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Volume 52,Issue 10,2025 Table of Contents
2025, 52(10):1039-1049.
DOI: 10.12177/emca.2025.104
Abstract:
[Objective] Harmonic current injection is an effective approach for suppressing electromagnetic vibration and noise in permanent magnet synchronous motor (PMSM). However, traditional methods face difficulties in determining the parameters of harmonic currents and neglect the coupling between the radial electromagnetic force and torque. [Methods] Firstly, a 10-pole 60-slot PMSM was taken as the research object in this paper. Considering the slotting effect, the dominant electromagnetic force component was identified as the 0-order 12-time frequency through two-dimensional fast Fourier transform, and its sources were analyzed. Subsequently, the coupling mechanism between harmonic currents and radial electromagnetic forces was analyzed. To address the strong interaction between harmonic currents and electromagnetic performance, a multi-objective optimization model was formulated that considers vibration suppression and torque performance. The amplitudes and phases of the harmonic currents were optimized using multi-objective genetic algorithm (MOGA). Finally, a multi-physics simulation model incorporating electromagnetic-structural coupling was developed to validate the vibration responses of the motor. [Results] The results demonstrated that the armature reaction slot harmonics and permanent magnet field interactions were the dominant contributors to radial electromagnetic forces. Further experimental verification showed that injecting harmonic currents with MOGA-optimized amplitudes and phases achieved a 12.75% reduction in motor vibration acceleration and a 2.61% decrease in torque ripple. These improvements validated both the effectiveness of the optimization algorithm and the feasibility of the harmonic current injection strategy for vibration suppression. [Conclusion] The MOGA-based harmonic current parameter optimization method proposed in this paper achieves an effective trade-off between vibration suppression and torque performance enhancement, providing both theoretical insights and practical references for electromagnetic force mechanism analysis and active suppression strategies.
[Objective] Harmonic current injection is an effective approach for suppressing electromagnetic vibration and noise in permanent magnet synchronous motor (PMSM). However, traditional methods face difficulties in determining the parameters of harmonic currents and neglect the coupling between the radial electromagnetic force and torque. [Methods] Firstly, a 10-pole 60-slot PMSM was taken as the research object in this paper. Considering the slotting effect, the dominant electromagnetic force component was identified as the 0-order 12-time frequency through two-dimensional fast Fourier transform, and its sources were analyzed. Subsequently, the coupling mechanism between harmonic currents and radial electromagnetic forces was analyzed. To address the strong interaction between harmonic currents and electromagnetic performance, a multi-objective optimization model was formulated that considers vibration suppression and torque performance. The amplitudes and phases of the harmonic currents were optimized using multi-objective genetic algorithm (MOGA). Finally, a multi-physics simulation model incorporating electromagnetic-structural coupling was developed to validate the vibration responses of the motor. [Results] The results demonstrated that the armature reaction slot harmonics and permanent magnet field interactions were the dominant contributors to radial electromagnetic forces. Further experimental verification showed that injecting harmonic currents with MOGA-optimized amplitudes and phases achieved a 12.75% reduction in motor vibration acceleration and a 2.61% decrease in torque ripple. These improvements validated both the effectiveness of the optimization algorithm and the feasibility of the harmonic current injection strategy for vibration suppression. [Conclusion] The MOGA-based harmonic current parameter optimization method proposed in this paper achieves an effective trade-off between vibration suppression and torque performance enhancement, providing both theoretical insights and practical references for electromagnetic force mechanism analysis and active suppression strategies.
LI Yaohua
,
GUO Weichao
,
WANG Qinzheng
,
WANG Zichen
,
GAO Sai
,
CHONG Guochen
,
XU Zhixiong
,
LIU Yahui
,
ZHANG Qian
,
HUANG Hanxuan
2025, 52(10):1050-1062.
DOI: 10.12177/emca.2025.098
Abstract:
[Objective] To solve the parameter mismatch problem of model predictive current control (MPCC) for permanent magnet synchronous motor (PMSM), incremental MPCC with model reference adaptive system (MRAS) is used for parameters identification in order to improve the parameter robustness of the control system. [Methods] Firstly, the MPCC system based on the incremental model was established to eliminate the flux linkage parameter, and the parameter sensitivity of the incremental MPCC model was also analyzed, in which the resistance parameter mismatch had a small and negligible influence on the current prediction effect. Therefore, based on the dq-axis current, MRAS was used to identify only the dq-axis inductance, thus avoiding the under-rank problem. [Results] Simulation analysis and real-time verification were carried out based on Matlab/Simulink and STM32 platform. The results showed that MRAS could accurately identify the dq-axis inductance parameters. And although the incremental MPCC and MRAS control models need to set the resistance parameter, the mismatch of the resistance parameter had less impact on the identification results of the dq-axis inductance. The added MRAS parameter identification system did not additionally increase the large computational burden, and it had a better real-time performance. [Conclusion] For the four parameters of dq-axis inductance, flux linkage and resistance of PMSM, the incremental MPCC eliminates the flux linkage parameter, while the resistance parameter does not need to be identified precisely, and only the dq-axis inductance need to be identified by MRAS. The mismatch of the resistance parameter of the MRAS control model and the incremental MPCC has no effect on the control performance, which effectively improves the MPCC parameter mismatch robustness of PMSM.
[Objective] To solve the parameter mismatch problem of model predictive current control (MPCC) for permanent magnet synchronous motor (PMSM), incremental MPCC with model reference adaptive system (MRAS) is used for parameters identification in order to improve the parameter robustness of the control system. [Methods] Firstly, the MPCC system based on the incremental model was established to eliminate the flux linkage parameter, and the parameter sensitivity of the incremental MPCC model was also analyzed, in which the resistance parameter mismatch had a small and negligible influence on the current prediction effect. Therefore, based on the dq-axis current, MRAS was used to identify only the dq-axis inductance, thus avoiding the under-rank problem. [Results] Simulation analysis and real-time verification were carried out based on Matlab/Simulink and STM32 platform. The results showed that MRAS could accurately identify the dq-axis inductance parameters. And although the incremental MPCC and MRAS control models need to set the resistance parameter, the mismatch of the resistance parameter had less impact on the identification results of the dq-axis inductance. The added MRAS parameter identification system did not additionally increase the large computational burden, and it had a better real-time performance. [Conclusion] For the four parameters of dq-axis inductance, flux linkage and resistance of PMSM, the incremental MPCC eliminates the flux linkage parameter, while the resistance parameter does not need to be identified precisely, and only the dq-axis inductance need to be identified by MRAS. The mismatch of the resistance parameter of the MRAS control model and the incremental MPCC has no effect on the control performance, which effectively improves the MPCC parameter mismatch robustness of PMSM.
2025, 52(10):1063-1074.
DOI: 10.12177/emca.2025.096
Abstract:
[Objective] This study addresses the limitations of conventional model predictive current control (MPCC) for doubly salient electromagnetic machine, including excessive current fluctuations and strong parameter dependency. An improved dual-vector model predictive current control (IDV-MPCC) strategy integrated with online parameter identification is proposed to enhance control robustness. [Methods] The proposed strategy achieved rapid sector localization of vectors, reduced the number of optimizations from 18 in traditional exhaustive search algorithms to just 4. This was accomplished by redefining the sectors and providing a new table for selecting vector combinations, which significantly reduced current prediction errors and ripples. To address parameter sensitivity issues, the model reference adaptive system (MRAS) was employed to perform online identification of the self-inductance and mutual inductance of the armature windings, and the identified results were fed back into the prediction model in real-time to correct it, thereby enhancing system robustness. [Results] The IDV-MPCC method significantly reduced current harmonics and torque ripples, while the MRAS-based identification effectively suppressed prediction errors caused by parameter mismatches. [Conclusion] The experimental results have verified that the proposed MRAS-IDV-MPCC method can effectively improve the control performance while reducing the computational burden of the system.
[Objective] This study addresses the limitations of conventional model predictive current control (MPCC) for doubly salient electromagnetic machine, including excessive current fluctuations and strong parameter dependency. An improved dual-vector model predictive current control (IDV-MPCC) strategy integrated with online parameter identification is proposed to enhance control robustness. [Methods] The proposed strategy achieved rapid sector localization of vectors, reduced the number of optimizations from 18 in traditional exhaustive search algorithms to just 4. This was accomplished by redefining the sectors and providing a new table for selecting vector combinations, which significantly reduced current prediction errors and ripples. To address parameter sensitivity issues, the model reference adaptive system (MRAS) was employed to perform online identification of the self-inductance and mutual inductance of the armature windings, and the identified results were fed back into the prediction model in real-time to correct it, thereby enhancing system robustness. [Results] The IDV-MPCC method significantly reduced current harmonics and torque ripples, while the MRAS-based identification effectively suppressed prediction errors caused by parameter mismatches. [Conclusion] The experimental results have verified that the proposed MRAS-IDV-MPCC method can effectively improve the control performance while reducing the computational burden of the system.
4 Open-Winding Brushless DC Motor Model Predictive Torque Control Based on the Extended Voltage Vector
2025, 52(10):1075-1085.
DOI: 10.12177/emca.2025.092
Abstract:
[Objective] To address the issue of large torque ripple in the direct torque control (DTC) system of brushless DC motor (BLDCM), this paper proposes a model predictive torque control (MPTC) method based on the open-winding brushless DC motor (OW-BLDCM). [Methods] First, a mathematical model of the OW-BLDCM was established, and the traditional voltage vector set was expanded by introducing a class of single-phase conducting voltage vectors to improve voltage adaptability. Next, based on the conventional DTC switching vector table, when torque needed to be increased, large vectors and single-phase vectors from the corresponding sector were used to increase torque. When torque needed to be reduced, in addition to the corresponding sector’s voltage vector, a model predictive control method was applied to roll and optimize the vector set, selecting the most effective voltage vector. Furthermore, simulation software was used to validate the proposed control method and the motor topology in terms of torque ripple suppression, observing the control effectiveness of torque and other parameters. Finally, physical experiments were conducted on an OW-BLDCM DTC platform. [Results] The results showed that the proposed MPTC method, compared to traditional DTC, achieved a 4.1% improvement in torque ripple suppression under rated operating conditions. The suppression of torque ripple was significantly enhanced at moderate and low speeds. Additionally, the proposed method in this study inherited the fast dynamic response characteristic, with a response time of only 1.706 ms, which was virtually identical to the 1.209 ms response time of DTC. [Conclusion] This study demonstrates that the proposed MPTC method for OW-BLDCM effectively reduces torque ripple and enhances control flexibility, providing a new approach for the efficient control of OW-BLDCM.
[Objective] To address the issue of large torque ripple in the direct torque control (DTC) system of brushless DC motor (BLDCM), this paper proposes a model predictive torque control (MPTC) method based on the open-winding brushless DC motor (OW-BLDCM). [Methods] First, a mathematical model of the OW-BLDCM was established, and the traditional voltage vector set was expanded by introducing a class of single-phase conducting voltage vectors to improve voltage adaptability. Next, based on the conventional DTC switching vector table, when torque needed to be increased, large vectors and single-phase vectors from the corresponding sector were used to increase torque. When torque needed to be reduced, in addition to the corresponding sector’s voltage vector, a model predictive control method was applied to roll and optimize the vector set, selecting the most effective voltage vector. Furthermore, simulation software was used to validate the proposed control method and the motor topology in terms of torque ripple suppression, observing the control effectiveness of torque and other parameters. Finally, physical experiments were conducted on an OW-BLDCM DTC platform. [Results] The results showed that the proposed MPTC method, compared to traditional DTC, achieved a 4.1% improvement in torque ripple suppression under rated operating conditions. The suppression of torque ripple was significantly enhanced at moderate and low speeds. Additionally, the proposed method in this study inherited the fast dynamic response characteristic, with a response time of only 1.706 ms, which was virtually identical to the 1.209 ms response time of DTC. [Conclusion] This study demonstrates that the proposed MPTC method for OW-BLDCM effectively reduces torque ripple and enhances control flexibility, providing a new approach for the efficient control of OW-BLDCM.
5 Optimization Design of the Electric Propulsion System for a Tethered High-Payload Firefighting UAV
2025, 52(10):1086-1096.
DOI: 10.12177/emca.2025.100
Abstract:
[Objective] Tethered heavy-payload firefighting UAVs performing sustained hovering at 200 m altitude face critical challenges: Insufficient power density, excessive winding temperature rise, and inadequate 100-hour operational reliability. Addressing performance limitations in conventional external-rotor permanent magnet synchronous motor-specifically high AC copper losses from round-wire windings and low air-gap flux density due to parallel magnetization, this study proposes an integrated solution combining multiphysics collaborative optimization with innovative topological design. The approach targets stringent triple requirements for electric propulsion systems: lightweight construction, minimized heat dissipation, and superior operational robustness in aerial firefighting scenarios. [Methods] Using a multi-parameter sensitivity hierarchical degradation model validated for predictive accuracy, parameter co-optimization for an external-rotor motor with 36 slot/32 pole was performed via response surface methodology. The design incorporated a five-segment Halbach magnet array topology to enhance fundamental air-gap flux density while suppressing torque ripple through optimized magnetization angle distribution. This was combined with flat-wire winding technology and a dovetail-groove stator design, achieving a 78% slot fill factor and reducing AC copper losses via three-dimensional end-turn optimization. Pareto-optimal solutions balancing thermal stability and efficiency were selected using a genetic algorithm based on a composite objective function. Prototypes underwent rigorous validation through propeller dynamometer tests simulating full-load aerodynamic profiles and flight demonstrations at 200 m altitude under variable atmospheric conditions. [Results] The optimized motor achieved a power density of 3.73 kW/kg with 92.9% system efficiency under rated propeller load. Winding temperature rise stabilized at 118 K, the electric drive system operated stably during the 200 m altitude flight test. Following 100 hours of continuous flight, efficiency degradation remained below 0.1%, outperforming similar products. [Conclusion] The hierarchical optimization framework synergized with Halbach-flat wire winding technology, achieving unprecedented power density and operational reliability essential for prolonged firefighting missions, while offering a scalable methodology for high-altitude electric propulsion design.
[Objective] Tethered heavy-payload firefighting UAVs performing sustained hovering at 200 m altitude face critical challenges: Insufficient power density, excessive winding temperature rise, and inadequate 100-hour operational reliability. Addressing performance limitations in conventional external-rotor permanent magnet synchronous motor-specifically high AC copper losses from round-wire windings and low air-gap flux density due to parallel magnetization, this study proposes an integrated solution combining multiphysics collaborative optimization with innovative topological design. The approach targets stringent triple requirements for electric propulsion systems: lightweight construction, minimized heat dissipation, and superior operational robustness in aerial firefighting scenarios. [Methods] Using a multi-parameter sensitivity hierarchical degradation model validated for predictive accuracy, parameter co-optimization for an external-rotor motor with 36 slot/32 pole was performed via response surface methodology. The design incorporated a five-segment Halbach magnet array topology to enhance fundamental air-gap flux density while suppressing torque ripple through optimized magnetization angle distribution. This was combined with flat-wire winding technology and a dovetail-groove stator design, achieving a 78% slot fill factor and reducing AC copper losses via three-dimensional end-turn optimization. Pareto-optimal solutions balancing thermal stability and efficiency were selected using a genetic algorithm based on a composite objective function. Prototypes underwent rigorous validation through propeller dynamometer tests simulating full-load aerodynamic profiles and flight demonstrations at 200 m altitude under variable atmospheric conditions. [Results] The optimized motor achieved a power density of 3.73 kW/kg with 92.9% system efficiency under rated propeller load. Winding temperature rise stabilized at 118 K, the electric drive system operated stably during the 200 m altitude flight test. Following 100 hours of continuous flight, efficiency degradation remained below 0.1%, outperforming similar products. [Conclusion] The hierarchical optimization framework synergized with Halbach-flat wire winding technology, achieving unprecedented power density and operational reliability essential for prolonged firefighting missions, while offering a scalable methodology for high-altitude electric propulsion design.
2025, 52(10):1097-1107.
DOI: 10.12177/emca.2025.099
Abstract:
[Objective] In response to the challenges associated with the identification of multiple parameters in permanent magnet synchronous motor (PMSM), which are characterized by difficulties and low accuracy, an improved tuna swarm optimization (TSO) algorithm has been proposed. This algorithm is designed to simultaneously identify several parameters of the PMSM. [Methods] Firstly, the Latin hypercube sampling (LHS) method was employed to initialize the tuna population, effectively circumvented the issue of initial aggregation of the tuna population caused by random initialization. This approach enhanced the diversity and uniformity of the initial population. Subsequently, to enhance the algorithm’s optimization performance across different stages of iteration, a dynamic nonlinear weight adjustment strategy was adopted. This strategy equipped the algorithm with stronger global search capabilities during the early stages of iteration, allowed for a broader exploration of the search space, and stronger local search capabilities during later stages, facilitated precise convergence to the optimal solution. Finally, a Gaussian mutation strategy was implemented, enabling the algorithm to effectively explore new solution spaces during the optimization process. This strategy mitigated the tendency of the algorithm to become trapped in local optima, thereby improved the convergence accuracy of the TSO algorithm. [Results] To validate the effectiveness of the proposed method, simulations and experimental analyses were conducted on both simulation software and motor platform. The results of both simulation and experiment demonstrated that the improved TSO algorithm, which incorporated LHS, dynamic nonlinear weight adjustment, and Gaussian mutation, outperforms particle swarm optimization algorithms and the original TSO algorithm in the identification process of PMSM resistance, inductance, and magnetic flux linkage. It achieved faster convergence speeds and higher convergence accuracy, with all parameter identification errors controlled within a range of 0.89%. [Conclusion] The improved TSO algorithm is capable of effectively synchronizing the identification of resistance, inductance, and magnetic flux linkage in PMSM. It exhibits superior identification accuracy and favorable convergence characteristics.
[Objective] In response to the challenges associated with the identification of multiple parameters in permanent magnet synchronous motor (PMSM), which are characterized by difficulties and low accuracy, an improved tuna swarm optimization (TSO) algorithm has been proposed. This algorithm is designed to simultaneously identify several parameters of the PMSM. [Methods] Firstly, the Latin hypercube sampling (LHS) method was employed to initialize the tuna population, effectively circumvented the issue of initial aggregation of the tuna population caused by random initialization. This approach enhanced the diversity and uniformity of the initial population. Subsequently, to enhance the algorithm’s optimization performance across different stages of iteration, a dynamic nonlinear weight adjustment strategy was adopted. This strategy equipped the algorithm with stronger global search capabilities during the early stages of iteration, allowed for a broader exploration of the search space, and stronger local search capabilities during later stages, facilitated precise convergence to the optimal solution. Finally, a Gaussian mutation strategy was implemented, enabling the algorithm to effectively explore new solution spaces during the optimization process. This strategy mitigated the tendency of the algorithm to become trapped in local optima, thereby improved the convergence accuracy of the TSO algorithm. [Results] To validate the effectiveness of the proposed method, simulations and experimental analyses were conducted on both simulation software and motor platform. The results of both simulation and experiment demonstrated that the improved TSO algorithm, which incorporated LHS, dynamic nonlinear weight adjustment, and Gaussian mutation, outperforms particle swarm optimization algorithms and the original TSO algorithm in the identification process of PMSM resistance, inductance, and magnetic flux linkage. It achieved faster convergence speeds and higher convergence accuracy, with all parameter identification errors controlled within a range of 0.89%. [Conclusion] The improved TSO algorithm is capable of effectively synchronizing the identification of resistance, inductance, and magnetic flux linkage in PMSM. It exhibits superior identification accuracy and favorable convergence characteristics.
2025, 52(10):1108-1114.
DOI: 10.12177/emca.2025.103
Abstract:
[Objective] To address the demands for real-time performance, reliability, and interactivity of embedded graphical interfaces in industrial control-particularly the high-precision and rapid response requirements for real-time parameter monitoring (e.g., current, voltage, temperature, vibration) in motor protectors and test systems within the electrical machinery domain, this paper aims to resolve the issues of lengthy development cycles and poor flexibility associated with traditional dedicated human-machine interface by proposing an efficient graphical user interface (GUI) solution based on Qt/Embedded. [Methods] The implementation utilized an ARM Cortex-A5 based IAC-A5D3x-Kit development board as the hardware platform. After comprehensive evaluation of various GUI frameworks including Microwindows and MiniGUI, Qt/Embedded was selected as the optimal development framework due to its superior performance characteristics. The development process adopted a host-target cross-compilation approach, successfully completing Linux 3.6.9 kernel configuration, Qt 4.8.2 library porting, and USB/VGA driver development. [Results] The test results showed that the system supported keyboard, mouse, and VGA display interactions, with a real-time monitoring interface refresh delay of less than 1 second and file copy efficiency reached 4.3 MB/s. These met the performance requirements of industrial control scenarios, validated the system’s high reliability and efficiency in dynamic data visualization and file operations. [Conclusion] By integrating Qt/Embedded with a customized Linux kernel, the system achieves high reliability and efficient human-machine interaction. This GUI solution provides an extensible framework that effectively supports industrial automation needs. The research not only contributes valuable insights and methodologies but also demonstrates broader application prospects and potential for further innovation in automation systems.
[Objective] To address the demands for real-time performance, reliability, and interactivity of embedded graphical interfaces in industrial control-particularly the high-precision and rapid response requirements for real-time parameter monitoring (e.g., current, voltage, temperature, vibration) in motor protectors and test systems within the electrical machinery domain, this paper aims to resolve the issues of lengthy development cycles and poor flexibility associated with traditional dedicated human-machine interface by proposing an efficient graphical user interface (GUI) solution based on Qt/Embedded. [Methods] The implementation utilized an ARM Cortex-A5 based IAC-A5D3x-Kit development board as the hardware platform. After comprehensive evaluation of various GUI frameworks including Microwindows and MiniGUI, Qt/Embedded was selected as the optimal development framework due to its superior performance characteristics. The development process adopted a host-target cross-compilation approach, successfully completing Linux 3.6.9 kernel configuration, Qt 4.8.2 library porting, and USB/VGA driver development. [Results] The test results showed that the system supported keyboard, mouse, and VGA display interactions, with a real-time monitoring interface refresh delay of less than 1 second and file copy efficiency reached 4.3 MB/s. These met the performance requirements of industrial control scenarios, validated the system’s high reliability and efficiency in dynamic data visualization and file operations. [Conclusion] By integrating Qt/Embedded with a customized Linux kernel, the system achieves high reliability and efficient human-machine interaction. This GUI solution provides an extensible framework that effectively supports industrial automation needs. The research not only contributes valuable insights and methodologies but also demonstrates broader application prospects and potential for further innovation in automation systems.
2025, 52(10):1115-1124.
DOI: 10.12177/emca.2025.095
Abstract:
[Objective] Permanent magnet synchronous motor (PMSM) is widely adopted in industrial applications due to their high power density, superior efficiency, and excellent dynamic performance. However, conventional control strategies, often exhibit degraded performance when confronted with system parameter variations and external disturbances. To address these challenges, this paper proposes a novel sliding mode variable structure control (SMVSC) strategy optimized by the marine predators algorithm (MPA), aiming to enhance system robustness and steady-state accuracy. [Methods] Within the vector control framework of PMSM, the conventional proportional integral controller in the speed outer loop was replaced with SMVSC controller. The inherent robustness of SMVSC was leveraged to mitigate the adverse effects of parameter uncertainties and external disturbances. To further refine the SMVSC design, a double-power reaching law was introduced to optimize the approaching process. During the initial phase of converging to the sliding mode surface, a high gain was employed to accelerate convergence speed. Subsequently, the gain was dynamically reduced in the later phase to suppress chattering phenomena, thereby balancing rapid dynamic response with control smoothness. Additionally, the MPA was utilized to optimize critical parameters of the SMVSC, including reaching rate coefficients and sliding surface parameters, avoided local optima and ensures the adaptability of controller parameters under diverse operating conditions. Finally, the simulation model of PMSM speed regulation system was built by Matlab/Simulink, and experiments were carried out on the physical platform, verified the effectiveness of the proposed method. [Results] Simulation and experimental results showed that the MPA-optimized double-power reaching law SMVSC significantly improves PMSM speed regulation performance, with enhanced stability, reduced overshoot, and shorter settling time. Compared to traditional methods, the optimized controller exhibits superior dynamic response and robustness under varying conditions. [Conclusion] The proposed MPA-optimized SMVSC strategy effectively enhances PMSM control performance under parameter uncertainties and external disturbances, offering a new solution for high-performance of PMSM.
[Objective] Permanent magnet synchronous motor (PMSM) is widely adopted in industrial applications due to their high power density, superior efficiency, and excellent dynamic performance. However, conventional control strategies, often exhibit degraded performance when confronted with system parameter variations and external disturbances. To address these challenges, this paper proposes a novel sliding mode variable structure control (SMVSC) strategy optimized by the marine predators algorithm (MPA), aiming to enhance system robustness and steady-state accuracy. [Methods] Within the vector control framework of PMSM, the conventional proportional integral controller in the speed outer loop was replaced with SMVSC controller. The inherent robustness of SMVSC was leveraged to mitigate the adverse effects of parameter uncertainties and external disturbances. To further refine the SMVSC design, a double-power reaching law was introduced to optimize the approaching process. During the initial phase of converging to the sliding mode surface, a high gain was employed to accelerate convergence speed. Subsequently, the gain was dynamically reduced in the later phase to suppress chattering phenomena, thereby balancing rapid dynamic response with control smoothness. Additionally, the MPA was utilized to optimize critical parameters of the SMVSC, including reaching rate coefficients and sliding surface parameters, avoided local optima and ensures the adaptability of controller parameters under diverse operating conditions. Finally, the simulation model of PMSM speed regulation system was built by Matlab/Simulink, and experiments were carried out on the physical platform, verified the effectiveness of the proposed method. [Results] Simulation and experimental results showed that the MPA-optimized double-power reaching law SMVSC significantly improves PMSM speed regulation performance, with enhanced stability, reduced overshoot, and shorter settling time. Compared to traditional methods, the optimized controller exhibits superior dynamic response and robustness under varying conditions. [Conclusion] The proposed MPA-optimized SMVSC strategy effectively enhances PMSM control performance under parameter uncertainties and external disturbances, offering a new solution for high-performance of PMSM.
9 CFD-based Blade Optimisation and System Energy Design for Vertical Axis Wind Turbine Characteristics
2025, 52(10):1125-1136.
DOI: 10.12177/emca.2025.094
Abstract:
[Objective] Aiming at the problem of inefficient utilization of wind resources in small-scale distributed energy systems, this paper proposes and studies the performance characteristics of an improved drag-type vertical axis wind turbine (VAWT). [Methods] Based on the traditional Savonius wind turbine, the starting ability and power generation efficiency of the wind turbine were improved by increasing the number of fan blades and optimizing the design of blade torsion. The simulation analysis of the wind turbine structure was carried out by using the CFD method, and the fluid-structure coupling behavior of the wind turbine was discussed from the aerodynamic point of view at different twist angles and starting wind speeds, with emphasis on the analysis of the pressure distribution, the change of the flow field and the force characteristics. On this basis, combined with the torque characteristics of the wind turbine, the mathematical model of the wind turbine system was constructed using Matlab, and the sliding mode direct torque control method was introduced to realize the dynamic simulation of the power generation process. [Results] By analyzing the key power generation characteristic curves such as voltage and current, and comparing and verifying them with the measured data, the results showed that the established model had good accuracy and engineering applicability. [Conclusion] The research results provide theoretical support and practical reference for the structural optimization and intelligent control of resistance-type vertical-axis wind turbine, which is of great significance for promoting the application of small wind power generation equipment in distributed energy systems.
[Objective] Aiming at the problem of inefficient utilization of wind resources in small-scale distributed energy systems, this paper proposes and studies the performance characteristics of an improved drag-type vertical axis wind turbine (VAWT). [Methods] Based on the traditional Savonius wind turbine, the starting ability and power generation efficiency of the wind turbine were improved by increasing the number of fan blades and optimizing the design of blade torsion. The simulation analysis of the wind turbine structure was carried out by using the CFD method, and the fluid-structure coupling behavior of the wind turbine was discussed from the aerodynamic point of view at different twist angles and starting wind speeds, with emphasis on the analysis of the pressure distribution, the change of the flow field and the force characteristics. On this basis, combined with the torque characteristics of the wind turbine, the mathematical model of the wind turbine system was constructed using Matlab, and the sliding mode direct torque control method was introduced to realize the dynamic simulation of the power generation process. [Results] By analyzing the key power generation characteristic curves such as voltage and current, and comparing and verifying them with the measured data, the results showed that the established model had good accuracy and engineering applicability. [Conclusion] The research results provide theoretical support and practical reference for the structural optimization and intelligent control of resistance-type vertical-axis wind turbine, which is of great significance for promoting the application of small wind power generation equipment in distributed energy systems.
2025, 52(10):1137-1147.
DOI: 10.12177/emca.2025.102
Abstract:
[Objective] Aiming at the problem that the single vector model predictive current control (MPCC) of the non-common bus open-winding permanent magnet synchronous motor with DC bus voltage ratio of 3∶1 needs to traverse all voltage vectors in the control set, which leads to a large computational burden, a finite control set predictive current control (FCS-MPC) strategy based on deadbeat control is proposed. By subdividing sectors, simplifying the control set, reducing the number of voltage vector traversals, and improving real-time performance. [Methods] Firstly, the position of the voltage vector corresponding to the sector was judged by establishing the sector discriminant. According to the voltage vector amplitude, the corresponding sector was divided into two sub-sectors. So as to simplify the control set while avoided the introduction of angle operation to increase the amount of calculation, affected the accuracy of judgment, and improved the computational efficiency. Then, the error between the reference value and the actual value of the dq axis current of the motor stator was used as the value function to continuously optimize and select the optimal voltage vector. [Results] To verify the effectiveness of the proposed FCS-MPC strategy, a comparative analysis was conducted through simulation. The results showed that the proposed method was similar to the traditional MPCC method in terms of steady-state performance, and the rotational speed fluctuation and current fluctuation of the two control methods were basically the same. In terms of computational efficiency, compared with the traditional MPCC method, the iteration times of the voltage vector of the proposed MPCC method had been reduced from 49 times to 4~5 times, a reduction of 50%. The proposed method could effectively reduce the computational burden of the traditional control method. [Conclusion] The simulation results show that the proposed method has a faster response speed and a shorter time than the traditional control method.
[Objective] Aiming at the problem that the single vector model predictive current control (MPCC) of the non-common bus open-winding permanent magnet synchronous motor with DC bus voltage ratio of 3∶1 needs to traverse all voltage vectors in the control set, which leads to a large computational burden, a finite control set predictive current control (FCS-MPC) strategy based on deadbeat control is proposed. By subdividing sectors, simplifying the control set, reducing the number of voltage vector traversals, and improving real-time performance. [Methods] Firstly, the position of the voltage vector corresponding to the sector was judged by establishing the sector discriminant. According to the voltage vector amplitude, the corresponding sector was divided into two sub-sectors. So as to simplify the control set while avoided the introduction of angle operation to increase the amount of calculation, affected the accuracy of judgment, and improved the computational efficiency. Then, the error between the reference value and the actual value of the dq axis current of the motor stator was used as the value function to continuously optimize and select the optimal voltage vector. [Results] To verify the effectiveness of the proposed FCS-MPC strategy, a comparative analysis was conducted through simulation. The results showed that the proposed method was similar to the traditional MPCC method in terms of steady-state performance, and the rotational speed fluctuation and current fluctuation of the two control methods were basically the same. In terms of computational efficiency, compared with the traditional MPCC method, the iteration times of the voltage vector of the proposed MPCC method had been reduced from 49 times to 4~5 times, a reduction of 50%. The proposed method could effectively reduce the computational burden of the traditional control method. [Conclusion] The simulation results show that the proposed method has a faster response speed and a shorter time than the traditional control method.
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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.
