中文说明：
针对感应电机扩展卡尔曼滤波器转速估计中难以取得卡尔曼滤波器系统噪声矩阵和测量噪声矩阵最优值的问题，提出了一种基于改进粒子群算法优化的扩展卡尔曼滤波器转速估计方法。算法通过融合遗传算法和粒子群算法的优点，采用可调整的算法模型对粒子群算法进行改进，将改进的粒子群算法对扩展卡尔曼滤波器中的系统噪声矩阵和测量噪声矩阵进行优化处理，将优化后的卡尔曼滤波器应用于感应电机转速估计。
English Description：
Aiming at the problem that it is difficult to obtain the optimal values of Kalman filter system noise matrix and measurement noise matrix in induction motor speed estimation based on extended Kalman filter (EKF), a speed estimation method based on Improved Particle Swarm Optimization (PSO) is proposed. By combining the advantages of genetic algorithm and particle swarm optimization algorithm, the particle swarm optimization algorithm is improved by using an adjustable algorithm model. The improved particle swarm optimization algorithm is used to optimize the system noise matrix and measurement noise matrix in the extended Kalman filter, and the optimized Kalman filter is applied to the speed estimation of induction motor.