臺灣博碩士論文加值系統

本篇論文提出一個適用於含直接傳輸項之未知系統並具有輸入／輸出／狀態飽和限制功能的新型二次數位軌跡追蹤器，並利用OKID型調整權重機制找出相對應的權重值。首先，首先利用離線的觀測器／卡爾曼濾波器鑑別方法計算出資料取樣系統之適當階數(或低階)的線性觀測器。為了克服模組誤差，應用一個具有高增益特性的數位再設計之線性觀測器，最後藉由鑑別出的觀測器來做為設計控制器的參數。為了解決真實系統無法承受之控制力、或是外來雜訊導致輸出超出預期範圍，修改線性二次效能指標使之具有輸入／輸出等飽和限制的概念，並利用此修改限性二次效能指標推導出一個適用於含直接傳輸項之未知系統並具有輸入／輸出／狀態飽和限制功能的新型二次數位軌跡追蹤器。最後，為了精確的限制輸入／輸出／狀態，利用OKID型調整機制來找出能有效限制輸入／輸出／狀態的權重值。

This thesis proposes an OKID-based adaptive mechanism for tuning weighting matrices of muti-objective cost function: An application to the digital redesign tracker for the unknown system with a direct feed-through term and input/state/output constraints. First, the observer/Kalman filter identification (OKID) is used to identify the unknown and nonlinear system with a feed-through term into the equivalent mathematical model containing a feed-through term; this identified model is used for the design of the controller and observer. By adding constraint terms into the linear quadratic performance index, a new linear quadratic digital tracker is derived to track the desired reference and posses the effectiveness of constraints. Further, an OKID-based adaptive mechanism for tuning the weighting matrices is constructed in the cost function to update input, state and output which exceed the limit bounds of saturation and make them update quickly and accurately. Next, the observer-based digital tracker is proposed for the system if the state is immeasurable. Illustrative examples realize the effectiveness of the proposed design.

中文摘要 i
Abstract ii
Acknowledgement iii
List of Contents iv
List of Tables vi
List of Figures vii
Chapter 1 Introduction 1
Chapter 2 Problem Description 3
Chapter 3 Observer/Kalman Filter Identification 5
3.1 Basic Observer Equation 5
3.2 Computation of Markov parameters 7
3.2.1 System Markov parameters 7
3.2.2 Observer gain Markov parameters 8
3.3 Eigensystem Realization Algorithm 8
3.4 Relationship to a Kalman Filter 10
Chapter 4 Observer-based Linear Quadratic Tracker for the Sample-Data System with Feed-Through Term with Input, State and Output Constraints 12
4.1 A New Optimal Linear Quadratic Analogue Tracker for the System 12
4.2 Prediction-based Linear Quadratic Digital Tracker for the Sampled-data System with a Direct Feed-Through Term with Input, State and Output Constraints 17
4.3 The Observer-based Digital Tracker for the System with a Direct Feed-Through Term with Input and Output Constraints 19
4.4 A New Algorithm of Calculation for Tuning the Weighting Matrices: OKID-based Tuning Mechanism 21
4.4.1 Off-line calculate virtual systems and virtual controllers 25
4.4.2 On-line calculate based on the OKID-based tuning mechanism 27
4.4.3 The design procedure of the system for the input/state/output constraints 31
Chapter 5 Illustrated Examples 37
Example 5.1 38
Example 5.2 39
Example 5.3 58
Example 5.4 78
Chapter 6 Conclusion 91
References 92

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