本篇論文提出一個適用於含直接傳輸項之未知線性系統並具有輸入限制功能的模型預測控制軌跡追蹤器。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直接傳輸項之資料取樣模型的適當階數(或低階)的線性觀測器。此外，採用具有預估之數位重新設計方法將具有高增益類比觀測器與追蹤器轉換得到一低增益且可執行的數位觀測與追蹤器。為了降低控制力來滿足輸入飽和限制，提出修正型模型預測控制的軌跡追蹤器，可滿足輸入飽和的限制，並且在具有輸入飽和限制時具有高增益的特性。

This thesis proposes a modified observer-based model predictive control tracker for linear unknown system with a direct transmission term and input constraint. First, the observer/Kalman filter identification(OKID) is used to identify the unknown and linear system with a transmission term into the equivalent mathematical model containing a transmission term. This identified model is used for the design of the controller and observer. Besides, the prediction-based digital redesign method is utilized to obtain a relatively low-gain and implementable observer and digital tracker from the theoretically well-designed high-gain analogue observer and tracker. The proposed modified observer-based model predictive control not only reduces the control input to fit the requirement of the input constraint, but also possesses the high-gain property of controlled system.

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