- 學位論文
車削系統之自組織模糊滑動模式類神經網路控制器
Self-Organizing Fuzzy Sliding-Mode Radial Basis-Function Neural-Network Controller for Turning Systems
摘要
車削系統一般都具有複雜和非線性的特點, 使得難以確切設計出以模式為基礎的控制器,來改善車削系統的控制性能。 為了解決這個問題， 本研究發展出自組織模糊滑動模式類神經網路控制器(SFSRBNC) 以保持車削系統之定力切削，SFSRBNC 不僅消除了自組織模糊控制器 (SOFC) 和自組織模糊滑動模式控制器 (SFSC), 在參數選擇的問題也決定了模糊控制器之適當的隸屬函數與模糊規則表， 並且由 SFSRBNC 解決了自組織模糊類神經網路控制器 (SFRBNC) 穩定性的問題。經由模擬結果證實, SFSRBNC 實現之控制性能優於 SOFC, SFSC, SFRBNC, 以控制車削系統之定力切削和定力切削與固定金屬移除率。
並列摘要
Turning systems generally have nonlinear and complex characteristics, so the design of model-based controllers to manipulate such systems to improve their control performances is impractical. To address this problem, this study developed a self-organizing fuzzy sliding-mode radial basis-function neural-network controller (SFSRBNC) for the control of turning systems. The SFSRBNC not only eliminates 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 the inappropriate membership functions and fuzzy rules for the design of a fuzzy logic controller, but also solves the stability problem of a self-organizing fuzzy radial basis-function neural-network controller (SFRBNC) application. Simulation results indicated that the SFSRBNC achieved better control performance than the SFSC, SFRBNC, and SOFC for the control of the constant cutting force, with or without fixed material removal rate, in turning.