針對未知資料取樣系統，本論文藉由數位重新設計技術提出觀測器/卡爾曼濾波器鑑別為基礎的反覆學習控制和高效能追蹤器，以達到良好的追蹤效果與閉迴路解耦特性。其研究主題陳述如下：首先，針對輸出響應追蹤問題，提出一種適用於非線性資料採樣系統的觀測器/卡爾曼濾波器鑑別為基礎的反覆學習式軌跡追蹤器。接著，針對由多個互聯子系統所組成的大尺度時延系統之控制問題，提出以觀測器/卡爾曼濾波器鑑別為基礎的離散型反覆學習式軌跡追蹤器，以同時獲得強健的解耦特性與良好的追蹤效果。最後，將類比反覆學習機制和具有高增益特性追蹤器的結合，提出一個具有高性能的反覆學習追蹤器，以克服傳統反覆學習控制在學習代數、初始條件和不連續的輸入參考訊號的問題。另外，這個追蹤器也改善了在傳統類比的線性二次式追蹤器和數位再設計追蹤器在暫態響應和權重矩陣比例的問題。在本論文中，以多個例題來說明所提方法之有效性。

This dissertation proposes observer/Kalman filter identification (OKID)-based iterative learning control (ILC) and high performance tracker for unknown sampled-data systems to obtain good tracking performances and a closed-loop decoupling property by the digital redesign technique. The major research topics of this dissertation are stated as follows: Firstly, an OKID-based iterative learning tracker is proposed to resolve the output tracking problem for nonlinear of sampled-data systems. Secondly, to deal with the control problem of large-scale state-delay systems consisting N interconnected subsystems, an OKID-based discrete iterative learning scheme is developed to simultaneously obtain the robust decoupling property in time domain and the good tracking performance in iterative domain. Finally, to combine analog ILC method and the high-gain property tracker design methodology, a high performance iterative learning tracker is developed to resolve some problems of the traditional ILC on learning epoches, initial condition and discontinuous reference input. Besides, the proposed method also improves the transient response and decrease the ratio of weighting matrices under the traditional analog linear quadratic tracker and digital redesign tracker. Some illustrative examples are given to demonstrate the effectiveness of the proposed methodologies.

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