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dc.contributor.author李威緒en_US
dc.contributor.authorWeu-xu Lien_US
dc.contributor.author鄧清政en_US
dc.contributor.authorChing-Cheng Tengen_US
dc.date.accessioned2014-12-12T02:24:13Z-
dc.date.available2014-12-12T02:24:13Z-
dc.date.issued1999en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#NT880591075en_US
dc.identifier.urihttp://hdl.handle.net/11536/66308-
dc.description.abstract在本篇論文中我們提出了利用模糊類神經網路之特性,去設計一個不確定系統之PID控制器的方法。這個方法有著快速以及精確度高的特性。設計出來的控制器可以使得即使系統因著外界干擾而改變部分參數時,其頻域穩定規格或者時域性能規格仍能滿足在一定的要求之內,此亦即QFT的設計方法。在本文中,我們針對開迴路非穩定系統,先用模糊類神經網路去訓練時域或者頻域的穩定性能規格與控制器參數間的關係之後,再利用已訓練過的網路去得到一組符合使用者需求規格之PI或者PID控制器參數,而不須靠任何數值分析或做圖法來決定。由結果顯示,模糊類神經網路的確可以有很好的表現。zh_TW
dc.description.abstractIn the thesis, we present a PID tuning method for uncertain processes using Fuzzy Neural Network. This method has the property of high-speed and high-accuracy. The designed controllers make the system, which could be affected by external interference to change the parameters of transfer function, stable and meet the specifications of time domain or frequency domain . This is also the design principle of QFT. In text, a fuzzy neural network approach is proposed to identify the relationship between the specifications of time or frequency domain and the PID controller parameters. Then, the FNN is used to automatically tune the PI or PID controller parameters for the specifications so that neither numerical nor graphical methods need to be used. From the simulation results, we can know that the FNN indeed can performance well. 中文摘要………………………………………………………………………Ⅰ 英文摘要………………………………………………………………………Ⅱ 誌謝…………………………………………………………………………..Ⅲ 目錄…………………………………………………………………………..Ⅳ 圖列…………………………………………………………………………..Ⅵ 表列………………………………………….……….……….…………….Ⅸ 1. 簡介……………………………………………………………………1 2. 模糊類神經系統………………...…...…………… ..….4 2.1 模糊推論系統…………………………..………………………...4 2.1.1 說明GMP……………….………………………………5 2.1.2 規則推論……………………………………………..6 2.1.3 一個簡化的模糊推論系統….....………………….8 2.1.4 模糊推論系統和類神經網路....………………….10 2.2 順向模糊類神經網路…….………...…………………….…...12 2.2.1 網路的結構………….………………………………12 2.2.2 各層的運算……………….…………………………14 2.2.3 學習法則…………….……….…………………….17 2.2.4 初始化……………….…….……………………….21 3. 基於性能規格利用FNN調整PID參數……........….…....…..24 3.1 頻域穩定性簡介……………………………………….24 3.2 基於增益邊限與相位邊限之訓練結構……….28 3.2.1 訓練流程…………………………………29 3.2.2 製造訓練數據……………………………30 3.3 時域性能簡介……..………………………………….34 3.4 基於最大超越量與上升時間的訓練結構…………….35 3.5 利用FNN之訓練步驟與調整方法……………….……….......38 4. 模擬結果…………………………………………………………….41 4.1 PI控制器設計……………………….………………41 4.1.1依頻域穩定規格設計PI控制器………….….….........……42 4.1.2 依時域性能規格設計PI控制器..…………….54 4.2 PID控制器設計……………………………………….65 4.2.1 依頻域規格設計PID控制器..………….65 4.2.2 依時域規格設計PID控制器…………….72 5. 結論………………………………………………………………….78 參考文獻...…………………………………………………………….79en_US
dc.language.isozh_TWen_US
dc.subjectPIDzh_TW
dc.subject參數不確定zh_TW
dc.subject模糊類神經zh_TW
dc.subjectPIDen_US
dc.subjectparameter variationsen_US
dc.subjectFuzzy neural networken_US
dc.title參數不確定系統之PID控制器設計: 利用模糊類神經網路zh_TW
dc.titleTuning of PID controllers for a system with Parameter Variations:A Fuzzy Neural approachen_US
dc.typeThesisen_US
dc.contributor.department電控工程研究所zh_TW
Appears in Collections:Thesis