本論文提出兩種適用於未知資料取樣非線性奇異系統的控制法則。一種是基於觀測/卡爾曼濾波器鑑別之具有狀態負迴授增益和正迴授增益的觀測型數位再設計追蹤器，以提出之控制法則能有效控制未知資料取樣非線性奇異系統。另一種控制法則是基於修正型線性自迴歸移動平均模型和觀測/卡爾曼濾波器鑑別以適用於未知非線性奇異系統之低階主動容錯型狀態空間自調式軌跡追蹤器。首先，利用觀測/卡爾曼濾波器鑑別去決定未知系統之階數與修正型線性自迴歸移動平均模型之優良的初始參數，以改善鑑別的效率，然後，基於修正型線性自迴歸移動平均模型之系統鑑別，一個相對應的適應性數位控制法則被提出以適用於狀態不可測之未知資料取樣非線性奇異系統。此外，對於未知資料取樣非線性奇異系統，為了克服輸入干擾，藉由修正型的狀態空間自調式控制以提出容錯控制法則。此控制法則可以有效處理系統輸入突發式和逐步式之故障。最後，利用一些範例證明提出之設計方法的有效性。

In this thesis, we present two control schemes for the unknown sampled-data nonlinear singular system. One is an observer-based digital redesign tracker with the state-feedback gain and the feed-forward gain based on off-line observer/Kalman filter identification (OKID) method. The presented control scheme is able to make the unknown sampled-data nonlinear singular system to well track the desired reference signal. The other is an active fault tolerance state-space self-tuner using the OKID method and modified autoregressive moving average with exogenous inputs (ARMAX) model-based system identification for unknown sampled-data nonlinear singular system with input faults. First, one can apply the off-line OKID method to determine the appropriate (low-) order of the unknown system order and good initial parameters of the modified ARMAX model to improve the convergent speed of recursive extended-least-squares (RELS) method. Then, based on modified ARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown sampled-data nonlinear singular system with immeasurable system state. Moreover, in order to overcome the interference of input fault, one can use a fault-tolerant control scheme for unknown sampled-data nonlinear singular system by modifying the conventional state space self-tuning control (STC). The presented method can effectively cope with partially abrupt and/or gradual system input faults. Finally, some illustrative examples are given to demonstrate the effectiveness of the presented design methodologies.

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