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孙宇新,沈启康,叶海涵,朱熀秋.基于改进UKF的无轴承异步电机无速度传感器控制[J].农业工程学报,2018,34(19):74-81.DOI:10.11975/j.issn.1002-6819.2018.19.010
基于改进UKF的无轴承异步电机无速度传感器控制
投稿时间:2018-01-22  修订日期:2018-06-14
中文关键词:  电机  控制  自适应渐消无迹卡尔曼滤波算法  改进AFUKF  无轴承异步电机  无速度传感器控制算法
基金项目:国家自然科学基金项目(51675244);江苏省重点研发计划项目(BE2016150)
作者单位
孙宇新 江苏大学电气信息工程学院镇江 212013 
沈启康 江苏大学电气信息工程学院镇江 212013 
叶海涵 江苏大学电气信息工程学院镇江 212013 
朱熀秋 江苏大学电气信息工程学院镇江 212013 
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中文摘要:基于传统卡尔曼滤波器的转速估计方法依赖于系统的精确数学模型,但目前通用的无轴承异步电机的数学模型是一个近似模型,针对该问题该文提出一种以实际转速为基准的改进的无轴承异步电机转速估算方案:首先,用残差归一化处理自动更新渐消因子并将其引入增益矩阵,以减小系统模型偏差对估算精度的影响,增强滤波器的稳定性;其次,用遗传算法自动更新噪声矩阵,使其具备补偿作用,再次优化转速估算精度,最终将估算精度控制在5 r/min左右,干扰误差控制在10 r/min左右,可有效应对建模误差和参数扰动对转速估算的影响,具备较高的鲁棒性和估算精度。最后,用dSPACE试验平台证明了所提方案的正确性和可行性,该研究为无轴承异步电机无速度传感器控制提供参考。
Sun Yuxin,Shen Qikang,Ye Haihan,Zhu Huangqiu.Speed-sensorless control system of bearingless induction motor based on modified adaptive fading unscented kalman filter[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(19):74-81.DOI:10.11975/j.issn.1002-6819.2018.19.010
Speed-sensorless control system of bearingless induction motor based on modified adaptive fading unscented kalman filter
Author NameAffiliation
Sun Yuxin College of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China 
Shen Qikang College of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China 
Ye Haihan College of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China 
Zhu Huangqiu College of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China 
Key words:motors  control  adaptive fading unscented kalman filter  modified AFUKF  bearingless induction motor  speed sensorless control algorithm
Abstract: Speed sensorless control estimates the speed of the motor by detecting the voltage and current in the motor, thereby avoiding the use of speed sensors in the system. This method can avoid the influence of the sensor on the rotation of the motor and effectively improve the speed regulation performance of the motor. The Kalman filter is widely used in speed estimation algorithm and this method is well applied to ordinary asynchronous motors, but when it is extended to bearingless induction motor(BIM), there is a mismatch, for the strong dependence of the Kalman filter on the system model, but the current mathematical model of the bearingless induction motor is an approximate model. Aiming at this problem, this study proposes a speed identification method based on improved adaptive fade-out unscented Kalman filter. By using the covariance of the residual sequence, to adaptively change the fading factor to adjust the weight of the new interest, so that the filtering algorithm is more convinced of the measured value in the estimation process, which helps to reduce the impact of stale measurement and system model uncertainty on estimation accuracy. At the same time, by normalizing the residuals, the effect of balancing the information between the residuals is achieved, and the speed of information extraction is improved. In addition, the residual part of the system model is regarded as noise. In order to further reduce the model error, the genetic algorithm is used to adaptively adjust the noise matrix. After repeated cycles of selection, crossover and mutation, the conditions for satisfying the whole population convergence can be selected. With the optimal chromosome, the noise matrix Q and R of the Manchester filter satisfying the optimal filtering condition can be obtained, and the model is compensated by the matrix, and the estimation error of the system is optimized again. In order to verify the effectiveness of the algorithm, this study selects dSPACE1005 developed by German dSPACE company as the core controller and designs the experimental platform. The platform consists of the dSPACE1005’s arithmetic control unit, computer, current sensor, voltage Hall sensor, photoelectric encoder and IPM module. The platform consists of the dSPACE1005 arithmetic control unit, computer, current sensor, voltage Hall sensor, photoelectric encoder and IPM module. The stator voltage, stator current and rotational speed are measured by current, voltage Hall and photoelectric encoder respectively. These signals are input into dSPACE1005 through signal conditioning board, and then the improved adaptive fade-out unscented Kalman filter algorithm and noise matrix are completed by dSPACE1005. The effectiveness of the proposed method is verified by comparing the estimation results of Kalman filter and the improved adaptive AFUKF. The robustness of the proposed method is verified by comparing the anti-jamming capabilities of the two algorithms. Experimental results show that this control method has high robustness and precision, and can effectively deal with the influence of modeling error and parameter disturbance on the accuracy of speed estimation. Finally, the correctness and feasibility of the proposed scheme are proved by dSPACE experimental platform.
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