本論文提出一個適用在非線性取樣系統之以觀測器/卡爾曼濾波器系
統鑑別法為基底的開迴路反覆學習控制其閉迴路追蹤器，此閉迴路控制法則可以使暫態和穩態相位都具有良好的追蹤效能。以觀測器為基底的數位再設計追蹤器可以抑制未知和非線性干擾。甚至可以不用重新設定其初始條件。首先，類比非線性系統的最佳化模式是被架構在操作點上。依據類比觀測器用於好的設計類比OKID-ILC 非線性系統更新是保證在操作點。然後，線性二次式調整器技術和具高增益特性被應用在最佳線性化模式的類比觀測器為基底的追蹤器。最後，我們提出數個多輸入多輸出的例子，來說明我們所提出方法的可行性。

In the thesis, a novel observer/Kalman filter identification (OKID) OKID-based iterative
learning control (ILC) for a class nonlinear sample-data system is proposed and supplies a good tracking performance in both the transient and steady-state phase. The proposed
observer-based digital redesign tracker can suppress the uncertainties and the nonlinear
perturbations, even without resetting the identical initial condition. First, the optimal linear model of the analog nonlinear system is constructed at the operating point. The operating point is generated due to the analog observer updated by the well-designed analog OKID-ILC nonlinear system. Thereafter, the linear quadratic regulator (LQR) design technique with a high-gain property is applied to design an analog observer-based tracker of the optimal linear model. Finally, MIMO numerical examples are given to illustrate the effectiveness and the feasibility of the proposed method.

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