本論文提出一使用兩個雙輸入單輸出之小波模糊類神經網路線上增益調整之內藏式永磁同步馬達驅動系統用以在不同速度命令下達到系統最佳頻寬，為了在不知道馬達參數以及特性的情況下尋找系統最佳頻寬，兩個雙輸入單輸出之小波模糊類神經網路被利用於線上調整PI速度控制器之參數。此外，本論文亦提出使用小波模糊類神經網路對內藏式永磁同步馬達驅動系統線上即時轉動慣量鑑別之技術，估測出之轉動慣量將應用於線上調整內藏式永磁同步馬達驅動系統IP速度控制器參數，並且在不同操作條件下做驗證。最後，本論文亦發展一種機械共振頻率偵測流程。實驗所使用之硬體為應用德州儀器公司生產之浮點數數位訊號處理器TMS320F28075之內藏式永磁同步馬達驅動系統。;In order to achieve the optimal bandwidth at different speed commands, an online auto-tuning technique using two two-input one-output wavelet fuzzy neural networks (WFNNs) for interior permanent magnet synchronous motor (IPMSM) servo drives is proposed in this thesis. Two two-input one-output four-layer WFNNs are proposed for the online auto-tuning of the gains of a proportional-integral (PI) speed controller of the servo motor drive in order to searching the optimal bandwidth without using the information of plant parameters and the characteristics of the servo motor drive. In addition, a real-time moment of inertia identification technique using wavelet fuzzy neural network for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed in this thesis. The estimated moment of inertia will be used in the online design of an integral-proportional (IP) speed controller to achieve the gains auto-tuning of the IPMSM servo drive. The WFNN is proposed for the real-time identification of the moment of inertia of the IPMSM servo drive system in order to tune the gains of the IP speed controller online at different operating conditions. Finally, a method for the detection of mechanical resonance frequency is also developed in this thesis. The experimentation using the IPMSM servo drive based on Texas Instruments′ floating point digital signal processor (DSP) TMS320F28075 is presented.
