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研究生:王志浩
研究生(外文):Jyh-HawWang
論文名稱:適用於未知資料取樣非線性且具有輸入─輸出傳輸項的正規系統之低階主動容錯狀態空間自調器
論文名稱(外文):A Low-Order Active Fault-Tolerant State Space Self-Tuner for the Unknown Sampled-Data Nonlinear Regular System with an Input-Output Feedthrough Term
指導教授:蔡聖鴻
指導教授(外文):Jason Sheng-Hong Tsai
學位類別:博士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:133
中文關鍵詞:狀態空間自調式控制資料取樣非線性奇異系統ARMAX模型容錯控制觀測器/卡爾曼濾波器鑑別系統鑑別
外文關鍵詞:State space self-tuner controlsampled-data nonlinear singular systemARMAX modelfault tolerant controlobserver/Kalman filter identificationsystem identification
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針對未知資料取樣的非線性且具有輸入─輸出傳輸項的正規系統,本論文藉由數位重新設計技術提出兩個創新的控制方案:其一是以觀測器為基礎的數位重新設計追蹤器,藉由觀測器/卡爾曼濾波器鑑別為方法的狀態反饋和前饋增益所設計的高效能追蹤器,以達到良好的追蹤特性。其二是以當有輸入錯誤的主動容錯狀態空間自調式控制器,基於觀測器/卡爾曼濾波器與修飾的ARMAX模型為系統鑑別方法基礎的數位重新設計追蹤器。研究主題陳述如下:首先,針對輸出響應追蹤問題,提出應用離線的觀測器/卡爾曼濾波器鑑別方法,可以決定未知資料取樣系統的合宜(-低階)維度,以及提供較佳的初始參數為修飾的ARMAX模型系統,可顯著改善其遞歸擴展最小平方(RELS)法的收斂速度。接著,基於修飾的ARMAX模型為系統鑑別方法的基礎,針對未知資料取樣的非線性奇異系統之不可量測系統的狀態問題,提出一個相對應的適應性數位控制方案。更進一步的,為了克服介面的輸入故障的問題,容錯控制和修飾的傳統自調式控制器,也被提出。所提的方法能有效的處理部分突發式與漸進式的輸入故障問題。在本論文中,舉出一些例子,也包含一個實際的電路系統,來說明所提方法之有效性。
A singular system can be reformulated into the equivalent regular system model with an input-output direct feedthrough term from the input to the output. This dissertation proposes two new control schemes for the unknown sampled-data nonlinear singular system. One is an observer-based digital redesign tracker with the state-feedback gain and the feed-forward gain based on off-line observer/Kalman filter identification (OKID) method. The presented control scheme is able to make the unknown sampled-data nonlinear singular system to well track the desired reference signal. The other is an active fault tolerance state-space self-tuner using the OKID method and modified autoregressive moving average with exogenous inputs (ARMAX) model-based system identification for unknown sampled-data nonlinear singular system with input faults. First, one can apply the off-line OKID method to determine the appropriate (low-) order of the unknown system order and good initial parameters of the modified ARMAX model to improve the convergent speed of recursive extended-least-squares (RELS) method. Then, based on modified ARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown sampled-data nonlinear singular system with the unmeasurable system state. Moreover, in order to overcome the interference of input fault, one can use a fault-tolerant control scheme for the unknown sampled-data nonlinear singular system by modifying the conventional self-tuner control (STC). The presented method can effectively cope with partially abrupt and/or gradual system input faults. Finally, some illustrative examples, including a real circuit system, are given to demonstrate the effectiveness of the presented design methodologies.
Contents
中文摘要 i
Abstract iii
Acknowledgement v
Contents vi
List of Figures ix
Symbols and Abbreviations xiii
1 Introduction 1
1.1 Literature Survey 2
1.1.1 Observer/Kalman filter identification 2
1.1.2 Autoregressive moving average with exogenous inputs (ARMAX) model-based system identification 3
1.1.3 Active fault tolerance 4
1.1.4 Input-constrain digital redesign 5
1.2 Dissertation Overview 7

2 An Optimal Digital Redesign for MIMO Singular Systems under Input Constraints 10
2.1 Introduction 11
2.2 Digital Redesign Optimal Control Scheme for Sampled-Data Systems with Input Constraint 13
2.2.1 Introduction of matrix sign function 13
2.2.2 Decomposition of the singular systems 15
2.2.3 Optimal control scheme with input constraint 22
2.2.4 Digital redesign of linear quadratic analog tracker 31
2.2.5 Illustrative examples 39
2.3 Summary 45

3 A Low-Order Active Fault-Tolerant State Space Self-Tuner for the Unknown Sampled-Data Nonlinear Singular System Using OKID and Modified ARMAX Model-Based System Identification 46
3.1 Introduction 48
3.2 Observer/Kalman Filter Identification 50
3.2.1 Basic observer form 50
3.2.2 Computation of Markov parameters 53
3.2.2-1 System Markov parameters 53
3.2.2-2 Observer-gain Markov parameters 54
3.2.3 Eigensystem realization algorithm 55
3.2.4 OKID algorithm 57
3.3 Quadratic Suboptimal Tracker and Observer for the Unknown Sampled-Data Nonlinear Singular System 58
3.3.1 Observer-based tracker for the unknown sampled-data nonlinear singular system 59
3.3.2 Prediction-based linear quadratic digital tracker 61
3.3.3 Prediction-based digital observer 62
3.3.4 Design procedure 64
3.4 An Alternative Digital Tracker for the Unknown Sampled-Data Nonlinear Singular System 65
3.5 Modified ARMAX Model for Self-Tuning Control Scheme 67
3.6 State-Space Innovation Form for Modified ARMAX Model 71
3.7 The Initial Parameters of ARMAX Model Based on OKID 73
3.8 Modified ARMAX Model-Based State-Space Self-Tuner for the Unknown Sampled-Data Nonlinear Singular System with Off-Line OKID Estimated Initial Parameters 74
3.9 Self-Tuning Control with Fault Tolerance 78
3.9.1 Problem statement 78
3.9.2 Modified active fault tolerance 80
3.10 Illustrative Examples 85
3.11 Summary 113

4 Conclusions 114
4.1 Conclusions 114
4.2 Future Research Directions 116

Appendix A 117
References 121
Biography 132
Publication List 133

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