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首页> 外文期刊>International Journal of Power Electronics and Drive Systems >Performance Comparison of Starting Speed Control of Induction Motor
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Performance Comparison of Starting Speed Control of Induction Motor

机译：感应电动机启动速度控制的性能比较

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In the induction motor speed control without sensors operated by the method Field Oriented Control (FOC) was required an observer to estimate the speed. Obsever methods have been developed, among others, was the method of Self-Constructing Fuzzy Neural Network (SCFNN) with some training algorithms such as backpropagasi (BP). Levenberg Marquard (LM) etc.. In the induction motor control techniques were also developed methods of Direct Torque Control (DTC) with observer Recurrent Neural Network (RNN). This paper compares the performance of the motor response to initial rotation between SCFNN observer method that uses the LM training algorithm with DTC control technique with RNN observer. From the observation performance of the motor response to initial rotation of the two methods shows that the LM method has better performances than the RNN. This can be seen on both the parameters : overshoot, rise time, settling time, peak and peak time. With the right method, can enhance better performance of the system. With the improvement of system performance, is expected to increase work efficiency in the industrial world, so overall, particularly for systems that require high precision, FNN methodcan be said to be better. Keywords: Motor Speed control without sensors, FOC, SCFNNO, DTC, Levenberg Marquardt and RNN
机译：在无传感器的感应电动机速度控制中，需要使用磁场定向控制（FOC）的方法来观察速度。已经开发了很多方法，其中包括带有反向传播算法（BP）的自训练模糊神经网络（SCFNN）方法。 Levenberg Marquard（LM）等。在感应电动机控制技术中，还开发了带有观测器递归神经网络（RNN）的直接转矩控制（DTC）方法。本文比较了SCFNN观察者方法与初始训练的电机响应性能，该方法使用LM训练算法和DTC控制技术以及RNN观察者。从电动机对初始旋转响应的观察性能来看，这两种方法表明LM方法具有比RNN更好的性能。这可以在两个参数上看到：过冲，上升时间，建立时间，峰值和峰值时间。用正确的方法，可以增强系统的更好性能。随着系统性能的提高，有望提高工业界的工作效率，因此总体而言，特别是对于要求高精度的系统而言，可以说FNN方法更好。关键字：无传感器的电动机速度控制，FOC，SCFNNO，DTC，Levenberg Marquardt和RNN

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