STEKF协同残差归一化的感应电机转速辨识方法

发布时间：2018-04-03 19:21

本文选题：强跟踪扩展卡尔曼滤波　切入点：残差归一化　出处：《电工技术学报》2017年05期

【摘要】：为了提高感应电机无速度传感器矢量控制系统的性能,提出了一种强跟踪扩展卡尔曼滤波(STEKF)协同残差归一化(NR)的自适应转速估计方法。通过将渐消因子引入到状态预测协方差矩阵中,实时在线调整增益矩阵,强迫输出残差序列保持相互正交。同时,通过对残差进行归一化处理,自适应地调节渐消因子,消除由残差本身数值差异引起的信息不对称。该文提出的方法提高了系统模型对于实际系统以及外部环境变化的适应性,有效降低了估计误差,满足对于低速的估计要求。对基于STEKF协同残差归一化观测器的感应电机无速度传感器矢量控制系统进行了实验验证,实验结果证明了算法的正确性和有效性。
[Abstract]:In order to improve the performance of speed sensorless vector control system of induction motor, an adaptive speed estimation method based on strong tracking extended Kalman filter (STEKF) cooperative residual normalization (NRs) is proposed.By introducing the fading factor into the state prediction covariance matrix and adjusting the gain matrix on-line, the output residuals are forced to remain orthogonal to each other.At the same time, by normalizing the residuals, the fading factor can be adjusted adaptively to eliminate the information asymmetry caused by the numerical differences of the residuals themselves.The method proposed in this paper improves the adaptability of the system model to the change of the real system and the external environment, reduces the estimation error effectively, and meets the requirement of low speed estimation.The speed sensorless vector control system of induction motor based on STEKF cooperative residual normalized observer is experimentally verified. The experimental results show that the algorithm is correct and effective.
【作者单位】：西安理工大学电气工程系;电力设备电气绝缘国家重点实验室(西安交通大学);
【基金】：国家自然科学基金项目(51307139) 陕西省青年科技新星资助项目(2015KJXX-29) 陕西省工业攻关项目(2014K08-30)资助
【分类号】：TM346

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