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研究生： 林佑
Lin, You
論文名稱： 適用於未知非線性隨機混合系統以NARMAX模型和OKID的主動容錯型狀態空間自調式控制
An Active Fault Tolerance using Novel NARMAX Model and OKID for State-Space Self-Tuning Control of Unknown Nonlinear Stochastic Hybrid Systems
指導教授： 蔡聖鴻
Tsai, Sheng-Hong
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2008
畢業學年度： 96
語文別： 英文
論文頁數： 70
中文關鍵詞： 容錯控制 、自調式控制 、非線性自回歸移動平均模型 、隨機系統 、觀測/卡曼濾波器鑑別
外文關鍵詞： NARMAX model, OKID, fault tolerant control, RELS, stochastic system, State-space self-tuning control, optimal linearization approach
相關次數： 點閱：140  下載：1 
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    • 論文提出一個適用於未知非線性隨機混合系統，以非線性自回歸移動平均模型為基底的主動容錯型狀態自調式控制方法。利用觀測/卡曼濾波器鑑別，可以得到一個優良的初始非線性自回歸移動平均模型，並且可以縮短鑑別系統的時間。用此可調整的非線性隨機系統模型，對連續時間非線性隨機系統提出一個相符合的適應性數位控制方法，而且此非線性隨機系統的系統參數未知、狀態不可得知、有可量測的雜訊等。此外，將自調式控制方法加以修改，發展出一種對未知多變數隨機系統的容錯控制法。當受控系統發生故障時，藉著比較在卡曼濾波器估測演算法中的誤差值，一種量化的準則被發展出來：權重矩陣重新設定技術，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協方差矩陣。因此，這方法可以改善用於系統回復的參數估測，並且有效地處理局部突發式或逐步式錯誤的系統錯誤、以及突發式或逐步式的輸入錯誤。

    An active fault tolerance using the novel nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuning is proposed in this thesis for unknown nonlinear stochastic hybrid systems. Through observer/Kalman filter identification method, ones has a good initial one of NARMAX model to reduce the time of the identifying process. With the adjustable NARMAX-based noise model, a corresponding adaptive digital control scheme is proposed for continuous-time multivariable nonlinear stochastic systems which have unknown system parameters, measurement noises, and inaccessible system states. Besides, by modifying the conventional self-tuning control, a fault tolerant control scheme is also developed for unknown multivariable stochastic systems. 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 weighting matrix resetting technique is developed by adjusting and resetting the covariance matrices of parameter estimate 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 failures with fault detection.

    中文摘要 I Abstract II Acknowledgements III List of Contents IV List of Figures VI Chapter 1. Introduction 1-1 2. NARMAX Model for Self-Tuning Control Scheme 2-1 2.1 The structure of the state-space STC 2-2 2.2 Digital controller design 2-2 2.3 NARMAX model for self-tuning control scheme of MIMO case 2-3 3. NARMAX Model-Based State-Space Observer for Self-Tuning Control 3-1 3.1 Preliminary structure of discrete-time state-space observer 3-2 3.2 OKID formulation 3-6 3.2.1 Basic observer equation 3-6 3.2.2 Computation of Markov parameters 3-9 3.2.2.1 System Markov parameters 3-10 3.2.2.2 Observer-gain Markov parameters 3-11 3.2.3 Relationship to a Kalman filter 3-13 3.2.4 OKID algorithm 3-16 3.3 The method of optimal linearization 3-17 3.4 STC scheme of unknown nonlinear stochastic hybrid systems based on a NARMAX model with initial OKID-estimated parameters 3-21 3.4.1 The combination of discrete-time state-space observer and STC scheme with NARMAX model through optimal linear model 3-21 3.4.2 The initial parameters of NARMAX model 3-26 4. NARMAX Model-Based State-Space Self-Tuner with OKID for Unknown Nonlinear Stochastic Hybrid Systems 4-1 4.1 The design procedure of state-space self-tuner with NARMAX model 4-2 5. Self-Tuning Control with Fault Tolerance 5-1 5.1 Problem statement 5-2 5.2 Modified active fault tolerance 5-3 6. Illustrative Examples 6-1 6.1 System identification by using RELS method (two-input-two-output) 6-2 6.1.1 Nonlinear NARMAX model system 6-2 6.1.2 Input time delay system 6-5 6.1.3 Singular system 6-7 6.2 Active fault tolerance using NARMAX model-based state-space self-tuning control with PWM 6-11 6.2.1 Design of PWM controller for the time-delay system using digital PAM 6-11 6.2.2 Simulation 6-12 7. Conclusions 7-1 Reference R-1

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