开关磁阻电机转矩脉动抑制的控制策略研究

发布时间：2018-08-12 19:11

【摘要】：开关磁阻电机(Switched Reluctance Motor, SRM)具有结构简单、制造成本低、系统可靠性强、能量转换效率高和调速范围广等优点，是未来纯电动汽车产业理想的驱动电机之一。开关磁阻电机已在航空、采矿、纺织等领域得到有效的应用，但电机在低速运行时的较大的转矩脉动及由此而引发的振动噪音严重制约了其在控制要求较高领域的广泛应用。由于开关磁阻电机特殊双凸极的结构特点和开关式的供电模式使其电磁特性呈强非线性，无法有效地建立精确的电机数学模型，而且传统的控制算法对强非线性对象不能达到满意的控制效果，这给设计减少转矩脉动的电机控制方法带来了很大困难。为了减少SRM的低速转矩脉动，本文提出了如下两种控制策略： (1)提出了基于大脑情感学习模型的SRM电流分配控制策略，通过调节电流间接对转矩进行控制。外环采用大脑情感学习模型调节器实现转速偏差到母线参考电流的转换，，母线参考电流通过电流分配函数得到三相参考电流。三相电流偏差经过内环的滞环电流控制单元得到的三相控制信号实现了电机的平滑换相，有效地降低了电机的转矩脉动。 (2)针对SRM强非线性和高度耦合性的特点，借鉴传统直接瞬时转矩控制(DirectInstantaneous Torque Control, DITC)策略，提出了基于所构造的柔性神经网络(FlexibleNeural Network, FNN)的SRM直接瞬时转矩控制策略。该控制策略外环采用不完全微分模糊PID对速度进行调节，内环采用以转矩误差的平方为性能指标函数的FNN自适应PID对转矩进行调节，达到了较为理想的控制效果。在MATLAB/SIMULINK环境下，仿真结果表明上述两种控制策略均能有效地抑制转矩脉动。在仿真研究的基础上，对基于大脑情感学习模型的SRM电流分配控制策略、SRM直接瞬时转矩控制策略和SRM电压斩波控制策略在SRM实验平台上进行了实验测试。实验结果表明前两种控制策略的转矩脉动抑制效果明显优于传统的电压斩波控制策略。
[Abstract]:The switched reluctance motor (Switched Reluctance Motor, SRM) has the advantages of simple structure, low manufacturing cost, strong system reliability, high efficiency of energy conversion and wide range of speed regulation. It is one of the ideal driving motors in the future pure electric vehicle industry. Switched reluctance motor (SRM) has been effectively applied in aviation, mining, textile and other fields. However, the large torque ripple and vibration noise caused by SRM in low speed operation seriously restrict its wide application in the field of high control requirements. Because of the special double salient structure of switched reluctance motor (SRM) and the switching mode of power supply, the electromagnetic characteristics of SRM are strongly nonlinear, so it is impossible to establish an accurate mathematical model of SRM effectively. Moreover, the traditional control algorithm can not achieve satisfactory control effect for the strong nonlinear object, which brings great difficulties to the design of the motor control method to reduce the torque ripple. In order to reduce the low speed torque ripple of SRM, two control strategies are proposed in this paper: (1) the SRM current allocation control strategy based on the brain emotional learning model is proposed, and the torque is indirectly controlled by adjusting the current. The external loop adopts the brain emotional learning model regulator to realize the conversion from the rotational speed deviation to the bus reference current, and the busbar reference current is obtained by the current distribution function. The three-phase current deviation obtained from the hysteresis current control unit of the inner loop realizes the smooth commutation of the motor and effectively reduces the torque ripple of the motor. (2) aiming at the characteristics of strong nonlinearity and high coupling of SRM, Based on the traditional direct instantaneous torque control (DirectInstantaneous Torque Control, DITC) strategy, a SRM direct instantaneous torque control strategy based on the constructed flexible neural network (FlexibleNeural Network, FNN) is proposed. The outer loop of the control strategy uses incomplete differential fuzzy PID to adjust the speed, and the inner loop adopts FNN adaptive PID with the square of torque error as the performance index function to adjust the torque. Simulation results under MATLAB/SIMULINK environment show that the two control strategies can effectively suppress torque ripple. Based on the simulation research, the SRM current allocation control strategy based on the brain emotional learning model and the SRM voltage chopper control strategy are tested on the SRM platform. The experimental results show that the torque ripple suppression effect of the first two control strategies is obviously better than that of the traditional voltage chopping control strategy.
【学位授予单位】：桂林电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM352

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