摘 要:为了准确对感应电机磁链进行实时估计,提出了一种基于人工神经网络(ANN)电压电流互补模型的估计方案。该方案利用基于ANN的电压与电流模型之间的磁链估计差值在线对电机参数进行更新,以减小因电机参数变化所引起的模型偏差,并最终通过电机频率范围决策转子磁链。实验结果表明,该方案对磁链估计有效,实时控制效果好。 关键词:磁链估计;人工神经网络;电压模型;电流模型;感应电机
Abstract: In order to estimate the induction motor’s online flux accurately, a estimation method using voltage-current model based on artificial neural networks(ANN) was proposed. Online updating the induction motor’s parameters were implemented by the error of flux estimation between the voltage model and current model based on ANN, thereby, reduced the model error because of parameter’s change. Furthermore, rotor flux estimation was selected by motor frequency. Experimental results show that this method obtained the high precision rotor flux and high-performance. Key words: flux estimation; artificial neural networks; voltage model; current model; AC motor
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