臺灣博碩士論文加值系統

本論文中，針對輸出響應追蹤問題，提出一種適用於非線性資料採樣系統的觀測型數位重新設計追蹤器。此追蹤器所需的參數是透過具有進化演算法特性之重複學習控制器與線性二次式追蹤器的混合式設計而給定。該追蹤器不僅於適當採樣時間裡，呈現優異的暫態與穩態響應。而且，較大的採樣時間中，其控制效能優於原先的數位重新設計追蹤器。首先，透過重複學習控制器與線性二次式類比追蹤器的混合式設計取得理想狀態。接著，以此狀態為操作點，根據最佳線性化方法，將每個採樣時間點的非線性系統轉換成最佳線性模型。最後，運用這些線性模型設計具有該類比控制器之優異效能的觀測型數位重新設計追蹤器。設計過程中，當系統狀態無法獲得時，針對此追蹤器設計一具有線性二次式調節器特性的觀測器。另外，為了加速重複學習控制器的收斂速度，以進化演算法搜尋最佳學習增益。再者，針對此學習控制器提出三項改善: 控制輸入訊號初始值設定、運用最佳線性化方法與線性二次式調節器技術設計觀測器、適用於二階微分系統其輸出矩陣 與輸入矩陣 之乘積為非滿秩的改良式輸入訊號更新法則。最後，利用數個多輸入多輸出非線性系統的例子展現本文所提方法之優異效果。

In this thesis, a novel observer-based digital redesign tracker, whose parameters are given by a hybrid design of EP-based iterative learning control and linear quadratic analog tracker, is proposed to resolve the output tracking problem for a class of nonlinear sampled data systems. The tracker possesses good tracking performance in transient state response as well as in steady that when an appropriate sampling time is given. Furthermore, its tracking result is better than that of an original digital redesign tracker at a large sampling time. Firstly, the ideal system state is obtained from the hybrid design of iterative learning control (ILC) and linear quadratic analog tracker (LQAT). Then, according to the optimal linearization algorithm, the nonlinear system will be converted into the linear model by the state as operating point at each sampling time. Finally, these models will be applied to design the digital redesign tracker which preserves the perfect tracking performance of the analog controller. In addition, an observer, whose design idea is derived from the method of the linear quadratic regulator (LQR), will be applied in the digital redesign tracker, when some system state could not be acquired. Furthermore, in order to improve the learning convergence of ILC, evolutionary programming will be applied to search for the optimal learning gain of ILC; three improved subjects of ILC, the initial control input decision, the observer design by optimal linearization algorithm and LQR, and the improved update learning rule for the second order derivative system, whose product matrix of C and B is not full rank, will be presented in this thesis. Several MIMO illustrative examples will be presented to demonstrate the effectiveness of the proposed methodology.

中文摘要 I
Abstract II
Acknowledgments III
List of Contents IV
List of Tables VI
List of Figures VII
Chapter
1. Introduction 1-1
2. Observer-Based Iterative learning Control via Evolutionary Programming 2-1
2.1 Introduction 2-2
2.2 Iterative learning control 2-3
2.2.1 Problem description 2-3
2.2.2 Observer-based iterative learning control 2-5
2.2.3 Convergence analysis for ILC 2-6
2.3 Improved ILC tracker via evolutionary programming 2-10
2.3.1 Quasi-random sequences (QRS) 2-11
2.3.2 The procedure introduction for evolutionary programming algorithm 2-13
2.4 Other improvement subjects of the ILC tracker 2-16
2.4.1 Initial control input decision for the ILC tracker 2-16
2.4.2 Iterative learning rule of ILC adjustment for robot manipulator 2-16
2.5 Illustrative examples 2-19
2.5.1 Example 2.1 Observer-based ILC for a nonlinear system 2-19
2.5.2 Example 2.2 Alterative learning update rule of ILC for robot manipulator 2-21
2.5.3 Example 2.3 Observer-based EP-ILC tracker for chaotic system 2-23
3. Observer-Based Digital Redesign Tracker via the Hybrid Design of EP-ILC and LQAT for Nonlinear Sample Data Systems 3-1
3.1 Introduction 3-2
3.2 The prediction-based digital redesign methodology 3-2
3.2.1 Optimal linearization 3-2
3.2.2 Linear quadratic analog tracker design 3-5
3.2.3 Observer-based linear quadratic analog tracker design 3-7
3.2.4 Digital redesign of the linear quadratic analog tracker 3-8
3.2.5 Digital redesign of the observer-based linear quadratic analog tracker 3-10
3.3 Observer-based digital redesign tracker via the hybrid design of EP-ILC and LQAT for nonlinear sampled data systems 3-13
3.4 An illustrative example 3-20
4. Conclusions 4-1
References R-1

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