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研究生：	鄭慶朗 Cheng, Ching-Lang
論文名稱：	適用在非線性奇異資料取樣系統之以進化演算法為基底的開迴路反覆學習控制其閉迴路追蹤器 A Novel Closed-loop Type Tracker through the EP-based Open-loop Type Iterative Learning Control for a Class of Nonlinear Singular Sampled-data Systems
指導教授：	蔡聖鴻 Tsai, Sheng-Hong
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2006
畢業學年度：	94
語文別：	英文
論文頁數：	71
中文關鍵詞：	非線性奇異系統、反覆學習控制
外文關鍵詞：	iterative learning control, nonlinear singular system
相關次數：	點閱：122 下載：2
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本文提出一個適用在非線性奇異資料取樣系統之以進化演算法為基底的開迴路反覆學習控制其閉迴路追蹤器，此閉迴路控制法則可以使暫態和穩態相位都具有良好的追蹤效能。首先，我們使用延伸型矩陣符號函數得到一個區塊模態矩陣來分解非線性奇異系統成為一個降階規則的慢子系統和一個非動態的快子系統。接著，我們可以利用在不同座標的轉換把此奇異性的問題視為一個標準規則性的問題。然後，我們可以使用反覆學習控制在此慢子系統來達到追蹤目的。此反覆學習控制的學習法則在於可以由前次的輸入和輸出資訊在固定的時間反覆地調整控制輸入直到在暫態響應和穩態響應都可以完成理想的追蹤。為了增進反覆學習控制器的效能，我們使用了進化演算法來調整學習增益以獲得較快的收斂速率。雖然此以進化演算法為基底的反覆學習控制器已經具有很好的追蹤效能，但仍然是一個適用在連續時間的開迴路控制器。基於先前設計之以進化演算法為基底的反覆學習控制器仍是開迴路系統，在本文將提出一個數位在設計的閉迴路追蹤器。此外，我們再提出一個數位的狀態觀測器來避免系統的狀態不能被取得時的情況。最後，我們需轉換後來在慢子系統的座標成原來在非線性奇異系統的座標。

A novel closed-loop type tracker through the
EP-based open-loop type iterative learning control (ILC) for a class of nonlinear singular sampled-data systems is proposed in this thesis so that the proposed closed-loop type control law yields a good tracking performance in both the transient and steady-state phases. First, this thesis uses the extended matrix sign function to find the block modal matrix for decomposing the nonlinear singular system into a reduced-order regular slow subsystem and a nondynamic fast subsystem. After that, regard the singular problem as the standard regular problem which is different from the singular one in different coordinates. Then, one can apply the ILC to the regular slow subsystem for tracking purpose. The learning rule of the ILC is that it can adjust the control input repeatedly, by using the input and output information of the previous iteration over a fixed time interval, until the perfect tracking in both the transient and steady-state responses is achieved. In order to improve the performance of the ILC tracker, this thesis uses the evolutionary programming (EP) to tune the learning gain for fast converging rate. Although the EP-based ILC (EP-ILC) tracker already has a good performance for tracking purpose, it is still an open-loop type controller for the continuous time systems. Based on the
pre-designed EP-ILC based open-loop type tracker, a digital redesign closed-loop tracker is then proposed in this thesis. Besides, this thesis proposes a digital state observer to avoid the situation that the system states are not available. Finally, we transform the tracker in the alternative coordinate of the slow subsystem into the one for the original coordinate of the nonlinear singular system.

中文摘要.........................................I
Abstract.........................................III
Acknowledgments...........................V
List of Contents.............................VI
List of Tables.............................VIII
List of Figures..............................IX

Chapter 1.     Introduction................................1-1
Chapter 2.     
EP-based Open-loop Type Iterative Learning Control for a Class of Nonlinear Singular Systems..............................2-1
2.1  Introduction...........................2-2
2.2  Problem formulation and preliminaries 
         for decomposition of the nonlinear 
     singular systems.......................2-3
2.2.1 Introduction to matrix sign function..2-3
2.2.2  Introduction to the regular pencil 
       and the standard one.................2-4
2.3    Decomposition of the nonlinear 
       singular systems.....................2-5
2.4    Iterative learning control for 
       nonlinear systems....................2-8
2.4.1  Problem formulation and ILC 
       updating law.........................2-8
2.4.2  ILC convergence analysis............2-10
2.5    Improved the ILC tracker: 
       An evolutionary programming approach..2-13
2.5.1  Quasi-random sequences (QRS)..........2-15
2.5.2  Tuning learning gain of 
       the ILC tracker.......................2-16
2.6    An illustrative example...............2-19
Chapter 3. 
A Novel Closed-loop Type Tracker through the EP-based Open-loop Type Iterative Learning Control for a Class of Nonlinear Singular Sampled-data Systems.................3-1
3.1Introduction...............................3-2
3.2 The prediction based digital 
    redesign method...........................3-2
3.2.1  Linear quadratic analog
       trackerdesign..........................3-3
3.2.2  Observer-based linear quadratic analog
        tracker design........................3-4
3.2.3  Digital redesign of linear quadratic
       analog tracker.........................3-5
3.2.4  Digital redesign of observer-based linear
       quadratic analog tracker..............3-8
3.2.5  Optimal linearization................3-10
3.3    A Novel Closed-loop Type Tracker through
       the EP-based Open-loop type Iterative
        Learning Control for a Class of Nonlinear
        Singular Sampled-data Systems........3-14
3.4    An illustrative example................3-20
Chapter 4.     Conclusion....................................4-1
Reference


                                    

[1] J. D. Roberts, “Linear model reduction and solution of the algebraic
Riccati equation by use of the sign function,” International Journal
Control, vol. 32, pp. 677-687, 1980.
[2] J. S. H. Tsai, C. T. Wang, and L. S. Shieh, “Model conversion and
digital redesign of singular systems,” Journal of Franklin Institute,
vol. 330, pp.1063-1086, 1993.
[3] L. Dai, “Impulsive modes and causality in singular system,”
International Journal Control, vol. 50, no. 4, pp. 1267-1281, 1989.
[4] S. Arimoto, S. Kawamura, and F. Miyazaki, “Bettering operation of
robots by learning,” Journal of Robotic Systems, vol. 1, pp. 123-140,
1984.
[5] H. S. Ahn, C.H. Choi, and K. B. Kim, “Iterative learning control for a
class of nonlinear systems,” Automatica, vol. 29, no. 6, pp. 1575-1578,
1993.
[6] T. Y. Kuc, J. S. Lee, and K. Nam, “An iterative learning control theory
for a class of nonlinear dynamic systems,” Automatica, vol. 28, no. 6,
pp. 1215-1221, 1992.
[7] J. X. Xu and Z. Qu, “Robust iterative learning control for a class of
nonlinear systems,” Automatica, vol. 34, pp. 983-988, 1998.
[8] S. Arimoto, “Learning control theory for robotic motion,” International
Journal of Adaptive Control and Signal Processing, vol. 4, pp. 54-564,
1990.
[9] L. S. Shieh, H. M. Dib, and S. Ganesan, “Continuous-time quadratic
regulators and pseudo-continuous-time quadratic regulators with pole
placement in a specific region,” IEE Proceedings Part D, vol. 134, no. 5,
pp. 338-346, 1987.
[10] L. S. Shieh, Y. T. Tsay, and R. E. Yates, “Some properties of matrix-sign
functions derived from continued fractions,” IEE Proceedings Part D, vol.
130, no. 3, pp. 111-118, 1983.
[11] J. S. H. Tsai, L. S. Shieh, and R. E. Yates, “Fast and stable algorithms
for computing the principal nth root of a complex matrix and the matrix-
sector function,” International Journal of Computers and Mathematics with
Applications, vol. 15, no. 11, pp. 903-913, 1988.
[12] K. L. Moore, “Iterative learning control: an expository overview,”
Applied & Computational Controls, Signal Processing, Circuits, vol. 1, pp.
151-214, 1998.
[13] A. Tayebi and J. X. Xu, “Observer-based iterative learning control for a
class of time-varying nonlinear systems,” IEEE Transactions on Circuits
and Systems, vol. 50, no.3, pp. 452-455, 2003.
[14] S. M. Guo, L. S. Shieh, G. Chen, and C. F. Lin, “Effective chaotic orbit
tracker: a prediction-based digital redesign approach,” IEEE Transactions
on Circuits and Systems-I, Fundamental Theory and Application, vol. 47,
no. 11, pp.1557-1570, 2000.
[15] J. H. Halton, “On the efficiency of certain quasi-random sequences of
points in evaluating multidimensional integrals,” Numerische Mathematic,
vol. 2, pp84-90, 1960.
[16] J.C. Van Der Corput, “Verteilungsfunktionen,” Proc. Kon Akad. Wet.,
Amstrerdam, vol. 38, pp. 1058-1066, 1935.

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校外：2006-07-05公開

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