| Summary: | 碩士 === 國立成功大學 === 電機工程學系碩博士班 === 97 === 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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