基于卡尔曼滤波器的永磁同步电机无位置传感器控制

发布时间：2018-07-24 12:57

【摘要】：电机是一种把电能转换机械能的装置，作为清洁、高效的代名词，电机在人们的生产生活已经在各个角落中崭露头角。永磁同步电机拥有构造简单、体积小、效率高和调速范围宽等优势，同时驱动系统可以满足多数情况下的高性能要求，使得其在近年来的电机体系中渐渐占据越来越重要的位置。本文通过电机的基本数学模型和两种坐标变换方法，推导出在d-q坐标系下的模型及公式，继而提出了多种应用于永磁同步电机控制的控制方法。当前在电机控制领域广泛使用的控制方法是矢量控制技术，结合SVPWM（空间矢量脉宽调制）实现对永磁同步电机的精确控制，这种控制系统目前在诸多领域中被科研工作者用来作为永磁同步电机的主要控制方案。在永磁同步电机矢量控制（FOC）中，自适应反馈系统是需要知道电机转子的位置和速度的，某些电机结构中包含了位置传感器，控制系统就可以从传感器中获取转速和角度信息，但是很多场合的电机中是没有位置传感器的，这时需要通过软件层面的算法来对转速和角度进行估算。本文提到了几种广泛使用的无位置传感器的控制策略，，在众多观测器中重点介绍卡尔曼滤波器算法原理和应用。由于电机系统是非线性的，因此需要对其进行线性化操作，继而衍生出了扩展卡尔曼滤波器（EKF）。并且提出了在坐标系下的EKF数学公式，以及在本文电机参数下的误差参数矩阵。本文在设计PMSM控制系统过程中，利用MATLAB-Simulink对电机系统实现模型的搭建和系统的仿真验证。在之前介绍的坐标系下的EKF数学公式的基础上，搭建整套系统的模型，包括矢量控制（FOC）、空间矢量脉宽调制（SVPWM）以及卡尔曼滤波器等等几个部分。在模型搭建的过程中有一些会影响仿真结果的细节需要关注，文章中给予了指出。对永磁同步电机在低速状态下和改变电机参数之后卡尔曼滤波器的工作状况进行了仿真实验，发现在一定范围内其依然保持优秀的性能。最后对本文所研究的内容需要进一步完善的地方做了说明，同时也对下一阶段的工作进行了展望。
[Abstract]:Motor is a mechanical energy conversion device, as a clean, efficient pronoun, the motor in people's production and life has emerged in every corner. PMSM has the advantages of simple structure, small size, high efficiency and wide speed range, and the drive system can meet the requirements of high performance in most cases. It gradually occupies a more and more important position in the motor system in recent years. Based on the basic mathematical model and two coordinate transformation methods of the motor, this paper deduces the model and formula in d-q coordinate system, and then puts forward a variety of control methods applied to the permanent magnet synchronous motor (PMSM) control. At present, vector control technology is widely used in the field of motor control. The precise control of permanent magnet synchronous motor (PMSM) is realized by combining with SVPWM (Space Vector Pulse width Modulation). This control system is used as the main control scheme of PMSM by researchers in many fields. In the (FOC) of permanent magnet synchronous motor (PMSM) vector control, the adaptive feedback system needs to know the position and speed of the motor rotor. Some motor structures contain the position sensor, and the control system can obtain the rotational speed and angle information from the sensor. However, there is no position sensor in the motor in many cases, so it is necessary to estimate the rotation speed and angle by software level algorithm. In this paper, several widely used sensorless control strategies are presented, and the principle and application of Kalman filter algorithm are emphasized in many observers. Because the motor system is nonlinear, it needs to be linearized, and then the extended Kalman filter (EKF).) is derived. The EKF mathematical formula in the coordinate system and the error parameter matrix under the parameters of the motor in this paper are also presented. In the process of designing the PMSM control system, the realization model of the motor system and the simulation verification of the system are built by MATLAB-Simulink. Based on the EKF mathematical formula in the coordinate system, the model of the whole system is built, including vector control (FOC), space vector pulse width modulation (SVPWM) and Kalman filter and so on. Some details that will affect the simulation results need to be paid attention to in the process of modeling, which is pointed out in this paper. In this paper, the simulation experiments on the working condition of the permanent magnet synchronous motor (PMSM) at low speed and after changing the parameters of the PMSM are carried out, and it is found that the PMSM still has excellent performance in a certain range. Finally, the contents of this paper need to be further improved, and the next stage of the work is also prospected.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM341

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