基于新型卡尔曼滤波器的无轴承异步电机无速度传感器控制  

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

作　　者：孙宇新[1] 沈启康 施凯[1] 朱熀秋[1] Sun Yuxin;Shen Qikang;Shi Kai;Zhu Huangqiu(School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 China)

机构地区：[1]江苏大学电气信息工程学院,镇江212013

出　　处：《电工技术学报》2018年第13期2946-2955,共10页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金(51675244);江苏省重点研发计划项目(BE2016150);江苏高校优势学科建设工程项目资助

摘　　要：为了提高无轴承异步电机无速度传感器矢量控制系统的精度,提出一种新型串联卡尔曼滤波器,通过将容易变化的电机参数作为待辨识状态向量增广到系统模型中,实现电机参数的在线计算,并将得到的参数值反馈到算法中实现电机转速的准确辨识,从而减小电机参数变化对转速估算精度的影响。通过采取三个扩展卡尔曼滤波器的串联结构降低系统模型矩阵的阶数,减小实际应用中数字芯片的计算负荷。通过仿真和实验对比了在电机参数变化时传统扩展卡尔曼滤波器和新型串联卡尔曼滤波器的估计转速误差,结果表明新型卡尔曼滤波器能有效减小参数变化对估计精度的影响,确保转子稳定悬浮运行。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.The comparision 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.

关 键 词：新型卡尔曼滤波器 参数变化 无轴承异步电机 无速度传感器 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]