臺灣博碩士論文加值系統

自組織模糊控制器(self-organizing fuzzy controller, SOFC) 已被應用在控制工程領域上, 然而, 控制過程中SOFC 不易找尋適當的學習速率和權重因子。為了解決這個問題,本研究發展出灰預測自組織模糊滑動模式徑向基函數類神經網路控制器(grey-prediction
self-organizing fuzzy sliding-mode radial basis-function neural-network controller, GPSFSRBNC)。該GPSFSRBNC 利用灰色預測演算法預測系統下一步的誤差做為控制器輸入。它不僅銷除了SOFC 和自組織模糊滑動模式控制器(self-organizing fuzzy slidingmodecontroller, SFSC) 因參數選擇不當引起的問題, 也解決了自組織模糊徑向基函數類神經網路控制器(self-organizing fuzzy radial basis-function neural-network controller,
SFRBNC) 穩定性的問題。此外, 相較於自組織模糊滑動模式徑向基函數類神經網路(selforganizingfuzzy sliding-mode radial basis-function neural-network controller, SFSRBNC), GPSFSRBNC 因擁有預測的特性, 提供了更好的控制性能。GPSFSRBNC 已用於控制主
動式懸吊系統已確定其控制性能。經由模擬結果證實, GPSFSRBNC 比SOFC, SFSC, SFRBNC, SFSRBNC 以及被動式控制, 在乘坐汽車的舒適性及汽車的操控性上達到更好的控制性能。

Self-organizing fuzzy controllers (SOFCs) have been applied to the control engineering fields. However, it is difficult to find appropriate parameters of learning rate and
weighting distribution for the design of an SOFC. To solve the problem, this study developed a grey prediction self-organizing fuzzy sliding-mode radial basis-function neuralnetwork controller (GPSFSRBNC). The GPSFRBNC uses a grey-prediction algorithm to predict the next step error of the system for the controller design. It not only eliminates
the problem caused by the inappropriate selection of parameters in both an SOFC and a self-organizing fuzzy sliding-mode controller (SFSC), but also solves the stability problem of a self-organizing fuzzy radial basis-function neural-network controller (SFRBNC) application. Moreover, as compared with a self-organizing fuzzy sliding-mode radial basis-function neural-network (SFSRBNC), the GPSFRBNC has a predicted property, thereby providing better control performance. The GPSFRBNC was employed to control
an active suspension system to determine its control performance. Simulation results demonstrated that the GPSFSRBNC achieved better control performance than the SOFC,
SFSC, SFRBNC, SFSRBNC as well as passive control, in terms of the ride comfort and the road-holding capability of the vehicle for active suspension control.

目錄
中文摘要
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英文摘要
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誌謝
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目錄
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表目錄
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圖目錄
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第一章緒論
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1.1 研究動機與目的
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1.2 懸吊系統分類
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1.3 文獻回顧
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1.4 本文架構
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第二章系統的數學模式
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2.1 主動式懸吊系統簡介
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2.2 主動式懸吊系統之數學模式
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第三章控制器的設計
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3.1 模糊控制理論
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3.2 自組織模糊控制器的設計
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3.2.1 自組織模糊演算法
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3.2.2 主動式懸吊系統之自組織模糊控制器的設計
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3.3 自組織模糊滑動模式控制器的設計
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3.3.1 滑動模式控制理論
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3.3.2 主動式懸吊系統之自組織模糊滑動模式控制器的設計
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3.4 自組織模糊徑向基類神經網路控制器的設計
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3.4.1 徑向基底函數類神經網路
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3.4.2 主動式懸吊系統之自組織模糊徑向基類神經網路控制器的設計
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3.5 主動式懸吊系統之自組織模糊滑動徑向基類神經網路控制器的設計. . . . . . . 28
3.6 灰預測自組織模糊滑動模式徑向基函數類神經網路控制器的設計
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3.6.1 GM(1,1) 模型灰色預測
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3.6.2 主動式懸吊系統之灰預測自組織模糊滑動模式徑向基函數類神經網路控制器的設計
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第四章數值模擬結果與分析
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4.1 正弦波路面擾動之懸吊系統的主動控制
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4.2 凸塊路面擾動之懸吊系統的主動控制
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4.3 隨機路面擾動之懸吊系統的主動控制
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第五章結論
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5.1 本文重要結論
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5.2 未來展望
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參考文獻
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