本論文主要探討整合壓電平台與龍門平台用於精密定位噴印平台之控制型噴印。龍門平台之精密定位控制方法主要是使用反覆式學習控制器，藉由反覆多次學習的次數中來更新未來的命令，也就是說這一次學習來作為下次控制修正的經驗，依據輸入與輸出的誤差來作改變，透過反覆式學習動作，將誤差量降至最低。在壓電平台部份是用採用Bouc-Wen model來建立系統模型，並採用遺傳演算法來求解LMI最佳化問題，來搜尋壓電平台系統上的參數。最後再結合磁滯觀測器來設計前饋控制器與PID回授控制器來補償磁滯現象來提高定位精度。

The purpose of this paper is to integrate a gantry stage with a PZT actuated stage to achieve precision positioning tasks. The iterative learning control is applied to the gantry stage to achieve the tracking task by the iteration. The learning control is based on the input and output of the error at each step to make the final error minimal. Piezoelectric-actuated stage is modeled by Bouc-Wen model and the parameter identification is using genetic algorithms to solve the LMI optimization problem. After that, the model is applied to design a feedforward controller to eliminate hysteresis effects; besides, a PID feedback controller is added to the control system to compensate to improve positioning accuracy.

目錄
封面內頁
簽名頁
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
圖目錄 ix
表目錄 xiv

第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 2
1.3 本文內容 3
第二章 壓電平台與龍門平台之架構 4
2.1 龍門平台 4
2.1.1馬達及馬達驅動器硬體設備 6
2.1.1 Next Move ES控制器 7
2.1.3雷射光學尺 10
2.2壓電平台 10
2.2.1 dSPACE控制器 14
第三章 龍門平台控制方法 19
3.1反覆式學習控制器設計 19
3.2 PID-ILC控制器設計 21
3.3反覆式學習控制與PID控制系統整合實驗結果 22
第四章 壓電平台控制方法 31
4.1建立壓電平台系統模型 32
4.2遺傳演算法 34
4.2.1初始族群 35
4.2.2編碼與解碼 35
4.2.3 適應性函數 36
4.2.4 選擇與複製 36
4.2.5 交配 37
4.2.6 突變 37
4.2.7 產生新的族群 37
4.2.8 終止條件 38
4.3遺傳演算法模擬之結果 38
4.4壓電平台控制結果 59
第五章 壓電平台與龍門平台整合結果 70
5.1壓電平台與龍門平台整合 71
第六章 結論 85
參考文獻 86

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