在過去，電壓/頻率(V/f)控制被應用於感應馬達之開迴路速度控制。電壓/頻率控制方法是相當容易去實現的，電壓主要是根據電器頻率之命令來做調整。然而，因為定子阻抗之影響和需要轉子滑差去產生轉矩之因素，此控制方法在低轉速之應用仍然是件艱鉅的工作。本論文提出間接磁場導向控制(Field-oriented control)將會解決上面之問題。
為了達到速度回授的閉迴路控制，精確的轉子速度對於感應馬達之控制是相當重要的。在過去，編碼器廣泛的被用來做為量取感應馬達速度之感測器。然而，使用速度感測器將會提高整個系統之費用和降低系統之可靠度。除此之外，對於高轉速馬達驅動之情況下，在安裝速度感測器時將會遇到很大的困難。無速度感測器之控制方法將可解決以上提到之問題。
本論文提出一個新式自動調整的模糊類神經網路演算法應用於感應馬達之速度估測。速度估測主要是依據轉子磁通和估測轉子磁通之推導得到，轉子磁通是由感應馬達之動態系統推導而來，而估測轉子磁通則是由模糊類神經網路計算出來。模糊類神經網路包括了四層架構，以轉子磁通和估測轉子磁通之誤差，並利用最深梯度法和倒傳遞法去調整模糊類神經網路之參數，使得轉子磁通和估測轉子磁通之間的誤差減到最小，這樣一來就可以應用於精確的轉子速度之估測。Lyapunov’s的穩定度推導和模糊類神經網路之收斂性將會被證明，並由此可以確定此系統是穩定的。

In the past, Voltage/frequency (V/f) control was used in open loop speed control for the induction motor. The V/f control is simple to implement. The voltage (V) is adjusted proportionally to frequency (f) command. However, its application is still challenging at low frequency due to the influence of the stator resistance and the necessary rotor slip to produce torque. The foregoing problem can be solved by using the field-oriented control.
In order to achieve the speed loop feedback control, precise rotor speed information is important for induction motor control. In the past, encoder was widely used to obtain the speed information of induction motor. However, speed sensor would increase the cost of entire system and reduce the system reliability. In addition, for some special applications such as very high speed motor drives, some difficulties are encountered in mounting these speed sensors. The speed sensorless control would overcome these problems mentioned above.
This thesis proposes a novel self-tuning fuzzy neural network algorithm for speed sensorless induction motor drives. The speed estimation is based on the deduction of rotor flux and estimated rotor flux. The rotor flux is derived from the dynamic model of the induction motor. The estimated rotor flux is calculated by fuzzy neural network. The fuzzy neural network includes a four-layer network. The gradient descent and back-propagation are used to adjust the parameters of fuzzy neural network in order to minimize the error between the rotor flux and the estimated rotor flux, which is implied to enable precise estimation of the rotor speed. Finally, the Lyapunov’s stability theorem and convergence of the fuzzy neural network used to prove that the system is stable.

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