本論文提出一種適用於具有內部狀態延遲連結之未知資料取樣大尺度之N階多輸入多輸出系統，且其具有閉迴路解藕特性的新式分散式重複學習追蹤器。首先，利用離線的觀測器/卡爾曼濾波器鑑別方法，取得具有內部連結之分散式大尺度系統模型。為了去除每一個子系統鑑別線性模型時模型誤差所造成的影響，使用一種基於數位再設計方法改進的高增益效應觀測器，取代傳統的觀測器/卡爾曼濾波器鑑別方法之觀測器。然後，將重複學習演算控制器嵌入在分散式的模型中。值得注意的是重複學習演算控制器的收斂速率直接被初始控制輸入所影響。為了加速重複學習演算控制器的收斂速率，一個具有高增益特性的分散式數位再設計軌跡追蹤器設計方法被用來作為重複學習演算控制器的初始輸入。高增益效應可以抑制不穩定之誤差例如模型誤差、非線性擾動，與外部干擾。因此，此具有閉迴路解藕特性之系統輸出可以在短時間內快速且準確的追蹤到目標之參照軌跡。

The decentralized iterative learning trackers for the unknown sampled-data interconnected large-scale state-delay system consisting of multi-input multi-output subsystems with the closed-loop decoupling property is proposed in this thesis. The off-line observer/Kalman filter identification (OKID) method is used to obtain the decentralized linear models for subsystems in the interconnected large-scale system. In order to get over the effect of modeling error on the identified linear model of each subsystem, an improved observer with the high-gain property based on the digital redesign approach is developed to replace the observer identified by OKID. Then, iterative learning control (ILC) scheme is embedded to the decentralized models. Notice that the convergence of ILC is directly influenced by the initial control input. To accelerate the convergence of ILC, the digital-redesign linear quadratic tracker with the high-gain property is proposed as the initial control input of ILC. The high-gain property controllers can suppress the uncertain errors such as modeling errors, nonlinear perturbations, and external disturbances. Thus, the system output can quickly and accurately track the desired reference in a short time interval with the closed-loop decoupling property.

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