本論文提出一種基於修正型線性自迴歸移動平均模型和觀測/卡曼濾波器鑑別以適用於未知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。利用觀測/卡曼濾波器鑑別，可以得到修正型線性自迴歸移動平均模型之階數和優良的初始參數，並且利用此優良的初始參數可以改善鑑別的效率。用此可調整之修正型線性自迴歸移動平均系統鑑別模型，對取樣資料多變數線性奇異系統提出一個相符合的適應性數位控制方法，而且此線性奇異系統的系統參數未知和狀態不可得知。此外，將自調式控制的方法將以修改，發展出一種對未知多變數線性奇異系統的容錯控制法。當系統偵測到故障發生，藉著比較在卡曼濾波器估測演算法中的誤差值，一種量化準則被發展出來：權重矩陣重新設定技術對於故障系統之恢復而言，它是藉著調整和重新設定在卡曼濾波器估測演算法中用以估測參數的斜方差矩陣。因此，這方法可有效處理突發式和逐步式系統故障或控制器故障的情況。最後，有一個範例去證明提出之設計方法的有效性。

An low-order active fault-tolerant state-space self-tuner for unknown linear singular system using observer/Kalman filter identification (OKID) and modified autoregressive moving average with exogenous input (ARMAX) model-based system identification is proposed in this thesis. Through OKID, to determination the order and a good initial guess of the modified ARMAX model can be obtained to improve the performance of the identification process. With the modified adjustable ARMAX-based system identification, a corresponding adaptive digital control scheme is proposed for the sampled-data multivariable linear singular system which has unknown system parameter and inaccessible system state. Besides, by modifying the conventional self-tuning control, a fault tolerant control scheme is also developed for the unknown multivariable singular system. For the detection of fault occurrence, a quantitative criterion is developed by comparing the innovation process errors estimated by the Kalman filter estimation algorithm, so that a resetting technique of the weighting matrix is developed by adjusting and resetting the covariance matrices of parameter estimation obtained by the Kalman filter estimation algorithm to improve the parameter estimation for faulty system recovery. The proposed method can effectively cope with partially abrupt and/or gradual system faults and/or input failure with fault detection. An illustrative example is given to demonstrate the effectiveness of the proposed design methodology.

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