本論文提出一種適用於具有內部狀態延遲連結之廣義未知大尺度資料取樣線性系統，且其具有閉迴路解耦特性的分散式模型化線性觀測器與輸入飽和軌跡追蹤器。首先，利用離線的觀測器/卡爾曼濾波器鑑別方法計算出具有內部狀態延遲連結之廣義未知大尺度資料取樣線性系統的適當階數(或低階)的分散式線性觀測器。然後此觀測器可以利用具有高增益性質的數位再設計方法，進一步改善模組的誤差。此外針對資料取樣系統，提出一個具有高增益的數位再設計觀測型線性二次式數位軌跡追蹤器，且提供良好的軌跡追蹤效果，而系統具有閉迴路解耦特性。最後，為了降低控制力來滿足輸入飽和限制的需要，提出修正型的線性二次式數位軌跡追蹤器。藉此控制力可以有效的被壓縮，而且不會損失原本良好的軌跡追蹤效果。

Decentralized modeling and linear observers/input constraint trackers for a more general class of unknown large-scale interconnected sampled-data linear systems with state delay and closed-loop decoupling property are proposed in this thesis. First, the off-line observer/Kalman filter identification (OKID) method is used to determine the appropriate (low-) order decentralized linear observers for the unknown large-scale interconnected sampled-data linear system with state delay. Then, a digital redesign approach with the high-gain property is applied to overcome the modeling error of the above observer effectively. Moreover, a digital-redesign observer-based linear quadratic digital tracker with high-gain property for the sampled-data system is presented, and it provides high performance on trajectory tracking with the closed-loop decoupling property. Finally, to reduce the magnitude of control input, which is caused by the high gain property to fit the requirement of the input constraint, the modified linear quadratic digital tracker (LQDT) is proposed. And the control input can be compressed effectively without losing the original high performance of tracking much.

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