电流自适应控制抑制开关磁阻电机转矩脉动

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摘要开关磁阻电机(SRM)的强非线性源自其双凸极结构、磁路非线性和脉冲供电方式。传统控制多采用SRM线性转矩模型求得参考电流，导致其运行时转矩脉动大。该文提出基于转矩偏差的双权值神经网络(DWNN)自适应PID控制与基于有限差分扩展卡尔曼滤波(FDEKF)预测电流的前馈补偿控制相结合的SRM控制策略。(1)加入偏差预处理，对转矩偏差进行非线性处理，实现“小误差，大增益，大误差，小增益”的控制，以此为基础进行双权值神经网络自适应PID的电流控制；(2)采用预测电流，构成参考电流的前馈补偿控制，提高控制系统一步预测能力。基于有限差分扩展卡尔曼滤波预测电流，将其与参考电流之差实时补偿参考电流，优化得到恒转矩下有效的控制电流，间接实现总转矩的有效控制。仿真结果证明该文所提控制策略能有效抑制SRM的转矩脉动。
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关键词 ：开关磁阻电机, 偏差预处理, 双权值神经网络, 有限差分扩展卡尔曼滤波

Abstract：A switched reluctance motor (SRM) has strong nonlinear characteristics due to its double-salient structure, nonlinear magnetic circuit and pulse power supply mode. In traditional control, the SRM linear torque model is used to calculate reference current to lead to large torque ripple when operating. Here, the SRM control strategy combining the self-adaptive PID control based on torque deviation’s double-weight neural network (DWNN) and the feed-forward compensation control based on finite-difference extended Kalman filtering (FDEKF) to predict current was proposed. The pretreatment of deviation was used to nonlinearly process torque deviation to realize the control with "small error, large gain, large error, small gain". Then, the self-adaptive PID control based on DWNN was used to control current. The current prediction was adopted to form the reference current’s feed-forward compensation control to improve the one-step predictive ability of the control system. The current was predicted based on FDEKF, the difference between the predicted current and the reference one was used to compensate the reference current in real time. After optimization, the effective controlled current was obtained under constant torque to realize indirectly the effective control of the total torque. Simulation results showed that the proposed control strategy can effectively suppress torque ripple of SRMs.

Key words： switched reluctance motor (SRM) pretreatment of deviation double-weight neural network (DWNN) finite difference extended Kalman filtering (FDEKF)

收稿日期: 2016-06-14 出版日期: 2018-01-28

引用本文:

党选举1，苗茂宇1，姜辉1，伍锡如1，李珊2. 电流自适应控制抑制开关磁阻电机转矩脉动[J]. 振动与冲击, 2018, 37(3): 66-71.
DANG Xuan-ju 1 MIAO Mao-yu 1 JIANG Hui 1 WU Xi-ru 1 LI Shan 2 . Torque ripple suppression for switched reluctance motors based on current self-adaptive control. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(3): 66-71.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I3/66

[1] Choi C, Kim S, Kim Y, et al. A new torque control method of a switched reluctance motor using a torque-sharing function[J]. IEEE Transactions on Magnetics, 2002, 38(5):3288-3290.
[2] Sun Q, Wu J, Gan C, et al. Investigation of direct torque control and torque sharing function strategy for switched reluctance motor applications[C]// International Conference on Electrical Machines and Systems. IEEE, 2015：864-868.
[3] S. Kurian, Nisha G. K. Torque ripple minimization of SRM using torque sharing function and hysteresis current controller[C]// International Conference on Control Communication & Computing India. IEEE, 2015：149-154.
[4] Ye J, Bilgin B, Emadi A. An Offline Torque Sharing Function for Torque Ripple Reduction in Switched Reluctance Motor Drives[J]. IEEE Transactions on Energy Conversion, 2015, 30(2):1-10.
[5] Wang J J. A common sharing method for current and flux-linkage control of switched reluctance motor[J]. Electric Power Systems Research, 2016, 131:19-30.
[6] Ouddah N, Loukkas N, Chaibet A, et al. Experimental evaluation of second sliding modes observer and Extended Kalman Filter in a sensorless robust control of Switched Reluctance Motor for EV application[C].Control and Automation. IEEE, 2015:986-992.
[7] 党选举, 肖逢, 林诚才. 基于电流迭代优化的SRM总转矩TSF闭环控制[J]. 电气传动, 2015, 45(8):41-46.
   DANG Xuan-ju, XIAO Feng, LIN Cheng-cai. Closed loop control of total torque TSF for switched reluctance motor based on current iteration optimization[J]. Electric Drive, 2015, 45(8): 41-46.
[8] 曹宇. 双权值人工神经网络用于数据拟合的研究[D]. 中国科学院半导体研究所, 2002:1-45.
   CAO yu. Data Fitting Based on Double Weights Neural Network[D]. Institute of Semiconductors, Chinese Academy of Sciences, 2002: 1-45.
[9] 韩京清. 从PID技术到“自抗扰控制”技术[J]. 控制工程, 2002, 9(3):13-18.
   HAN Jing-qing. From PID to "Active Disturbance Rejection Control Technique"[J]. Control Engineering of China, 2002, 9(3): 13-18.
[10] 刘福才, 陈鑫, 贾亚飞,等. 模糊自抗扰控制器在挠性航天器振动抑制中的应用[J]. 振动与冲击, 2015(9):9-14.
    LIU Fu-cai, CHEN Xin, JIA Ya-fei, et al. Application of fuzzy auto disturbance rejection controller in flexible spacecraft vibration suppression[J]. Journal of Vibration and Shock, 2015(9): 9-14.
[11] Wu C, Han C. Strong tracking finite-difference extended Kalman filtering for ballistic target tracking[C]. IEEE International Conference on Robotics & Biomimetics. IEEE, 2007:1540 - 1544.
[12] 王宏华. 开关磁阻电动机调速控制技术[M]. 第2版.北京：机械工业出版社, 2014.1-264.
    WANG Hong-hua. Switched Reluctance Motor Drive Control Technology [M]. Version 2. Beijing：China Machine Press, 2014. 1-264.
[13] Dowlatshahi M, Saghaian Nejad S M, Moallem M, et al. Torque ripple reduction of switched reluctance motors considering copper loss minimization[C].IEEE International Conference on Industrial Technology. 2014:858-865.