基于粒子群优化的感应电机模糊扩展卡尔曼滤波器转速估计方法

A Speed Estimation Method of Fuzzy Extended Kalman Filter for Induction Motors Based on Particle Swarm Optimization

为了减弱固定的先验噪声模型对扩展卡尔曼滤波器(EKF)状态估计的影响,提出一种基于粒子群优化的感应电机模糊EKF(PFEKF)转速估计方法。通过将粒子群优化(PSO)算法引入模糊控制器,监视实际残差与理论残差的偏离程度,自适应选择模糊调整因子,在线递推修正测量噪声协方差矩阵的加权值,使其逐渐逼近真实噪声水平,从而使滤波器进行优化估计,并减小外部干扰和时变测量噪声对系统性能的影响。仿真和实验结果验证了基于PSO的感应电机模糊EKF转速估计方法的正确性与有效性。

This paper presents a speed estimation method of particle swarm optimization fuzzy extended Kalman filter （PFEKF） for induction motors, to weaken the impacts of priori measurement noise model on the estimation accuracy of extended Kalman filter （EKF）. The proposed algorithm modifies the measurement noise covariance of EKF recursively and chooses a fuzzy factor to make its noise model close to real noise model adaptively, based on PSO fuzzy controller which monitors the degree of divergence （DOD） parameters. Accordingly the optimal estimation is realized, and the impacts of system performance under gross external error and unknown measurement noises are weakened. Simulation and experimental results verify the proposed method for induction motors.