基於二維線性系統理論之反覆學習控制器設計及其應用

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研究生：	黃加恩 Chia-En Huang
論文名稱：	基於二維線性系統理論之反覆學習控制器設計及其應用 Toward an Iterative Learning Control Based on Two-Dimensional Linear System Theory and Its Application
指導教授：	陳建祥 Chen, Jian-Shiang
口試委員:	吳尚德 Wu, Shang-Teh 呂有勝 Lu, Yu-Sheng 葉廷仁 Yeh, Ting-Jen 陳榮順 Chen, Rong-Shun
學位類別：	博士 Doctor
系所名稱：	工學院 - 動力機械工程學系 Department of Power Mechanical Engineering
論文出版年：	2012
畢業學年度：	100
語文別：	英文
論文頁數：	96
中文關鍵詞：	二維系統、基於濾波器之反覆學習控制、羅素模型
外文關鍵詞：	Two-Dimensional System, Filter-Based Iterative Learning Control, Roessor’s model
相關次數：	點閱：2 下載：0
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基於二維(Two-Dimensional, 2-D)線性系統理論，本論文提出了一個基於濾波器之反覆學習控制(Filter-Based Iterative Learning Control, FILC)之控制器及其設計方法，而FILC是由一個反饋控制器和一個前饋濾波器所組成的，本論文將FILC控制系統轉換成2-D線性系統中所謂的“Roessor模型”，由於2-D可分離系統只要滿足一個條件即可證明其穩定，因此為了利用此2-D可分離系統穩定性的條件，本論文技巧性地假設一個新的狀態使整體 FILC控制系統可簡化成可分離系統的2-D系統，如此一來，整個FILC控制系統的收斂性可利用2-D分離系統之穩定性來證明，而且只要滿足一個條件。此外，為了驗證本論文所提出之FILC控制器設計準則，我們建立一個氣壓致動的主動式下肢輔具(Pneumatic Power Active Lower-Limb Orthosis, PPALO)，其動態方程式也被建立用來驗證所提出之控制器性能。另外，本論文也採用小波轉換濾波器(Wavelet Transform Filter, WTF)為前饋濾波器來將誤差訊號之可學習的部分萃取出來更新控制訊號，而不可學習的部份則由PD反饋控制器來加以壓制，最後，以PPALO為平台來進行一些軌跡追隨控制的模擬和實驗來驗證本論文所提出的設計方法。

Based on two-dimensional (2-D) linear system theory, this thesis proposes a design method for a filter-based iterative learning control (FILC) scheme. The FILC scheme consists of a feedback controller and a feedforward filter. The 2-D model for the FILC is established in the form of the so-called “Roessor’s model”. Moreover, stability for a 2-D separable model can be simplified with one criterion to be met. In order to utilize the criterion derived from the 2-D separable system, the overall FILC control system is constructed in the form of a 2-D separable system by assuming a new state for the 2-D model. Therefore, convergence of the overall control system can be proved by stability of 2-D separable model and a condition for convergence of the overall control system can be reached. Moreover, to validate the design criterion of the FILC scheme in this thesis, we develop a pneumatic power active lower-limb orthosis (PPALO), and its dynamic model is also established to verify the performance of the proposed controller. Additionally, the wavelet transform filter (WTF) is adopted as the feedforward filter to extract the learnable part from the error signal which can be used to update the control profile. Thus, the effects from unlearnable dynamics on the controlled system can be attenuated by a PD feedback controller. Finally, using PPALO as the controlled plant, we conduct some trajectory tracking control simulations and experiments to validate the proposed scheme.

TABLE OF CONTENTS
ABSTRACT    i
TABLE OF CONTENTS    iii
LIST OF FIGURES    v
LIST OF TABLES    viii
Chapter 1 Introduction    1
1.1 Motivations    1
1.2 Literature Survey    1
1.3 Objectives    6
1.4 Organization    7
Chapter 2 The Proposed Controller Design    8
2.1 A Filter-Based Iterative Learning Control Scheme    8
2.2 Stability Analysis    10
2.3 Simulation Verification    17
2.4 Summary    24
Chapter 3 Dynamic Modeling of Pneumatic Power Active Lower-limb Orthosis    25
3.1 Dynamic modeling of the lower-limb orthosis    25
3.2 Actuator torque    29
3.3 The dynamics of a pneumatic actuator    31
3.4 Inverse Kinematics    35
3.5 Summary    36
Chapter 4 Application of the Proposed Method to a PPALO    37
4.1 The Dynamics of PPALO to Independent Joint Space    37
4.2 FILC system of the PPALO    38
4.3 The Stability Analysis for FILC system of the PPALO in 2-D System    40
4.4 Performance Evaluation    45
4.5 Summary    56
Chapter 5 Experimental Studies    57
5.1 An Introduction to the Hardware System of the PPALO    57
5.2 Experimental Setup    60
5.3 The outer-loop controllers design    63
5.4 Inner-loop feedback controller design    69
5.5 Outer-loop controller design    72
5.6 Experimental Results of Position-Tracking    78
5.7 Summary    82
Chapter 6 Summary and Recommendations    83
6.1 Summary    83
6.2 Future Works    84
Appendix A. Re-visit of Wavelet Transform    85
Appendix B. The elements of the dynamic equation, equation (3.1).    87
Appendix C. The coefficients of the transform function (5.8).    89
References    90

                                

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