臺灣博碩士論文加值系統

灰色系統理論可以改善系統響應同時也可預測系統行為變化趨勢，因此具有事前控制及防患未然的能力。所以已廣範的應用在氣候、地震、軍事以及控制等預測上且有不錯的效果。
傳統灰色預測控制器的缺點在於其預測步距為固定值，由實驗測試得知，以預測系統輸出作回授控制時，若預測步距較小系統響應雖快但會有超越量，而較大的預測步距將造成過量補償，以致於系統響應變慢。所以結合模糊預測步距與灰色預測結構，可提供適當的預測量以補償系統。所以它的控制效果較固定步距之灰色預測控制器及比例積分控制器為佳。
本研究分別將仿線性、內涵型及差分型灰色預測模式結合模糊預測步距而成為仿線性模糊灰色預測控制器、內涵型模糊灰色預測控制器及差分型模糊灰色預測控制器。我們首先將它們植入三階系統中，經由模擬得知，三種模糊灰色預測控制器之性能的確優於傳統比例積分(PI)控制器。最後本研究將他們植入三相感應馬達之向量控制系統中以改善馬達之動態特性，並且驗証其實用性，由實驗結果証明，其強鍵的適應能力極適合作為感應機向量控制系統之控制器。

The grey system theory can not only improve system’s response, but also predict the tendency of its variation.
Therefore, it has a control and prevention ability in advance. It has been widely applied on varieties of fields, and has good effects on climate, earthquake, military and prediction control.
However, the drawback of the conventional grey prediction controller is its fixed prediction step size. The smaller the step size, the faster the response but with larger overshoot. The larger the step size, the slower the response. By combining fuzzy and grey prediction theory, an appropriate prediction step size can be obtained for compensation. Therefore, the effectiveness of the proposed scheme is better than the conventional PI controller and the grey prediction controller with fixed prediction step.
Three types of fuzzy prediction controllers were studied in this project. One is the linearlized fuzzy grey prediction controller (LFGPC), which combines the linearlized grey model and fuzzy theory. Another is the intensive fuzzy grey prediction controller (IFGPC), which combines the intensive grey model and fuzzy theory. The other is the difference fuzzy grey prediction controller (DFGPC), which combines the difference grey model and fuzzy theory. We simulated and analyzed a third-order system to verify the feasibility of the above three controllers. In addition, we applied them to the three-phase induction motor speed control system in order to prove their practicability. Experiment results closely meet the computer simulation results. Their performances are better than PI controller’s whose robust adaptability make them suitable for vector control systems of induction motors.

目 次
摘要 iii
誌謝 v
目次 vi
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
第二章 灰色系統理論 4
2.1 背景說明 4
2.2 灰色系統理論簡介 5
2.3 灰色模型導論 6
2.4 數據擷取與處理 7
2.5 灰色建模技術 10
2.6 仿線性灰色預測模型 11
2.7 內涵型灰色預測模型 13
2.8 差分型灰色預測模型 17
第三章 模糊控制理論 22
3.1 模糊理論起源 22
3.2 模糊集合 22
3.3 模糊邏輯控制設計 25
3.4 模糊推論 28
第四章 模糊灰色預測控制器設計 31
4.1 灰色預測控制器結構 31
4.2 模糊預測步距 35
4.3 仿線性模糊灰色預測控制器設計 38
4.4 內涵型模糊灰色預測控制器設計 40
4.5 差分型模糊灰色預測控制器設計 42
第五章 感應電動機磁場導向控制系統 44
5.1 簡介 44
5.2 感應電動機數學模型 45
5.3 向量控制系統之LFGPC設計 50
5.4 向量控制系統之IFGPC設計 62
5.5 向量控制系統之DFGPC設計 63
第六章 感應機向量控制之模擬與實驗 66
6.1 感應機仿線性模糊灰色預測控制模擬與實驗 66
6.2 感應機內涵型模糊灰色預測控制模擬與實驗 104
6.3 感應機差分型模糊灰色預測控制模擬與實驗 140
第七章 結論與未來研究方向 176
7.1 結論 176
7.2 未來研究方向 176
參考文獻 178
附錄
A 三相感應電動機參數 181
B 三相感應電動機驅動系統實際配置圖 182
作者簡介 183

[1] C. H. Liu, C. C. Chen and L. C. Chou, “Microprocessor-Based Field-Oriented Control of Current Source Inverter-Fed Induction Motor Drives,” Journal of the Chinese Institute of Engineers, Vol. 10, No. 1, pp. 53-66, 1987.
[2] H. W. Broeck, H. C. Skudelny, and G. V. Stanke, ”Analysis and Realization of a Pulse width Modulator Based on Voltage Space Vectors,” IEEE Trans. Ind. Appl., Vol. 24, NO.1, JAN/FEB 1988.
[3] S. Ogasawara, H. Akagi and A. Nabae, “The Generalized Theory of Indirect Vector Control for AC Machines,” IEEE Trans. Ind. Appl., Vol. 24, No. 3, pp. 470-478, May/June 1988.
[4] H. W. Vander Broeck, H. C. Skudelny and G. V. Stanke, “Analysis and Realization of a Pulse width Modulator Based on Voltage Space Vectors,” IEEE Trans. Ind. Appl., Vol. 24, No.1, pp. 142-150, January/Feburary 1988.