臺灣博碩士論文加值系統

本研究發展一自組織模糊滑動模式類神經網路控制器， 以控制非線性系統。 用於自組織模糊滑動模式類神經網路控制器裡面的模糊邏輯控制器之輸入變數，是滑動平面以及其變化量，而不是誤差與誤差變化，這能確保系統運行的穩定性。 自組織模糊滑動模式類神經網路控制器不僅削除了使用自組織模糊類神經網路控制器的穩定性問題，也克服了自組織模糊控制器與自組織模糊滑動模式控制器兩者因為參數選擇不適當所產生的問題， 以及在模糊控制器中因定義不適合的隸屬函數和模糊規則所產生的問題。 為了證明所提出的方法之可行性，我們將自組織模糊滑動模式類神經網路控制器用於控制非線性系統， 其包含微制動器系統以及機械手臂系統， 以確定其控制性能。 而模擬結果證實了使用自組織模糊滑動模式類神經網路控制器來控制非線性系統能夠得到比使用自組織模糊控制器, 自組織模糊滑動模式控制器, 和自組織模糊類神經網路控制器更好的控制性能。

This study developed a self-organizing fuzzy sliding-mode radial basis-function neural-network controller (SFSRBNC) for nonlinear systems. The sliding surface and its differential, rather than the error and error change of the system,are used as input variables of a fuzzy logic controller (FLC) in the SFSRBNC, which guarantees the stability of the system operation. The SFSRBNC not only eliminates the stability problem of a self-organizing fuzzy radial basis-function neural-network controller (SFRBC) application,but also overcomes the problem caused by the inappropriate selection of parameters in both a self-organizing fuzzy controller (SOFC) and a self-organizing fuzzy sliding-mode controller (SFSC), and by the determination of unsuitable membership functions and fuzzy rules in an FLC.To demonstrate the feasibility of the proposed method, the SFSRBNC was applied to controlling nonlinear systems which are a microactuator system and a robotic system to determine their control performances.
Simulation results verified that the SFSRBNC gained better control performance than the SFRBC, SFSC, and SOFC for the control of the nonlinear systems.

中文摘要. . . . . . . . . . . . . . . . . . . . . . . .i
英文摘要. . . . . . . . . . . . . . . . . . . . . . . ii
誌謝 . . . . . . . . . . . . . . . . . . . . . . . . . iii
目錄. . . . . . . . . . . . . . . . . . . .. . . . . . iv
表目錄 . . . . . . . . . . . . . . . . . . . . . . . . vi
圖目錄. . . . . . . . . . . . . . . .. . . . . . . . . vii
第一章緒論 . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 研究動機與目的 . . . . . . . . . . . . . . . . . . . 1
1.2 文獻回顧 . . . . . . . . . . . . . . . . . . . . . . 1
1.2.1 靜電微致動器系統控制之文獻回顧 . . . . . . . . . . 1
1.2.2 機械手臂系統控制之文獻回顧 . . . . . . . . . . . . 3
1.3 本文架構 . . . . . . . . . . . . . . . . . . . . . . 4
第二章系統的數學模式 . . . . . . . . . . . . . . . . . . 5
2.1 平行板式靜電微致動器系統簡介 . . . . . . . . . . . . 5
2.1.1 平行板式靜電微致動器之動態方程式 . . . . . . . . . 5
2.1.2 平行板式靜電微致動器之吸附效應 . . . . . . . . . . 7
2.2 機械手臂系統簡介 . . . . . . . . . . . . . . . . . .10
2.2.1 二軸機械手臂系統之動態方程式 . . . . . . . . . . .10
2.2.2 三軸機械手臂系統之動態方程式 . . . . . . . . . . .14
第三章控制器的設計. . . . . . . . . . . . . . . . . . . 18
3.1 自組織模糊控制器 . . . . . . . . . . . . . . . . . 18
3.1.1 模糊控制理論 . . . . . . . . . . . . . . . . . . .18
3.1.2 自組織模糊控制器的設計 . . . . . . . . . . . . . .21
3.1.3 應用於系統之自組織模糊控制器的設計. . . . . . . . 25
3.2 自組織模糊滑動模式控制器 . . . . . . . . . . . . . .26
3.2.1 滑動模式控制理論 . . . . . . . . . . . . . . . . .27
3.2.2 應用於控制系統之自組織模糊滑動模式控制器的設計. . 29
3.3 自組織模糊類神經網路控制器 . . . . . . . . . . . . .31
3.3.1 徑向基底函數類神經網路. . . . . . . . . . . . . . 31
3.3.2 應用於控制系統之自組織模糊類神經網路控制器的設計. . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4 自組織模糊滑動模式類神經網路控制器 . . . . . . . . .36
3.4.1 應用於控制系統之自組織模糊滑動類神經網路控制器的設計. . . . . . . . . . . . . . . . . . . . . . . . . . . 36
第四章數值模擬結果與分析. . . . . . . . . . . . . . . . 38
4.1 平行板靜電微致動器系統之數值模擬分析. . . . . . . . 38
4.1.1 SOFC 與 SFRBNC 之控制性能比較 . . . . . . . . . . 39
4.1.2 SFSC 與 SFSRBNC 之控制性能比較 . . . . . . . . . .54
4.2 二軸機械手臂系統之數值模擬分析. . . . . . . . . . . 68
4.2.1 SOFC 與 SFRBNC 之控制性能比較 . . . . . . . . . . 69
4.2.2 SFSC 與 SFSRBNC 之控制性能比較 . . . . . . . . . .83
4.3 三軸機械手臂系統之數值模擬分析. . . . . . . . . . . 96
4.3.1 SOFC 與 SFSC 之控制性能比較 . . . . . . . . . . . 97
第五章結論. . . . . . . . . . . . . . . . . . . . .. . 103
5.1 本研究之結論. . . . . . . . . . . . . . . . . . . .103
參考文獻. . . . . . . . . . . . . . . . . . . . . . . .105
著作發表. . . . . . . . . . . . . . . . . . . . . . . .110

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