臺灣博碩士論文加值系統

本論文探討一個新型且有效的隨機整數/分數階渾沌系統的低階狀態空間自調式控制及其容錯控制之研究。本文包括以下三個方向﹕首先，針對隨機渾沌混合系統，利用觀測/卡爾曼濾波器識別法，設計一個有效的低階自調式控制器，這個系統狀態係經由一般座標形式轉換至另一觀測形式。因此，它提供了線上 “自回歸-移動平均-輸入變數模式” 識別的一個實質有效的初始化數值，並提出一個低階狀態空間自調式追蹤器的設計法則。其次，針對隨機分數階渾沌系統，基於所提出的修正型自調式控制法則，一個相對於觀測/卡爾曼濾波器識別的低階自調式控制器被提出。為了在自調式控制機制中能更有效地估測時變參數，本文採用新型的卡爾曼濾波器參數估測法，以取代慣用的識別演算法。除此之外，利用數位再設計方法的優點，基於即時輸出的觀測器也一併被提出。最後，將慣用的自調式控制法則加以修改，並發展出一套適用於方程式未知多變數隨機系統的低階自調式容錯控制架構。當被控系統有故障發生時，經由比較卡爾曼濾波器參數估測演算法的更新過程誤差，可以在自調式控制器結構中進行故障檢測。此方法可以有效地復原當系統有突發性或逐漸性部份故障，以及控制輸入有突發性或逐漸性部份故障時的容錯控制。

A study of a new effective low-order state-space self-tuning control for stochastic integer/fractional order chaotic systems and fault tolerant control is presented in this dissertation. This dissertation includes three aspects: First of all, an effective lower-order tuner for a stochastic chaotic hybrid system is designed using the observer/Kalman filter identification method, in which the system state in a general coordinate form is transformed to one in an observer form. Moreover, it provides a lower-order realization of the tracker, with computationally effective initialization, for on-line “auto-regressive moving average process with exogenous model-based” identification and a lower-order state-space self-tuning control technique. Secondly, based on the modified state-space self-tuning control, a novel low-order tuner via the observer/Kalman filter identification is proposed for stochastic fractional-order chaotic systems. Then, in stead of using the conventional identification algorithm used in self-tuning control, the Kalman filter as a parameter estimator with the state-space innovation form is presented for effectively estimating the time-varying parameters. Besides, taking the advantage of the digital redesign approach, the current-output-based observer is proposed for the modified self-tuning control. Finally, a new low-order self-tuning fault-tolerant control scheme for unknown multivariable stochastic systems by modifying the conventional self-tuning control is also developed. For the detection of fault occurrence, a quantitative criterion is developed by comparing the innovation process errors occurring in the Kalman filter estimation algorithm. The proposed method can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection.

Abstract ii
Acknowledgement iii
Contents iv
List of Figures vi
List of Table viii
Symbols and Abbreviations ix
Chapter 1 Introduction 1
1.1 Observer/Kalman filter Identification for stochastic systems 1
1.2 Self-tuning control for stochastic integer/fractional order chaotic systems 2
1.3 Self-tuning fault-tolerant control 5
1.4 Organization of the dissertation 6
Chapter 2 Observer/Kalman filter Identification for Stochastic Systems 7
2.1 OKID formulation 7
2.2 Digital redesign of observer 18
2.3 An illustrative example 23
2.4 Summary 32
Chapter 3 Lower-Order State-Space Self-Tuning Control for a Stochastic Chaotic Hybrid System 33
3.1 Introduction 33
3.2 State-space innovation models for linear/nonlinear stochastic systems 35
3.3 Prediction-based digital redesign method 39
3.4 Effective state-space self-tuner for stochastic chaotic hybrid systems 42
3.5 Illustrative examples 44
3.6 Summary 60
Chapter 4 State-Space Self-Tuning Control for Stochastic Fractional Order Chaotic Systems 61
4.1 Introduction 61
4.2 Fractional derivative and its approximation 62
4.3 Self-tuning control with fractional-order chaotic systems 63
4.4 Effective state-space self-tuner for stochastic chaotic systems 65
4.5 Illustrative examples 68
4.6 Summary 75
Chapter 5 Low-Order State-Space Self-Tuning Control for Fault Tolerance 76
5.1 Introduction 76
5.2 Problem statement 77
5.3 State-space self-tuning control with fault tolerance 79
5.4 The design procedure of self-tuning control with fault tolerance 82
5.5 An illustrative example 83
5.6 Summary 91
Chapter 6 Conclusions 92
References 94
Biography 99

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