Torque ripple minimization in direct torque control of induction motors
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摘要: 針對異步電動機直接轉矩控制低速轉矩脈動大的問題,充分利用模糊控制吸收人的經驗思維,以及神經網絡對信息的處理具有自組織、自學習的特點,提出一種新的模糊神經網絡控制方法.該方法實現了逆變器開關周期的占空比控制,使感應電動機的轉矩脈動達到最小.其中,模糊神經網絡的訓練采用最小二乘法,解決了常規的BP算法容易陷入局部極小的問題.將傳統的直接轉矩控制方案和模糊神經網絡占空比控制方案進行了比較研究,仿真結果校驗了模糊神經網絡占空比控制方案的有效性.Abstract: As kind of new fuzzy to large torque ripple in direct torque control (DTC) of induction motors at low speed, a neural networks (FNN) approach was proposed based on the merits that fuzzy control absorbs man's empirical thinking and neural networks have self-organization and self-study ability. The new approach achieved inverter switch's duty ratio control and made torque ripple minimum. The problem that BP algorithm easily gets into local minimum was solved by using least square method for the training of fuzzy neural networks. By comparing traditional DTC approach with FNN duty ratio control scheme, the effectiveness of FNN duty ratio control scheme was verified.
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