基于EKF的无位置传感器永磁同步电机转速估计

发布时间：2019-05-29 11:53

【摘要】：近些年电机的高效利用和控制逐渐成为了各界研究和探索的主要课题。永磁同步电机(PMSM)凭借其功率密度高、工作效率高、应用范围广,和无需外接励磁装置等诸多优点被广泛应用于各种交流变频调速系统中,并且与其相关的电子器件及控制方法也已经有了比较深入的研究。传统的电机控制方式中,需要精确的转子的转速、转角信息来判读转自位置,所以需要传感器来检测转子的转速和转子角位置。这不但增加了电机的成本,还对电机在一些特殊环境下的运行造成了可靠性的风险,因此限制了电机的应用场合,为了取消这些位置信号采集装置,针对无位置传感器电机的转速估算方法不断成为学术界的重要课题。本文从电机的经典控制理论出发,介绍了交流电机相关的控制方法和无位置传感器的电机转速估算方法,详细介绍了PMSM的数学模型以及矢量控制的原理。针对无位置传感器的PMSM控制,讨论了现有一些电机转速估算和调速方案,并应用了扩展卡尔曼滤波(Extended Kalman Filter)算法,对PMSM的状态方程进行了线性化处理,将Kalman Filter算法推广到了非线性的电机控制系统当中,运用EKF设计了对无位置传感器的PMSM转速估计系统。在以往的大多数应用扩展卡尔曼滤波对电机转速估计得论文当中,主要是对稳态和精确的电机参数下数学模型的转速估计研究,这样对理论依赖性过强,相对实际应用缺乏参考性。本文根据电机实际的工作状态,对一些常见的负载工况使用了Matlab中的Simlink仿真软件进行了无位置传感器转速估计的仿真研究。并且从真实的工程实际出发,针对电机参数的变化引起的实际模型和参考模型之间的差异,用EKF算法进行了验证,并分析了EKF这一递推算法在一定的公差允许范围内有较强的鲁棒性和容错性。
[Abstract]:In recent years, the efficient utilization and control of motor has gradually become the main research and exploration topic. Permanent magnet synchronous motor (PMSM) has been widely used in various AC variable frequency speed regulation systems because of its high power density, high working efficiency, wide application range and no external excitation device. And the related electronic devices and control methods have been deeply studied. In the traditional motor control mode, accurate rotor speed and rotation angle information are needed to judge the rotor position, so sensors are needed to detect the rotor speed and rotor angle position. This not only increases the cost of the motor, but also causes the reliability risk to the operation of the motor in some special environments, so it limits the application of the motor. In order to cancel these position signal acquisition devices, The speed estimation method for sensorless motor has become an important topic in academic circles. Based on the classical control theory of motor, this paper introduces the control method of AC motor and the estimation method of motor speed without position sensor. the mathematical model of PMSM and the principle of vector control are introduced in detail. Aiming at the sensorless PMSM control, some existing motor speed estimation and speed regulation schemes are discussed, and the state equation of PMSM is linearized by using the extended Kalman filter (Extended Kalman Filter) algorithm. The Kalman Filter algorithm is extended to the nonlinear motor control system, and the position sensorless PMSM speed estimation system is designed by using EKF. In most of the previous papers, extended Kalman filter (EKF) is used to estimate the speed of motor, which is mainly about the speed estimation of mathematical model under steady state and accurate motor parameters, which is too dependent on theory. There is a lack of reference in relative practical applications. In this paper, according to the actual working state of the motor, the position sensorless speed estimation is simulated by using the Simlink simulation software in Matlab for some common load conditions. And from the real engineering practice, aiming at the difference between the actual model and the reference model caused by the change of motor parameters, the EKF algorithm is used to verify the difference between the actual model and the reference model. The robustness and fault tolerance of EKF algorithm in a certain tolerance range are analyzed.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM341

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