基于自适应模糊神经网络的开关磁阻电机控制系统开发

发布时间：2018-04-01 01:03

本文选题：开关磁阻电机　切入点：自适应模糊神经网络　出处：《山东理工大学》2016年硕士论文

【摘要】：传统汽车行业在促进经济发展,方便人们生活的同时,一系列的问题也随之而来。燃油汽车不仅消耗石油,而且排放污染物污染环境。为了解决能源和环境问题,使用清洁高效的新能源汽车是大势所趋。作为新能源电动汽车的重要组成部分,有必要研究开发高效率的电机调速系统。开关磁阻电机(SRM)调速系统具有成本低、工作稳定可靠、控制灵活、电机的能量密度大、环境适应性强的优点,近年来受到较多关注与开发。但是由于开关磁阻电机固有的双凸极定转子机械结构,以及开关形式的供电方式,使得开关磁阻电机非线性特点明显,限制了电机的使用范围和领域。针对电机存在的高度非线性问题,本文通过自适应模糊神经网络方法建立电机的仿真模型,来解决这一存在问题。在对开关磁阻电机控制系统进行软件开发时,为提高开发效率,本文不是采用传统的手写代码的方式,而是采用基于模型的设计方法(Model Based Design)进行设计开发。首先,本文介绍了开关磁阻电机的结构构造、工作原理、运行特性等相关的内容。其次,在熟悉了开关磁阻电机的工作原理、控制方式后,针对开关磁阻电机高度非线性的特点,采用自适应模糊神经网络(ANFIS),对开关磁阻电机的转矩正模型、转矩逆模型和磁链逆模型进行辨识,搭建开关磁阻电机的模型。对电机模型进行仿真、分析。然后,应用基于模型的设计的方法进行软件开发,首先根据控制思想,搭建控制模型。在控制模型中集成设计了GPIO模块、ADC模块、PWM模块以及测速模块等。最后经过系统设置,生成嵌入式C代码。在硬件方面,选择TI公司的F28335芯片作为主控制器芯片,然后设计其他硬件部分。最后,结合软件设计和硬件电路,对开关磁阻电机在空载的情况下调试。试验表明,基于模型的设计的思想和方法在设计电机调速系统软件开发时是可行的。
[Abstract]:While the traditional automobile industry promotes economic development and facilitates people's life, a series of problems follow. Fuel cars not only consume oil, but also emit pollutants to pollute the environment. In order to solve the energy and environmental problems, The use of clean and efficient new energy vehicles is the trend of the times. As an important part of new energy electric vehicles, it is necessary to study and develop a high efficiency motor speed regulation system. The switched reluctance motor (SRM) speed regulation system has the advantages of low cost, stable and reliable operation. The advantages of flexible control, high energy density and strong environmental adaptability have attracted more attention and development in recent years. However, due to the inherent mechanical structure of double-salient pole stator rotor of switched reluctance motor and the power supply mode of switch form, The nonlinear characteristic of switched reluctance motor is obvious, which limits the application range and field of the motor. In view of the high nonlinearity of the motor, the simulation model of the motor is established by the method of adaptive fuzzy neural network. In order to improve the efficiency of the software development of switched reluctance motor control system, this paper does not adopt the traditional way of handwritten code. First, this paper introduces the structure of switched reluctance motor (SRM), working principle, operating characteristics and other related contents. After being familiar with the working principle and control mode of switched reluctance motor, aiming at the characteristics of high nonlinearity of switched reluctance motor, an adaptive fuzzy neural network (ANFIS) is used to model the torque positive of switched reluctance motor. The torque inverse model and flux chain inverse model are identified, and the model of switched reluctance motor is built. The motor model is simulated and analyzed. Then, the software is developed with the method of model-based design. The control model is built. In the control model, the GPIO module is integrated and designed, such as the GPIO module and the speed measurement module. Finally, the embedded C code is generated through the system setup. In hardware, TI's F28335 chip is selected as the main controller chip. Finally, combined with software design and hardware circuit, the switched reluctance motor is debugged under the condition of no load. The idea and method of model-based design is feasible in the software development of motor speed regulation system.
【学位授予单位】：山东理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U469.7;U463.6

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