- 學位論文
自組織模糊滑動模式徑向基底函數類神經網路控制器在切削系統的應用
Self-organizing Fuzzy Sliding mode Radial Basic-function Neural-network controller for cutting systems
摘要
自組織模糊控制器已被應用在控制工程上。在控制過程中, 它可以重零規則庫不斷更新自組織模糊控制器之模糊規則庫。它改善了適當找到隸屬函數和模糊規則設計的模糊邏輯控制器問題。但是,在工程上的應用,自組織模糊控制器的參數學習速率和權重因子不易調整的問題。為了解決自組織模糊控制器所引起的問題, 本研究提出自組織模糊滑動模式徑向基底函數類神經網路控制器。自組織模糊滑動模式徑向基底函數類神經網路控制器應用徑向基底函數類神經網路即時調整自組織模糊控制器的學習速率和權重因子來得到適當的參數。為了確認自組織模糊滑動模式徑向基底函數類神經網路控制器的適用性, 自組織模糊滑動模式徑向基底函數類神經網路控制器應用系統操作的轉折點。然後對控制型能進行了評估。由模擬結果得知, 自組織模糊滑動模式徑向基底函數類神經網路控制器比自組織模糊控制和自組織模糊滑動模式控制器有較好的控制性能, 改善定力切削控制性能。
並列摘要
A self-organizing fuzzy controller (SOFC) has been proposed to control engineering applications. During the control process, the SOFC continually updates the learning strategy in the form of fuzzy rules, beginning with empty fuzzy rules. This eliminates the difficulty of finding appropriate membership functions and fuzzy rules for the design of a fuzzy logic controller. It is, however, intricate to select appropriate parameters for both the learning rate and weighting distribution in the SOFC for control engineering applications. To solve the problem caused by the SOFC, this study developed a self-organizing fuzzy sliding mode radial basis-function neural network controller (SOFSMRBNC). The SOFSMRBNC used a radial basis function neural-network (RBFN) to regulate in real-time these parameters of the SOFC to appropriate values for gaining perfect control performance of the system. To confirm the applicability of the proposed SOFSMRBNC, the SOFSMRBNC was applied in manipulating a turning system. Then its control performance was evaluated. Simulation results demonstrated that the SOFSMRBNC has better control performance than both the SOFC and self-organizing fuzzy sliding mode controller in improving the performance of the constant cutting force operation.
參考文獻
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