针对传统扩展卡尔曼滤波器(EKF)固定的噪声协方差矩阵在观测感应电动机转速时不能同时满足系统动态和静态下精确估计的问题,提出了一种模糊自适应调整噪声协方差的方法。该方法可以根据状态鉴别器输出状态,经模糊自适应调整噪声协方差矩阵参数,解决了系统在动态和静态时对噪声协方差矩阵中不同参数需求的问题。仿真表明所提模糊自适应EKF转速估计精度更高,有效地提高了系统的抗干扰能力。

The fixed noise covariance matrix fixed by the traditional extended Kalman filter (EKF) could not accurately satisfy the estimation requirement under dynamic and static conditions when observing the rotational speed of induction motor. Aiming at this problem, a fuzzy adaptive method was proposed. The method could adjust the noise covariance matrix parameters according to the state discriminator output, and solve the problem that the fixed noise covariance matrix could not acquire different parameters in different states at the same time. Simulation showed that the fuzzy adaptive EKF had higher accuracy and improved the antiinterference ability of the system.

TM 343

河北省高等学校创新团队领军人才培育计划项目(LJRC013)；河北省自然科学基金项目(F2016203249)