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研究生：	魏兆隆 Wei, Chao-Lung
論文名稱：	適用於資料取樣系統之具有容錯的觀測器與軌跡追蹤器：數位再設計方法 Fault-Tolerant Observer and Trajectory Tracker for Sampled-Data Systems: Digital Redesign Approach
指導教授：	蔡聖鴻 Tsai, Sheng-Hong
學位類別：	博士 Doctor
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：	2010
畢業學年度：	98
語文別：	英文
論文頁數：	144
中文關鍵詞：	容錯機制、數位再設計適應性觀測器、資料取樣的非線性時變系統、進化規劃演算法、卡爾曼濾波器
外文關鍵詞：	Fault-tolerant, digitally redesigned adaptive observer, sampled-data nonlinear time-varying systems, evolutionary programming, Kalman filter
相關次數：	點閱：118 下載：1
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針對具有致動器和狀態錯誤的資料取樣系統，本論文提出創新的數位再設計適應性觀測器、具有容錯機制的觀測器、軌跡追蹤器，以達到有效的錯誤偵測和效能恢復。本論文的研究成果可以分為以下：針對致動器的錯誤偵測和效能恢復，結合輸入補償的架構，提出以進化規劃演算法為基礎的數位再設計適應性觀測器，以改善錯誤估測和目標追蹤的效能；針對致動器與狀態的錯誤偵測，利用即時量測的輸出以估測目前的狀態，提出預測型的數位再設計適應性觀測器，達成高效能的錯誤偵測目標追蹤；在診斷錯誤的過程中，提出留數產生的架構和自動調整切換增益的機制，以匹配致動器或狀態的事件；針對資料取樣的非線性時變系統，利用最佳線性化技巧，提出另一種以預測型卡爾曼濾波器為基礎的適應性估測器，依自動偵測錯誤的類別與代表錯誤量的衰減因子，估測出錯誤量並補償錯誤的系統。在本論文中，同時以說明性的例題驗證所提方法的有效性。

Digitally redesigned adaptive observer, fault-tolerant observer, and trajectory tracker for some classes of sampled-data systems with actuator and state faults have been proposed in this dissertation to obtain an efficient fault detection and performance recovery. Here, it can be stated in following aspects: the evolutionary programming-based digitally redesigned adaptive observer, fault estimator, and trajectory tracker for the actuator fault detection and performance recovery via the input-compensation scheme have been established; the prediction-based digitally redesigned adaptive observer, which uses the currently measured output to estimate the current state for sampled-data linear time-varying system, is proposed to achieve a high-performance recovery for the faulted system with actuator and state faults. For achieving this goal, a residual generation scheme and a mechanism for auto-tuning switched gain to fit either the actuator or state fault scenario are proposed; the predictive Kalman filter-based adaptive observer and trajectory tracker for sampled-data nonlinear time-varying system have been proposed to automatically detect the types of faults (i.e., state fault or actuator fault), the amount of unanticipated fault in terms of decay factors, and compensate for it. The utilization of optimal linearization method accomplishes the proposed methodology for actuator fault detection and performance recovery for sampled-data nonlinear time-varying systems. The effectiveness of the digitally redesigned adaptive observer and trajectory tracker for performance recovery of the faulted systems is also demonstrated by illustrative examples.

中文摘要    i
Abstract    ii
Acknowledgement    iii
Contents    iv
List of Tables    vii
List of Figures    viii
Symbols and Abbreviations    xi
Chapter 1	Introduction    1
1	Literature survey and motivation    2
1.1 Fault detection and diagnosis    2
1.2 Fault tolerant control    4
1.3 Adaptive observer    6
1.4 Digital redesign and evolutionary programming    6
1.5 Motivation of the dissertation    8
2	Organization of the dissertation    10
Chapter 2	Digitally Redesigned Observer-Based Predictive Tracker    11
1	Introduction    12
2	Analog linear quadratic tracker and observer designs    13
3	Derivation of the observer-based tracker for the sampled-data linear system    16
4	Summary    26
Chapter 3	EP-Based Digitally Redesigned Adaptive Observer, Fault Estimator, and Trajectory Tracker for Sampled-Data Nonlinear Time-Varying Systems with Actuator Fault    27
1	Introduction    28
2	Digitally redesigned adaptive observer    29
2.1 Adaptive observer for linear time-varying systems    29
2.2 Derivation of the novel digitally redesigned adaptive observer (DRAO)    33
3	EP-based observer and trajectory tracker for nonlinear time-varying systems with actuator fault    37
3.1 Methodology of actuator fault detection and performance recovery with input compensation    37
3.2 Actuator fault detection and performance recovery for nonlinear time-varying systems    39
3.2-1 Optimal linearization for nonlinear systems    39
3.2-2 Actuator fault detection and performance recovery for nonlinear time-varying systems with optimal linearization    42
3.3 Adaptive observer/tracker: an evolutionary programming approach    45
3.3-1 Quasi-random sequences (QRS)    46
3.3-2 Tuning gain of the digitally redesigned adaptive observer    48
4	Illustrative Examples    51
5	Summary    63
Chapter 4	Prediction-Based Digitally Redesigned Adaptive Observer, Fault Estimator, and Trajectory Tracker for Sampled-Data Linear Time-Varying Systems with Actuator and State Faults    64
1	Introduction    65
2	An improved prediction-based digitally redesigned adaptive observer    67
2.1 Adaptive observer    67
2.2 Digitally redesigned adaptive observer (DRAO)    68
2.3 An improved prediction-based digitally redesigned adaptive observer    70
3	A universal auto-tuning switch mechanism    75
3.1 Formulation of actuator and state faults and performance recovery with input and output compensation    77
3.2 Residual-generation-based auto-tuning switch mechanism for detection and diagnosis of actuator and state faults by using PBDRAO    79
4	Illustrative examples    84
5	Summary    95
Chapter 5	Predictive Kalman Filter-based Adaptive Observer, Fault Estimator, and Trajectory Tracker for Sampled-Data Nonlinear Time-Varying Systems with Actuator and State Faults    96
1	Introduction    97
2	An improved Kalman filter-based adaptive observer    98
2.1 Adaptive observer    99
2.2 Kalman filter-based adaptive observer (KFBAO)    100
2.3 An improved Kalman filter-based adaptive observer    102
3	Auto-tuning switched mechanism for sampled-data nonlinear time-varying system against actuator and state faults    108
3.1 Actuator and state faults detection and performance recovery for nonlinear (slowly) time-varying systems with optimal linearization    110
3.2 Residual-generation-based auto-tuning switched mechanism for detection and diagnosis of actuator and state faults by using PKFBAO    114
4	Illustrative examples    119
5	Summary    131
Chapter 6	Conclusions    132
1	Conclusions    132
2	Further works    134
References    135
Biography    142
Publication List    143

                                    

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校內：2012-07-23公開
校外：2013-07-23公開

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