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研究生:周彥騰
研究生(外文):Yan-Teng Chou
論文名稱:適應性類神經網路於單缸膜片式 氣壓隔振系統之控制
論文名稱(外文):Adaptive neural network control for a diaphragm-type pneumatic vibration isolator
指導教授:梁晶煒梁晶煒引用關係陳宏毅陳宏毅引用關係
指導教授(外文):Jin-Wei LiangHung-Yi Chen
口試委員:林志哲
口試委員(外文):Chih-Jer Lin
口試日期:2015-01-29
學位類別:碩士
校院名稱:明志科技大學
系所名稱:機械工程系機械與機電工程碩士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:92
中文關鍵詞:單缸膜片氣壓隔振系統小波類神經網路輻射類神經網路學習能力
外文關鍵詞:Diaphragm-type pneumatic vibration isolatoradaptive wavelet neural network controlleradaptive radial basis function neural networkTaguchi method
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由於氣體的可壓縮性及氣壓系統孔口流等非線性之特性,使氣壓隔振系統具有時變與高度非線性,若要建立系統正確之數學模式是非常不容易的。所以本研究嘗試以適應性類神經網路控制法則,針對主動式氣壓隔振系統進行即時控制。因為類神經網路具有強大的學習能力,高度的容忍誤差、平行運算之學習能力等特性,所以可以應用於非線性動態函數之近似。本研究以適應性類神經網路控制法則,包括適應性來小波類神經網路及適應性輻射函數類神經網路,結合函數近似法則來進行控制器之設計,針對主動式單缸膜片氣壓隔振系統進行隔振控制,實驗過程中並利用田口法則來求得系統控制之最佳參數。從實驗結果可以得知,本研究所設計之控制器在單缸膜片式氣壓隔振系統之控制中,可呈現明顯之隔振成效。
It is well known that a pneumatic actuating system has nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate process model for designing a model-based controller to monitor the pneumatic actuating force. An intelligent control strategy for a pneumatic vibration isolation system is developed in this research. In this paper, a model-free adaptive wavelet neural network (AWNN) controller and radial basis function neural network (ARBFNN) controller is proposed to control a diaphragm-type pneumatic vibration isolator. This approach has online learning ability and the advantage to achieve the controller design without knowledge of the system dynamic model. In order to validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. In addition, Taguchi method had been utilized to obtain the optimal control gain values for this control system. Experimental results are executed to show the control performance of the proposed intelligent controller.
明志科技大學碩士學位論文指導教授推薦書 i
明志科技大學碩士學位論文口試委員會審定書 ii
誌謝 iii
中文摘要 iii
目錄 vi
表目錄 viii
圖目錄 x
第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 2
1.3 研究動機與目的 5
1.4 論文架構 6
第二章 主動式氣壓隔振系統 7
2.1 主動式氣壓隔振系統架構 7
2.2 控制單元 8
2.2.1 個人電腦 8
2.2.2 Reconfigure Controller 9
2.2.3 壓力感測器 13
2.2.4 加速度感測器 14
2.3 致動系統 15
2.3.1 單缸膜片式氣壓驅動器 15
2.3.2 氣壓流量比例閥 16
2.3.3 激振器 19
第三章 控制理論 20
3.1 簡介 20
3.2 類神經網路控制器原理 20
3.2.1 類神經網路簡介 20
3.2.2 人工神經元模型 21
3.2.3 適應性小波類神經網路 24
3.2.4 適應性輻射類神經網路 29
3.2.5 適應性輻射類神經網路控制器 33
3.2.6 函數近似法 36
3.2.7 FA+類神經網路控制器 38
第四章 實驗結果 41
4.1 系統動態測試 41
4.2 實驗分析 42
4.2.1 田口法介紹 43
4.2.2 AWNN之實驗結果 64
4.2.3 FA+AWNN之實驗結果 72
4.2.4 ARBFNN之實驗結果 76
4.2.5 FA+ARBFNN之實驗結果 81
第五章 結論與未來研究 88
第六章 參考文獻 89

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