本論文針對含有未知參數、系統干擾、量測雜訊及狀態不可得的非線性隨機混合系統，提出一種基於非線性自回歸-移動平均-輸入變數(NARMAX)模型之狀態空間自調式控制架構，以設計一個具有容錯機制的最佳軌跡追蹤器。本文的研究內容包括以下兩個主要部份：一、 針對上述非線性隨機混合系統提出一種基於有理數式非線性自回歸-移動平均-輸入變數模型之狀態空間自調式控制架構。此非線性模型需先經代數運算轉換為參數線性模型，以供遞迴式演算法辨識模型的參數。自調式控制設計過程中，基於狀態估測及控制器增益設計的需要，本論文提出建構最佳標準觀測器型式之狀態空間模型的方法，以設計最佳軌跡追蹤器。二、 針對前述非線性隨機混合系統，提出一種較為簡單的多項式非線性自回歸-移動平均-輸入變數模型之主動容錯的狀態空間自調式控制架構，以設計一個具有故障偵測及效能恢復之脈波寬度調變式的最佳軌跡追蹤器。設計過程中，為了縮短辨識模型參數的時間及提高受控系統性能的考量，透過觀測器/卡爾曼濾波器辨識演算法及最佳線性化法，導出求取遞迴式演算法之參數初始值的方法。關於系統故障的偵測，本論文給定一種判定系統是否發生故障的準則，至於恢復系統因故障而衰退的性能，則提出重新設定改良型卡爾曼濾波器演算法之協方差矩陣的方法。

In this dissertation, a state-space self-tuning control scheme based on the nonlinear autoregressive moving average with exogenous inputs (NARMAX) model has been proposed and used to design the optimal fault-tolerant trajectory tracker for nonlinear stochastic hybrid systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. The contents of this dissertation include two main parts: i) The novel state-space self-tuning control scheme based on the rational NARMAX model has been proposed for the nonlinear stochastic hybrid systems mentioned above. For parameter estimation, the nonlinear parameter model needs to be transformed into a linear-in-the-parameter model via algebra operations. In the design process of state-space self-tuning control, the procedure to constructing the optimal state-space model in canonical observer form and the optimal trajectory tracker design have been developed for state estimation and self-tuner design. ii) For the nonlinear stochastic hybrid systems mentioned previously, the efficient state-space self-tuning control mechanism involving an active fault tolerance based on the polynomial NARMAX model has been addressed and utilized to achieve an active fault-tolerant pulse-width-modulated (PWM) trajectory tracker. A formula to computing the initial parameter used in recursive estimation algorithm has been derived via the observer/Kalman filter identification (OKID) algorithm and the optimal linearization method so as to not only reduce the identification process time, but also enhance the controlled system performances. A criterion is given to determine whether the system faults occur or not. For system faults, the method to initializing estimation error covariance matrices used in the modified Kalman filter algorithm has been also developed in order to prevent the performance degeneration due to the system faults.

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