臺灣博碩士論文加值系統

適應性控制應用於工業界已有一段相當長之時間，模型規範適應性控制器在整個適應性控制中是一相當重要之控制方式，但模型規範適應性控制對於控制參數之推導無論是採用MIT法則或Lyapunovc函數都非常困難，整個系統之響應更因不同之適應性增益而有很大之差異，所以實用上控制初期需先交由PID控制器控制，待系統穩定後再交由模型規範適應性控制器進行控制，因此造成使用上之不方便。
本論文主要是探討將倒傳遞類神經網路和模型規範適應性控制器作結合。利用倒傳遞類神經網路對受控系統作預測，並將預測值提供給模型規範適應性控制器之調整機構，調整機構則利用此預測值和理想模型之值作比較，以便對受控系統進行控制，如此可減少因模型規範適應性控制器中調整機構根據MIT法則或Lyapunov 函數對受控系統作參數調整，所造成之困擾。文中分別介紹類神經網路之種類及應用範圍，並介紹模型規範適應性控制使用之MIT及Lyapunov函數兩大法則，最後模擬結果證實此種加入倒傳遞類神經網路之模型規範適應性控制器有更好之暫態響應。

Adaptive controllers have been used in industrial field for many years. The Model Reference Adaptive Control is an important control method in adaptive control field. The key problem with MRAS (Model Reference Adaptive System) is to determine the adjustment mechanism. The Mechanism for adjusting the parameters in a MRAC can be obtained in two ways: one is using a gradient method (also called MIT rule), the other is applying Lyapunov stability theory. The MIT rule has one parameter, the adaptation gain, that must be chosed by the user. It is difficult to find a Lyapunov function. So it is not convenient to use a Model Reference adaptive controller.
A new method of MRAS is proposed in this thesis. It combines the Back-Propagation Network (BPN) and the Model Reference Adaptive Control (MRAC). The BPN can provide a forecasting value of a plant. The adjustment mechanism adjusts the controller parameters in such a way that the error, which is the difference between BPN forecasting value and model output, is small. This method can control MRAS in a good response without finding adaptation gain or Lyapunov function. Computer simulation results show that the new method can achieve good transient response.

目 錄
中文摘要…………………………………………………………………… i
英文摘要…………………………………………………………………… iii
誌謝………………………………………………………………………… v
目錄………………………………………………………………………… vi
圖表目錄…………………………………………………………………… viii
第一章、緒論……………………………………………………………… 1
1.1 研究背景及動機………………………………………………… 1
1.2 研究目的………………………………………………………… 2
1.3 章節概述………………………………………………………… 3
第二章、類神經網路……………………………………………………… 5
2.1 簡介……………………………………………………………… 5
2.2 類神經網路之學習種類………………………………………… 7
2.3 類神經網路之種類及應用……………………………………… 9
2.4 倒傳遞類神經網路(Back-Propagation Network)之學習及回
想過程…………………………………………………………… 12
第三章、模型規範適應性控制器………………………………………… 20
3.1 簡介……………………………………………………………… 20
3.2 MIT 法則………………………………………………………… 21
3.3 Lyapunov 穩定性定理 ………………………………………… 28
3.4 類神經網路為基礎之模型規範適應性控制器………………… 31
第四章、模擬結果………………………………………………………… 36
第五章、結論……………………………………………………………… 53
參考文獻…………………………………………………………………… 54
授權書……………………………………………………………………… 58

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