在某些情況下，一包含多重子系統之未知系統會被動地由一個子系統切換到另一子系統。在本論文中，觀測/卡爾曼濾波器鑑別法被採用來鑑別每個未知子系統，並提出一具有主動切換機制的軌跡追蹤器。為了解決未知系統的輸入飽和限制的問題，提出了一個結合數位重新設計的模型預測控制，此修改型模型預測控制可以有系統地調整權重，使控制力處於我們所希望的範圍內，以解決輸入飽和限制的問題，並且不會失去良好的追蹤效率。此修改型模型預測控制也可預測未來的輸出並藉此即時識別系統模型切換的時機，並透過主動切換機制，轉換到相對應的控制器以維持良好的追蹤效果。

In some cases, an unknown system consisting of multi-subsystems would switch from one subsystem to another passively. In this thesis, the off-line observer/Kalman filter identification (OKID) method is adopted to identify each unknown subsystem, and an active switching mechanism is presented for a passively switched multi-input multi-output (MIMO) subsystems. To resolve the input constraint problem for the unknown system, the modified observer-based model predictive control (MPC) combining with prediction-based digital redesign is proposed, without losing the good tracking performance as possible. The proposed modified model predictive control scheme can predict the future output and identify the switching instant immediately, and it systematically compresses a huge control input within the desired range by adjusting the weighting matrix of the cost function for the linear time-invariant (LTI) input-constrained system.

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