以基因演算法為基礎之遞迴式模糊類神經網路 控制線型感應馬達驅動系統

Recurrent Fuzzy Neural Network Controlled Linear Induction Motor Drive Based on Genetic Algorithm

黃柏凱(Po-Kai Huang)

指導教授 ： 林法正 ； 洪穎怡

中原大學/工學院/電機工程研究所/碩士(2003年)

https://doi.org/10.6840/cycu200300138

摘要

本論文之主旨為發展以基因演算法為基礎之遞迴式模糊類神經網路線型感應馬達驅動系統。首先推導出間接磁場導向線型感應馬達的動態模型，並利用個人電腦、AD/DA伺服控制卡及三角波比較電流控制之驅動電路，完成電腦控制之線型感應馬達驅動系統，接著分別提出利用實數型基因演算法線上調整遞迴式模糊類神經網路學習速率及鍵結值之兩種不同的控制系統，以達到強健與精密控制的目的。利用實數型基因演算法來搜尋遞迴式模糊類神經網路學習速率之最佳值，可以減少因利用嘗試錯誤法找出適當的學習速率所花費的時間，並可以保証獲得最佳解。另一方面，利用實數型基因演算法對網路鍵結值作最佳化，除了減少訓練時間之浪費外，亦可避免遞迴式模糊類神經網路由於結構不佳，而降低網路的收斂速度或造成系統發散。本論文並以模擬與實測驗證上述所提出之兩種控制架構的有效性。

關鍵字

遞迴式模糊類神經網路 ； 線型感應馬達 ； 基因演算法

並列摘要

The subject of this thesis is to develop a recurrent fuzzy neural network (RFNN) controlled linear induction motor (LIM) drive based on genetic algorithm (GA). First, the dynamic model of an indirect field-oriented linear induction motor drive is derived. The personal computer (PC) controlled LIM drive system consists of PC, AD/DA servo control card, and a ramp comparison current-controlled PWM. Then, the on-line tuning of the RFNN learning rates and weights using real-coded GA are developed individually to achieve the robust and precise position control of the LIM. Using real-coded GA to search the optimal learning rates of the RFNN can reduce time consumption in the trial-and-error process and guarantee an optimal solution. In addition, using the real-coded GA to search the optimal weights of the RFNN can avoid the network from converging slowly or diverging due to bad network architecture. Finally, the effectiveness of the proposed control schemes is demonstrated by simulated and experimental results.

並列關鍵字

recurrent fuzzy neural network ； linear induction motor ； genetic algorithm

參考文獻

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