Optimization three-vector-based model predictive current control for permanent magnet toroidal motor
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摘要:
为提高传统预测电流控制下永磁超环面电机系统的稳态输出性能，研究了永磁超环面电机优化三矢量模型预测电流控制(OTV-MPCC)策略。基于永磁超环面电机的结构原理，在旋转坐标系下建立该电机具有时变参数的数学模型，分析结构参数对电机输出的影响。针对含有时变参数的永磁超环面电机系统，采用永磁超环面电机OTV-MPCC策略，在一个采样周期内作用3个电压矢量，同时通过遍历5种三矢量组合选取第二最优电压矢量，减少电流预测迭代。将永磁超环面电机OTV-MPCC策略与占空比模型预测电流控制(DR-MPCC)策略、双矢量模型预测电流控制(TV-MPCC)策略进行仿真对比，结果表明：OTV-MPCC策略可有效降低永磁超环面电机的电流与转矩脉动，提高稳态输出性能。
Abstract:To improve the steady-state output performance of permanent magnet toroidal motor system with traditional predictive current control, the optimization three-vector-based model predictive current control (OTV-MPCC) strategy for toroidal motor was studied in this paper. A mathematical model of a toroidal motor with time-varying parameters was created in a rotating coordinate system, based on the structural principle of a toroidal motor. The influence of structural parameters on output was analyzed for toroidal motor. For toroidal motor system with time-varying parameters, the OTV-MPCC strategy was adopted. Three voltage vectors were applied in one sampling period. In the meantime, the current prediction iteration is decreased by selecting the second optimal voltage vector through a traversal of five groups of three vector combinations. Three strategies for toroidal motors were simulated and compared: the OTV-MPCC approach, the duty ratio model predictive current control (DR-MPCC) strategy, and the two-vector-based model predictive current control (TV-MPCC) method. The research results show that OTV-MPCC strategy can reduce current and torque ripple for toroidal motor effectively, and it can improve steady-state output performance.
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图 7 永磁超环面电机OTV-MPCC系统结构框图
Figure 7. Block diagram of OTV-MPCC system for permanent magnet toroidal motor
图 8 永磁超环面电机OTV-MPCC系统流程
Figure 8. Flow diagram of OTV-MPCC system for permanent magnet toroidal motor
图 13 不同转速时3种控制策略下的电流脉动
Figure 13. Current ripples with three control strategies at different speeds
表 1 电压矢量选择
Table 1. Voltage vector selection
Uopx Uy Uz U1 U2, U3, U4, U5, U6 U0 或 U7 U2 U1, U3, U4, U5, U6 U0 或 U7 U3 U1, U2, U4, U5, U6 U0 或 U7 U4 U1, U2, U3, U5, U6 U0 或 U7 U5 U1, U2, U3, U4, U6 U0 或 U7 U6 U1, U2, U3, U4, U5 U0 或 U7 
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表 2 控制策略的比较
Table 2. Comparison of control strategies
控制策略 电压矢量
数目电流预测
次数电压矢量的
选择范围作用时间的
计算方式DR-MPCC 2 6 方向固定
幅值可调q轴电流
无差拍TV-MPCC 2 15 方向可调
幅值可调q轴电流
无差拍OTV-MPCC 3 11 方向可调
幅值可调d-q轴电流
无差拍
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表 3 永磁超环面电机参数
Table 3. Parameters of permanent magnet toroidal motor
参数 数值 R/Ω 3.8 np1 4 z3 28 z2 12 φv/(°) 90 k1 0.3 k2 5 B/(N·m·s) 0.001 J/(kg·m2) 0.0015 $L_{{\mathrm{s}}0}' $/H 0.012 $L_{{\mathrm{s}}2}' $/H 0.004 $L_{{\mathrm{s}}2}'' $/H 0.003 Udc/V 380 $ \psi _{\mathrm{f}} $/Wb 0.26
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[1] 刘佳敏, 葛召炎, 吴轩, 等. 基于占空比调制的永磁同步电机预测电流控制[J]. 中国电机工程学报, 2020, 40(10): 3319-3328.LIU J M, GE Z Y, WU X, et al. Predictive current control of permanent magnet synchronous motor based on duty-cycle modulation[J]. Proceedings of the CSEE, 2020, 40(10): 3319-3328(in Chinese). [2] LIU S Y, LIU C H. Virtual-vector-based robust predictive current control for dual three-phase PMSM[J]. IEEE Transactions on Industrial Electronics, 2021, 68(3): 2048-2058. doi: 10.1109/TIE.2020.2973905 [3] 赵亚辉, 冯明, 李卫文. 基于SMO的改进型转子位置检测方法[J]. 北京航空航天大学学报, 2020, 46(12): 2329-2338.ZHAO Y H, FENG M, LI W W. Improved rotor position detection method based on SMO[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(12): 2329-2338(in Chinese). [4] ZENG X L, YAO L G, ZHANG J, et al. Mathematical modeling and characteristics analysis of the toroidal drive with elastic deformation in the conjugating process[J]. IEEE Access, 2020, 7: 131234-131247. [5] 郝秀红, 朱学军, 许立忠. 机电集成超环面传动系统参数振动研究[J]. 振动与冲击, 2013, 32(22): 113-118.HAO X H, ZHU X J, XU L Z. Parametric vibration of an electromechanical integrated toroidal drive[J]. Journal of Vibration and Shock, 2013, 32(22): 113-118(in Chinese). [6] XU L Z, FU L P. Toroidal drive with half stator[J]. Advances in Mechanical Engineering, 2015, 7(6): 1687814015589270. [7] XU L Z, LI R, SONG W T. Permanent magnetic toroidal drive with half stator[J]. Advances in Mechanical Engineering, 2017, 9(1): 1687814016686316. [8] LIU X, SUN M P, WANG X Y. Analysis of end-effect force and weakening method for permanent magnet toroidal motor[J]. IEEE Transactions on Magnetics, 2020, 56(12): 8200707. [9] 刘欣, 许立忠, 聂岭. 新型超环面混合励磁电机的结构及特性分析[J]. 中国电机工程学报, 2015, 35(20): 5335-5343.LIU X, XU L Z, NIE L. Structure and characteristic analysis of a novel toroidal motor with hybrid excitation[J]. Proceedings of the CSEE, 2015, 35(20): 5335-5343(in Chinese). [10] 李争, 安金峰, 肖宇, 等. 基于自适应观测器的永磁同步直线电机模型预测控制系统设计[J]. 电工技术学报, 2021, 36(6): 1190-1200.LI Z, AN J F, XIAO Y, et al. Design of model predictive control system for permanent magnet synchronous linear motor based on adaptive observer[J]. Transactions of China Electrotechnical Society, 2021, 36(6): 1190-1200(in Chinese). [11] ZHANG Y Q, YIN Z G, LI W, et al. Adaptive sliding-mode-based speed control in finite control set model predictive torque control for induction motors[J]. IEEE Transactions on Power Electronics, 2021, 36(7): 8076-8087. doi: 10.1109/TPEL.2020.3042181 [12] 徐艳平, 王极兵, 张保程, 等. 永磁同步电机三矢量模型预测电流控制[J]. 电工技术学报, 2018, 33(5): 980-988.XU Y P, WANG J B, ZHANG B C, et al. Three-vector-based model predictive current control for permanent magnet synchronous motor[J]. Transactions of China Electrotechnical Society, 2018, 33(5): 980-988(in Chinese). [13] SUN X D, LI T, TIAN X, et al. Fault-tolerant operation of a six-phase permanent magnet synchronous hub motor based on model predictive current control with virtual voltage vectors[J]. IEEE Transactions on Energy Conversion, 2022, 37(1): 337-346. doi: 10.1109/TEC.2021.3109869 [14] XIE H T, WANG F X, XUN Q, et al. A low-complexity gradient descent solution with backtracking iteration approach for finite control set predictive current control[J]. IEEE Transactions on Industrial Electronics, 2022, 69(5): 4522-4533. doi: 10.1109/TIE.2021.3084164 [15] ZHANG Y C, JIANG H, YANG H T. Model predictive control of PMSM drives based on general discrete space vector modulation[J]. IEEE Transactions on Energy Conversion, 2021, 36(2): 1300-1307. doi: 10.1109/TEC.2020.3036082 [16] YUAN J, WEN D D, ZHANG Y. Model predictive current control strategy with reduced computation burden[J]. Mathematical Problems in Engineering, 2021, 2021: 6677543. [17] ZHANG X G, ZHAO Z H, CHENG Y, et al. Robust model predictive current control based on inductance and flux linkage extraction algorithm[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14893-14902. doi: 10.1109/TVT.2020.3043474 [18] XU D Z, ZHAO W X, TANG H Y, et al. Three-vector-based model predictive current control with zero-sequence current suppression for open-winding LPMVM drives[J]. IEEE Transactions on Vehicular Technology, 2021, 70(1): 225-236. doi: 10.1109/TVT.2020.3045257 [19] KANG S W, SOH J H, KIM R Y. Symmetrical three-vector-based model predictive control with deadbeat solution for IPMSM in rotating reference frame[J]. IEEE Transactions on Industrial Electronics, 2020, 67(1): 159-168. doi: 10.1109/TIE.2018.2890490 [20] ZHANG Q K, YI Y P, LIU P. Robust model predictive current control without dead-zone compensation[J]. IET Electric Power Applications, 2020, 14(11): 2193-2201. doi: 10.1049/iet-epa.2020.0102 [21] ZHANG X G, YAN K, ZHANG W H. Composite vector model predictive control with time-varying control period for PMSM drives[J]. IEEE Transactions on Transportation Electrification, 2021, 7(3): 1415-1426. doi: 10.1109/TTE.2020.3047898 -
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