反覆學習滑動控制應用於電腦數值控制的機電整合系統，能提供良好的控制能力，同時學習並補償工具機在執行相同任務時所遭受的周期性干擾量，能使系統的循跡精度逐代提升。然而，直接對系統設計反覆學習滑動控制時，會需要多狀態回授，因此回授資訊的品質將影響控制效能。在本論文的應用中，以速度回授影響最大，所以本論文透過改良速度估測方法改善反覆學習滑動控制的能力，並搭配四種不同速度估測法的實驗，驗證本論文提出之基於梯度下降法之速度觀測器，能提供較優異的速度回授品質，並同時讓反覆學習滑動控制有更佳的循跡控制能力。另外，有鑑於基本滑動控制具有顫動效應，對高精度循跡運動有嚴重影響，故本論文根據模糊理論及NURBS參數式曲線，分別提出模糊減顫滑動架構及NURBS減顫滑動架構，並透過實驗驗證，搭配這兩種減顫架構之反覆學習滑動控制，可以更進一步降低循跡運動過程中的追蹤誤差。

Iterative learning sliding mode control (ILSMC) is a methodology particularly suitable to cope with periodic disturbance. However, when there is high frequency disturbance in the system, ILSMC becomes ineffective in estimating external disturbance. This is mainly because high frequency disturbance can compromise the accuracy of velocity estimation. The degradation of the accuracy of velocity estimation not only affects the accuracy of estimated disturbance value but also hinders the performance of switching force. In order to cope with this disadvantage, this thesis uses four different types of strategy to improve velocity estimation.The experimental results have verified that the quality of velocity feedback signal can efficiently enhance estimating accuracy for periodic disturbance of ILSMC. Another disadvantage of ILSMC is that it inherits the chattering effect of sliding mode control, which exhibits high frequency oscillations. In order to cope with this disadvantage, this thesis proposes two different types of boundary layer design. In the first scheme, fuzzy logic is used to design saturation function which is in boundary layer. The main idea behind the design is to use human control intuition and logic to adjust the internal structure of boundary layer. Although fuzzy saturation function can reduce chattering effect, it doesn’t work well with machine learning. Therefore, this thesis proposes a scheme that adopts NURBS parametric curve to design a smoother saturation curve. This scheme not only reduces chattering but stabilizes machine learning. In this thesis, theoretical analysis and experimental results are discussed to verify the effectiveness of all the modified iterative learning sliding mode control scheme.

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