基于优化EKF的永磁同步电机DTC控制系统研究

发布时间：2018-07-23 09:38

【摘要】：永磁同步电动机(PMSM)具有很多优点,其中它具有体积小、噪声低、效率高、可靠性高、功率密度大等特点被广泛运用于性能较高的场合,因此永磁同步电机越来越引起人们的关注和重视。而作为一种新型的交流电动机控制技术,直接转矩控制技术具有很多优点,如控制方法简单直接、系统具有强鲁棒性以及动态响应快。因两者优秀的特征,将这两种技术结合已成为现代交流传动领域重要的热点话题。本文分析了永磁同步电机的数学模型和直接转矩控制策略,并分析了传统DTC控制系统本身采用电压积分对定子磁链进行估算的缺点,使定子磁链值不能得到准确计算;此外,通常对控制系统需要安装机械传感器来测量电机的速度和位置信号,但是在某些恶劣的环境下安装机械传感器,由于环境的因素机械传感器自身会存在测量不准确、精度不高等带来的误差都会影响控制系统的性能。为了解决上述问题提出了无传感器技术,本文设计了基于两相静止(α-β)坐标系下EKF的PMSM_DTC控制系统仿真模型,算法运用估计误差均方差最小的原则方法,进而避免传统DTC控制方法存在不足和缺陷,仿真结果表明,定子磁链脉动和转矩脉动有明显程度上的降低。另外,基于EKF观测器在进行转速的估计上具有精准性和有效性,并实现了无传感器运行。同时本文对永磁同步电机提出了改进粒子群算法优化扩展卡尔曼滤波(EKF)器噪声矩阵的方法来实现永磁同步电机(PMSM)无传感器控制,克服了以往关于扩展卡尔曼滤波器状态估计中最优噪声矩阵难以选取的问题。通过将遗传算法(GA)和粒子群算法(PSO)结合起来并继承它们各自的优点。结合改进后的粒子群算法来优化扩展卡尔曼滤波器中的噪声矩阵,然后应用于PMSM无传感器直接转矩控制系统中。本文在Matlab/Simulink平台搭建系统仿真,将改进的粒子群与简便的试凑法、遗传算法和粒子群算法比较来看,其能够更好的改善扩展卡尔曼滤波器的滤波特性以及抗噪声能力,从而对感应电机无传感器直接转矩控制(DTC)系统控制性能有明显的提高。
[Abstract]:Permanent magnet synchronous motor (PMSM) has many advantages, such as small size, low noise, high efficiency, high reliability and high power density. Therefore, permanent magnet synchronous motor (PMSM) has attracted more and more attention. As a new type of AC motor control technology, direct torque control technology has many advantages, such as simple and direct control method, strong robustness and fast dynamic response of the system. Because of their excellent characteristics, the combination of these two technologies has become an important hot topic in the field of modern AC transmission. In this paper, the mathematical model and direct torque control strategy of permanent magnet synchronous motor are analyzed, and the shortcoming of traditional DTC control system to estimate stator flux by voltage integral is analyzed, which makes the stator flux value can not be calculated accurately. Mechanical sensors are usually installed to measure the speed and position signals of the motor. However, in some harsh environments, the mechanical sensors themselves will be inaccurate because of the environmental factors. The error caused by low precision will affect the performance of the control system. In order to solve the above problem, this paper designs a simulation model of PMSM DTC control system based on EKF in two-phase stationary (伪-尾) coordinate system. The algorithm uses the principle method of minimum mean square error of estimation error. The simulation results show that the stator flux ripple and torque ripple are obviously reduced. In addition, the EKF observer is accurate and effective in speed estimation, and realizes sensorless operation. At the same time, an improved particle swarm optimization algorithm is proposed to optimize the extended Kalman filter (EKF) noise matrix to realize sensorless control of permanent magnet synchronous motor (PMSM). It overcomes the problem that the optimal noise matrix is difficult to select in the state estimation of extended Kalman filter. Genetic algorithm (GA) and particle swarm optimization (PSO) are combined and their respective advantages are inherited. The improved particle swarm optimization algorithm is used to optimize the noise matrix in extended Kalman filter and then applied to PMSM sensorless direct torque control system. In this paper, the system simulation is built on Matlab / Simulink platform. Compared with the improved particle swarm optimization, genetic algorithm and particle swarm optimization algorithm, the improved particle swarm optimization algorithm can better improve the filtering characteristics and anti-noise ability of the extended Kalman filter. Thus, the control performance of sensorless direct torque control (DTC) system of induction motor is improved obviously.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM341;TP273

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