EKF协同马尔科夫链的感应电机转速辨识方法

发布时间：2018-05-05 06:09

本文选题：马尔科夫链 + 多模型　；参考：《西安理工大学》2017年硕士论文

【摘要】：转速的闭环控制在高性能交流调速系统中是不可缺少的,一般通过光电编码器等速度传感器对转速进行检测。然而,速度传感器的安装给系统带来了很多负而影响,例如,系统硬件成本增加,难以适应高温、高湿度等恶劣环境,降低了调速系统的简易性和可靠性,限制了其应用范围。因此,近年来无速度传感器矢量控制技术受到国内外学者的广泛关注,成为了电机控制领域的研究热点。本文主要针对基于马尔科夫链的多模型扩展卡尔曼滤(Multiple-Model Extended Kalman Filter with Markov Chain, MC-MM-EKF)感应电机转速估计方法的鲁棒性和抗差性能进行了深入研究。第一,本文分析了感应电机的数学模型,并基于数学模型分析了电机本身的稳定性。第二,详细论述了扩展卡尔曼滤波的基本原理及其在感应电机无速度传感器矢量控制中的应用;讨论了干扰和电机参数变化对无速度传感器控制性能的影响,特别是对电机转速估计环节的影响。第三,阐述了多模型理论的基本原理,分析了多模型理论对模型不确定性问题的解决思路;建立了基于扩展卡尔曼滤波转速辨识的多模型抗差数学模型,并研究了其抗差机理;宽速范围尤其是低速条件下,分析了基于马尔科夫链的多模型扩展卡尔曼滤波无速度传感器控制抗差系统的稳定性和对参数的敏感性。第四,通过Matlab/Simulink软件对基于马尔科夫链的多模型扩展卡尔曼滤波的感应电机无速度传感器矢量控制系统抗差性能进行了仿真验证。最后,搭建了以TI公司DSP芯片TMS320F28335为微处理器的实验平台,并对基于马尔科夫链的多模型扩展卡尔曼滤波转速辨识方法进行了实验验证。仿真和实验结果表明与扩展卡尔曼滤波算法相比,本文提出的转速估计方法能有效提高系统模型对于实际系统以及外部环境变化的适应性,显著降低了在电机参数变化和干扰发生时的转速估计误差,提高了感应电机无速度传感器矢量控制系统的稳态和动态性能。
[Abstract]:Closed-loop speed control is indispensable in high performance AC speed regulation system. Speed is generally detected by photoelectric encoder and other speed sensors. However, the installation of speed sensors has a lot of negative effects on the system. For example, the hardware cost of the system is increased, it is difficult to adapt to the harsh environment such as high temperature and humidity, which reduces the simplicity and reliability of the speed regulating system, and limits its application scope. Therefore, in recent years, speed sensorless vector control technology has been widely concerned by scholars at home and abroad, and has become a research hotspot in the field of motor control. In this paper, the robustness and robust performance of the multi-model extended Kalman filter Multiple-Model Extended Kalman Filter with Markov Chain, MC-MM-EKF-based speed estimation method based on Markov chain are studied in detail. Firstly, the mathematical model of induction motor is analyzed, and the stability of the motor itself is analyzed based on the mathematical model. Secondly, the basic principle of extended Kalman filter and its application in speed sensorless vector control of induction motor are discussed in detail, and the influence of interference and motor parameter change on speed sensorless control performance is discussed. Especially the effect on motor speed estimation. Thirdly, the basic principle of multi-model theory is expounded, and the solution of multi-model theory to model uncertainty is analyzed, and the mathematical model of multi-model robust based on speed identification of extended Kalman filter is established, and its robust mechanism is studied. In the wide speed range, especially at low speed, the stability and sensitivity to parameters of multi-model extended Kalman filter (EKF) based on Markov chain for robust control of speed sensorless systems are analyzed. Fourthly, the robust performance of speed sensorless vector control system of induction motor based on Markov chain extended Kalman filter is simulated by Matlab/Simulink software. Finally, an experimental platform based on TI DSP chip TMS320F28335 is built, and the method of multi-model extended Kalman filter speed identification based on Markov chain is verified. The simulation and experimental results show that the proposed speed estimation method can effectively improve the adaptability of the system model to the actual system and the external environment, compared with the extended Kalman filter. The error of speed estimation is significantly reduced when the motor parameters change and disturbance occurs, and the steady-state and dynamic performance of the sensorless vector control system of induction motor is improved.
【学位授予单位】：西安理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM346

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