high-resolution floating-point parameters in the model were quantized into 8-bit low-resolution integer parameters. Building on this, model inference was performed to further reduce the computational complexity and achieve multi-level model sparsity. [Results] The results of numerical experiments and performance evaluations showed that compared with the original MobileNetV2 model, the improved multi-level sparse model proposed in this study achieved a fault identification accuracy of 95.2%, while reducing the parameter number, computational complexity, and model size by approximately 73.5%, 96.9%, and 68.8%, respectively. Moreover, the inference time for identifying 1 000 images was only 0.66 seconds. [Conclusion] The proposed method in this study effectively combines three types of individual sparsity methods: compact model improvement, model pruning, and parameter quantization. It achieves multi-level sparsity of deep learning models while maintaining high accuracy, effectively addressing the issue of over-parameterization caused by limited sample data in power transformer fault diagnosis and eliminating its adverse effects.
2025, 52(5):527-539.
DOI: 10.12177/emca.2025.033
Abstract:
[Objective] In field-oriented control (FOC) system of traditional permanent magnet synchronous motor (PMSM), the speed loop and the stator dq-axis current loops typically adopt proportional integral (PI) control, which results in a significant speed overshoot. Although sliding mode control (SMC) can reduce overshoot, it still suffers from slow dynamic response and significant current ripple. [Methods] To address these issues, this study adopted super-twisting sliding mode control (STSMC) to regulate the speed outer loop and stator dq-axis current inner loop to improve response speed and suppress speed and current ripples, thereby enhancing the dynamic and steady-state performance of the system. Furthermore, to improve system robustness under external load disturbances, an extended state observer (ESO) was designed to observe disturbances. By treating the load as an extended state variable, the proposed method reduced speed recovery time and mitigated speed drop during sudden load torque changes through feedforward compensation, thereby enhancing system robustness. [Results] Simulation results showed that under an operating condition of 1 000 r/min reference speed and 10 N·m step load, compared with speed SMC, the STSMC reduced the speed regulation time by 86.25%, the root mean square error (RMSE) of speed ripple by 95.35%, the d-axis current ripple RMSE by 45.44%, and the q-axis current ripple RMSE by 34.31%. Compared with speed SMC, the STSMC based on ESO (STSMC-ESO) reduced the speed regulation time by 86.25%, the speed ripple RMSE by 95.70%, the d-axis current ripple RMSE by 45.61%, and the q-axis current ripple RMSE by 37.02%. Moreover, under external load disturbances, compared with STSMC, STSMC-ESO reduced the speed drop by 21.81% and the speed recovery time by 90%. [Conclusion] The STSMC-ESO strategy effectively reduces speed overshoot and enhances dynamic response speed. Meanwhile, it suppresses system chattering and significantly reduces speed and current ripples, thus improving steady-state performance of the system. Under external load disturbances, the system can quickly recover speed and maintain relative stability.
[Objective] In field-oriented control (FOC) system of traditional permanent magnet synchronous motor (PMSM), the speed loop and the stator dq-axis current loops typically adopt proportional integral (PI) control, which results in a significant speed overshoot. Although sliding mode control (SMC) can reduce overshoot, it still suffers from slow dynamic response and significant current ripple. [Methods] To address these issues, this study adopted super-twisting sliding mode control (STSMC) to regulate the speed outer loop and stator dq-axis current inner loop to improve response speed and suppress speed and current ripples, thereby enhancing the dynamic and steady-state performance of the system. Furthermore, to improve system robustness under external load disturbances, an extended state observer (ESO) was designed to observe disturbances. By treating the load as an extended state variable, the proposed method reduced speed recovery time and mitigated speed drop during sudden load torque changes through feedforward compensation, thereby enhancing system robustness. [Results] Simulation results showed that under an operating condition of 1 000 r/min reference speed and 10 N·m step load, compared with speed SMC, the STSMC reduced the speed regulation time by 86.25%, the root mean square error (RMSE) of speed ripple by 95.35%, the d-axis current ripple RMSE by 45.44%, and the q-axis current ripple RMSE by 34.31%. Compared with speed SMC, the STSMC based on ESO (STSMC-ESO) reduced the speed regulation time by 86.25%, the speed ripple RMSE by 95.70%, the d-axis current ripple RMSE by 45.61%, and the q-axis current ripple RMSE by 37.02%. Moreover, under external load disturbances, compared with STSMC, STSMC-ESO reduced the speed drop by 21.81% and the speed recovery time by 90%. [Conclusion] The STSMC-ESO strategy effectively reduces speed overshoot and enhances dynamic response speed. Meanwhile, it suppresses system chattering and significantly reduces speed and current ripples, thus improving steady-state performance of the system. Under external load disturbances, the system can quickly recover speed and maintain relative stability.
LI Yaohua
,
CHONG Guochen
,
GUO Weichao
,
XU Zhixiong
,
WANG Zichen
,
GAO Sai
,
WANG Qinzheng
,
ZHANG Xinquang
,
TONG Ruiqi
,
DENG Yizhi
,
DING Hong
2025, 52(5):540-551.
DOI: 10.12177/emca.2025.025
Abstract:
[Objective] To address the heavy computational burden of traversing all voltage vectors in two-step model predictive current control (MPCC) for permanent magnet synchronous motor (PMSM), this study proposes three streamlined control sets. These sets reduce the number of voltage vector traversal by incorporating additional constraints, thereby improving real-time performance. [Methods] Firstly, the voltage vectors were classified into five categories based on the positive and negative signs of their projections on the dq-axis. Subsequently, the error between the reference and actual values of the motor stator dq-axis current was used as constraints. The selection patterns of these five voltage categories were analyzed for both the first and second steps of model prediction under the imposed constraints. By eliminating underutilized voltage vectors, streamlined control set 1 and streamlined control set 2 were proposed. Based on this, an additional constraint on the square root of the dq-axis current error was introduced to analyze the distribution of zero voltage vectors under different error bands, further simplifying the control set and leading to the proposal of streamlined control set 3. [Results] The simulation and real-time experimental results showed that streamlined control set 1 achieved identical performance to the traditional control set, reducing the voltage vector sequence to 36 and decreasing the runtime to 53.31% of the traditional control set. Streamlined control set 2 and streamlined control set 3 showed performance comparable to traditional control set with their voltage vector sequences reduced to 9 and 4, respectively, and runtime reduced to 19.98% and 12.57% of the traditional control set’s runtime. [Conclusion] The three proposed streamlined control sets based on error constraints and voltage distribution patterns can significantly reduce traversal times while maintaining comparable control performance. This approach effectively optimizes real-time performance and provides a new solution for reducing the computational burden in two-step MPCC.
[Objective] To address the heavy computational burden of traversing all voltage vectors in two-step model predictive current control (MPCC) for permanent magnet synchronous motor (PMSM), this study proposes three streamlined control sets. These sets reduce the number of voltage vector traversal by incorporating additional constraints, thereby improving real-time performance. [Methods] Firstly, the voltage vectors were classified into five categories based on the positive and negative signs of their projections on the dq-axis. Subsequently, the error between the reference and actual values of the motor stator dq-axis current was used as constraints. The selection patterns of these five voltage categories were analyzed for both the first and second steps of model prediction under the imposed constraints. By eliminating underutilized voltage vectors, streamlined control set 1 and streamlined control set 2 were proposed. Based on this, an additional constraint on the square root of the dq-axis current error was introduced to analyze the distribution of zero voltage vectors under different error bands, further simplifying the control set and leading to the proposal of streamlined control set 3. [Results] The simulation and real-time experimental results showed that streamlined control set 1 achieved identical performance to the traditional control set, reducing the voltage vector sequence to 36 and decreasing the runtime to 53.31% of the traditional control set. Streamlined control set 2 and streamlined control set 3 showed performance comparable to traditional control set with their voltage vector sequences reduced to 9 and 4, respectively, and runtime reduced to 19.98% and 12.57% of the traditional control set’s runtime. [Conclusion] The three proposed streamlined control sets based on error constraints and voltage distribution patterns can significantly reduce traversal times while maintaining comparable control performance. This approach effectively optimizes real-time performance and provides a new solution for reducing the computational burden in two-step MPCC.
2025, 52(5):552-561.
DOI: 10.12177/emca.2025.034
Abstract:
[Objective] To address the difficulty in heat dissipation of drive motors used in vacuum pumps during actual operation and the tendency of its temperature to exceed insulation limits under impact loads, a neural network method is used to predict the heating of vacuum pump drive motors. [Methods] Taking a 4.5 kW vacuum pump drive motor as an example, the temperature simulation analysis of the motor under impact loads was first conducted. The instantaneous data were classified, and a neural network was trained using historical data to establish the mapping relationship between the operating data and temperature of the vacuum pump drive motor. Subsequently, a motor test platform was constructed to conduct impact tests on the motor. The test data were used to correct the neural network training model. By comparing predicted temperatures with actual measured temperatures through experiments, the accuracy of the corrected model was validated. [Results] The experimental results showed that the accuracy of the neural network model for temperature prediction was improved after error correction. The bidirectional long short-term memory network with error correction outperformed the convolutional neural network and the long short-term memory network in predicting the temperature at the stator winding end, with a coefficient of determination reaching 0.979. [Conclusion] This study provides a method and a technical approach for heating prediction of vacuum pump drive motors under impact loads without relying on temperature sensors, offering an algorithmic basis for the effective control of drive motors.
[Objective] To address the difficulty in heat dissipation of drive motors used in vacuum pumps during actual operation and the tendency of its temperature to exceed insulation limits under impact loads, a neural network method is used to predict the heating of vacuum pump drive motors. [Methods] Taking a 4.5 kW vacuum pump drive motor as an example, the temperature simulation analysis of the motor under impact loads was first conducted. The instantaneous data were classified, and a neural network was trained using historical data to establish the mapping relationship between the operating data and temperature of the vacuum pump drive motor. Subsequently, a motor test platform was constructed to conduct impact tests on the motor. The test data were used to correct the neural network training model. By comparing predicted temperatures with actual measured temperatures through experiments, the accuracy of the corrected model was validated. [Results] The experimental results showed that the accuracy of the neural network model for temperature prediction was improved after error correction. The bidirectional long short-term memory network with error correction outperformed the convolutional neural network and the long short-term memory network in predicting the temperature at the stator winding end, with a coefficient of determination reaching 0.979. [Conclusion] This study provides a method and a technical approach for heating prediction of vacuum pump drive motors under impact loads without relying on temperature sensors, offering an algorithmic basis for the effective control of drive motors.
2025, 52(5):562-571.
DOI: 10.12177/emca.2025.027
Abstract:
[Objective] To address the issues of low robustness and high dependence on motor parameters in model predictive control (MPC) systems for dual three-phase permanent magnet synchronous motor (PMSM), this study improves and optimizes the sliding mode parameter identification method. A dual three-phase PMSM model predictive current control (MPCC) method based on super-twisting sliding mode observer (ST-SMO) parameter identification is proposed. [Methods] Firstly, a higher-order sliding mode algorithm was introduced to replace the switching function sign in traditional sliding mode observers as the new sliding mode reaching law. A super-twisting algorithm-based ST-SMO was designed to achieve more accurate identification of motor inductance parameters. Then, stability analysis of the designed higher-order sliding mode observer was conducted using Lyapunov theory. Combined with incremental equations, the prediction model in the MPCC system was optimized, eliminating the effect of flux linkage parameters on the robustness of the motor control system. Finally, the inductance parameters were accurately identified using the parameter identification algorithm, reducing the dependence of the MPCC system on motor parameters. [Results] The improved dual three-phase PMSM MPCC system incorporating the ST-SMO and incremental prediction model demonstrated excellent steady-state and dynamic performance. For parameter identification, it eliminated the chattering phenomenon caused by the first-order sliding mode system, improved the accuracy of parameter identification, and enhanced the identification speed of motor parameters. Additionally, the control system maintained high control performance for the dual three-phase PMSM under parameter mismatch conditions. [Conclusion] The MPCC system based on ST-SMO parameter identification proposed in this study demonstrates good feasibility and stability under various operating conditions, such as speed and torque sudden transients.
[Objective] To address the issues of low robustness and high dependence on motor parameters in model predictive control (MPC) systems for dual three-phase permanent magnet synchronous motor (PMSM), this study improves and optimizes the sliding mode parameter identification method. A dual three-phase PMSM model predictive current control (MPCC) method based on super-twisting sliding mode observer (ST-SMO) parameter identification is proposed. [Methods] Firstly, a higher-order sliding mode algorithm was introduced to replace the switching function sign in traditional sliding mode observers as the new sliding mode reaching law. A super-twisting algorithm-based ST-SMO was designed to achieve more accurate identification of motor inductance parameters. Then, stability analysis of the designed higher-order sliding mode observer was conducted using Lyapunov theory. Combined with incremental equations, the prediction model in the MPCC system was optimized, eliminating the effect of flux linkage parameters on the robustness of the motor control system. Finally, the inductance parameters were accurately identified using the parameter identification algorithm, reducing the dependence of the MPCC system on motor parameters. [Results] The improved dual three-phase PMSM MPCC system incorporating the ST-SMO and incremental prediction model demonstrated excellent steady-state and dynamic performance. For parameter identification, it eliminated the chattering phenomenon caused by the first-order sliding mode system, improved the accuracy of parameter identification, and enhanced the identification speed of motor parameters. Additionally, the control system maintained high control performance for the dual three-phase PMSM under parameter mismatch conditions. [Conclusion] The MPCC system based on ST-SMO parameter identification proposed in this study demonstrates good feasibility and stability under various operating conditions, such as speed and torque sudden transients.
11 Low-Complexity Three-Vector Model Predictive Current Control for Permanent Magnet Synchronous Motors
2025, 52(5):572-584.
DOI: 10.12177/emca.2025.029
Abstract:
[Objective] To address computational complexity issues in traditional three-vector model predictive current control (TV-MPCC) for permanent magnet synchronous motor (PMSM), a low-complexity TV-MPCC (LCTV-MPCC) method based on improved voltage vector selection and calculation is proposed. [Methods] Firstly, a discrete current prediction model for PMSM was established based on the forward Euler method, and three candidate vectors were selected using an adjacent effective voltage vector screening method to narrow down the voltage vector selection range. Then, the value function was calculated to determine the optimal voltage vector combinations, and their corresponding virtual action time was calculated. The desired voltage vector was synthesized using two virtual voltage vectors and their action time. Based on the duration of virtual voltage vector action time, the actual voltage vectors and their action time required for synthesizing the desired voltage vector were further determined. Finally, the inverter switching sequence output was generated according to the principle of minimum switching frequency. [Results] To verify the effectiveness of the proposed method, a comparative analysis was conducted through simulations and experiments. The results showed that the proposed LCTV-MPCC method achieved comparable steady-state performance to the TV-MPCC method, with nearly identical speed and current fluctuations under both control methods. In terms of calculation efficiency, the proposed LCTV-MPCC method reduced the average calculation time per cycle from 54.3 μs to 37.6 μs, which was a reduction of 30.76% compared to TV-MPCC. Additionally, the number of voltage vector iterations decreased from 6 to 3, representing a 50% reduction. The proposed method effectively reduced the computational burden compared to traditional control methods. [Conclusion] The LCTV-MPCC method proposed in this study effectively addresses the computational complexity issue of the TV-MPCC method by modifying the candidate voltage vector set and the calculation method for actual voltage vector action time. This method reduces average cycle runtime of the system, maintains fixed switching frequency, and ensures unchanged steady-state control performance of motors.
[Objective] To address computational complexity issues in traditional three-vector model predictive current control (TV-MPCC) for permanent magnet synchronous motor (PMSM), a low-complexity TV-MPCC (LCTV-MPCC) method based on improved voltage vector selection and calculation is proposed. [Methods] Firstly, a discrete current prediction model for PMSM was established based on the forward Euler method, and three candidate vectors were selected using an adjacent effective voltage vector screening method to narrow down the voltage vector selection range. Then, the value function was calculated to determine the optimal voltage vector combinations, and their corresponding virtual action time was calculated. The desired voltage vector was synthesized using two virtual voltage vectors and their action time. Based on the duration of virtual voltage vector action time, the actual voltage vectors and their action time required for synthesizing the desired voltage vector were further determined. Finally, the inverter switching sequence output was generated according to the principle of minimum switching frequency. [Results] To verify the effectiveness of the proposed method, a comparative analysis was conducted through simulations and experiments. The results showed that the proposed LCTV-MPCC method achieved comparable steady-state performance to the TV-MPCC method, with nearly identical speed and current fluctuations under both control methods. In terms of calculation efficiency, the proposed LCTV-MPCC method reduced the average calculation time per cycle from 54.3 μs to 37.6 μs, which was a reduction of 30.76% compared to TV-MPCC. Additionally, the number of voltage vector iterations decreased from 6 to 3, representing a 50% reduction. The proposed method effectively reduced the computational burden compared to traditional control methods. [Conclusion] The LCTV-MPCC method proposed in this study effectively addresses the computational complexity issue of the TV-MPCC method by modifying the candidate voltage vector set and the calculation method for actual voltage vector action time. This method reduces average cycle runtime of the system, maintains fixed switching frequency, and ensures unchanged steady-state control performance of motors.
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Electric Machines & Control Application ® 2025
Supported by:Beijing E-Tiller Technology Development Co., Ltd.
沪ICP备16038578号-3
Electric Machines & Control Application ® 2025
Supported by:Beijing E-Tiller Technology Development Co., Ltd.
