Speed-Sensorless Control System of Bearingless Induction Motor Based on the Novel Extended Kalman Filter

被引：0
|
作者：
Sun Y. [1 ]
Shen Q. [1 ]
Shi K. [1 ]
Zhu H. [1 ]
机构：
[1] School of Electrical and Information Engineering, Jiangsu University, Zhenjiang
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2018年 / 33卷 / 13期
关键词：
Bearingless induction motor; Novel Kalman filter; Parameter change; Speed-sensorless control;
D O I：
10.19595/j.cnki.1000-6753.tces.170757
中图分类号：
学科分类号：
摘要：
To improve the performance of bearingless induction motor drives with speed sensorless vector control system, a novel extended Kalman filter with a series structure is proposed. The on-line calculation of motor parameters is achieved by extending the easily changing motor parameters to the system model to be the state vector to be identified. The accurate identification of the motor speed can be realized by feeding back the obtained parameter value into the algorithm, which is conductive to reduce the impact of motor parameter variation on speed estimation accuracy. The order of system model matrix can be decreased by employing the series structure extended Kalman filters, and also the computational load and complexity of digital chips in practical applications would be reduced. Thecomparision of estimated speed error between the traditional extended Kalman filter and the novel extended Kalman filter in the case of motor parameters are changed is completed by simulation and experiment. The results demonstrate that the novel Kalman filter can effectively reduce the impact of parameter variation on the estimation accuracy. © 2018, Electrical Technology Press Co. Ltd. All right reserved.
引用
收藏
页码：2946 / 2955
页数：9
相关论文
共 17 条
  • [1] Wang X., Deng Z., Analysis of flux-oriented strategies of bearingless asynchronous motor, Proceedings of the CSEE, 27, 27, pp. 77-82, (2007)
  • [2] Lin M., Li Y., Wu C., Et al., A model reference adaptive system based sliding mode observer for model predictive controlled permanent magnet synchronous motor drive, Transactions of China Electrotechnical Society, 32, 6, pp. 156-163, (2017)
  • [3] Chen S., Pi Y., Position sensorless control for permanent magnet synchronous motor based on sliding mode observer and sliding mode controller, Transactions of China Electrotechnical Society, 31, 12, pp. 108-117, (2016)
  • [4] Maiti S., Verma V., Chakraborty C., Et al., An adaptive speed sensorless induction motor drive with artificial neural network for stability enhancement, IEEE Transactions on Industrial Informatics, 8, 4, pp. 757-766, (2012)
  • [5] Chen Z., Gao J., Wang F., Et al., Sensorless control for SPMSM with concentrated windings using multisignal injection method, IEEE Transactions on Industrial Electronics, 61, 12, pp. 6624-6634, (2014)
  • [6] Liu J., Lu J., High-precision estimation method of initial rotor position for IPMSM based on phase difference of positive and negative sequence current component, Transactions of China Electrotechnical Society, 31, 23, pp. 63-69, (2016)
  • [7] Kong L., Cheng M., Zhang B., Position sensorless control of modular linear flux-switching permanent magnet machine based on model reference adaptive system, Transactions of China Electrotechnical Society, 31, 17, pp. 132-139, (2016)
  • [8] Wang Q., Zhang X., Zhang C., Double sliding-mode model reference adaptive system speed identification for vector control of permanent magnet synchronous motors, Proceedings of the CSEE, 34, 6, pp. 897-902, (2014)
  • [9] Yin Z., Li G., Zhang Y., Et al., A speed estimation method based on strong tracking extended Kalman filter with normalized residuals for induction motors, Transactions of China Electrotechnical Society, 32, 5, pp. 86-96, (2017)
  • [10] Yin Z., Zhao C., Zhong Y., Et al., Research on robust performance of speed-sensorless vector control for the induction motor using an interfacing multiple-model extended Kalman filter, IEEE Transactions on Power Electronics, 29, 6, pp. 3011-3019, (2014)
12