開關磁阻電機轉矩脈動抑制的控制策略研究
發(fā)布時間：2018-08-12 19:11
【摘要】：開關磁阻電機(Switched Reluctance Motor, SRM)具有結構簡單、制造成本低、系統(tǒng)可靠性強、能量轉換效率高和調速范圍廣等優(yōu)點，是未來純電動汽車產(chǎn)業(yè)理想的驅動電機之一。開關磁阻電機已在航空、采礦、紡織等領域得到有效的應用，但電機在低速運行時的較大的轉矩脈動及由此而引發(fā)的振動噪音嚴重制約了其在控制要求較高領域的廣泛應用。由于開關磁阻電機特殊雙凸極的結構特點和開關式的供電模式使其電磁特性呈強非線性，無法有效地建立精確的電機數(shù)學模型，而且傳統(tǒng)的控制算法對強非線性對象不能達到滿意的控制效果，這給設計減少轉矩脈動的電機控制方法帶來了很大困難。為了減少SRM的低速轉矩脈動，本文提出了如下兩種控制策略： (1)提出了基于大腦情感學習模型的SRM電流分配控制策略，通過調節(jié)電流間接對轉矩進行控制。外環(huán)采用大腦情感學習模型調節(jié)器實現(xiàn)轉速偏差到母線參考電流的轉換，，母線參考電流通過電流分配函數(shù)得到三相參考電流。三相電流偏差經(jīng)過內(nèi)環(huán)的滯環(huán)電流控制單元得到的三相控制信號實現(xiàn)了電機的平滑換相，有效地降低了電機的轉矩脈動。 (2)針對SRM強非線性和高度耦合性的特點，借鑒傳統(tǒng)直接瞬時轉矩控制(DirectInstantaneous Torque Control, DITC)策略，提出了基于所構造的柔性神經(jīng)網(wǎng)絡(FlexibleNeural Network, FNN)的SRM直接瞬時轉矩控制策略。該控制策略外環(huán)采用不完全微分模糊PID對速度進行調節(jié)，內(nèi)環(huán)采用以轉矩誤差的平方為性能指標函數(shù)的FNN自適應PID對轉矩進行調節(jié)，達到了較為理想的控制效果。 在MATLAB/SIMULINK環(huán)境下，仿真結果表明上述兩種控制策略均能有效地抑制轉矩脈動。在仿真研究的基礎上，對基于大腦情感學習模型的SRM電流分配控制策略、SRM直接瞬時轉矩控制策略和SRM電壓斬波控制策略在SRM實驗平臺上進行了實驗測試。實驗結果表明前兩種控制策略的轉矩脈動抑制效果明顯優(yōu)于傳統(tǒng)的電壓斬波控制策略。
[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.
【學位授予單位】：桂林電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM352
本文編號：2180047
[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.
【學位授予單位】：桂林電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM352
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