跳到主要內容

臺灣博碩士論文加值系統

(44.222.189.51) 您好!臺灣時間:2024/05/20 13:19
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:蔡志仁
研究生(外文):Zhi-Ren Tsai
論文名稱:針對非線性大型延遲系統以類神經網路為基礎的模糊強健控制設計
論文名稱(外文):Robustness Design of Fuzzy Control for Nonlinear Multiple Time-Delay Large-scale Systems via Neural-Network-Based Approach
指導教授:蕭鳳翔
指導教授(外文):Feng-Hsiag Hsiao
學位類別:碩士
校院名稱:長庚大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:英文
論文頁數:52
中文關鍵詞:大型系統類神經網路近似誤差相依於延遲的穩定準則
外文關鍵詞:Large-scale systemneural networkmodeling errordelay-dependent stability criterion
相關次數:
  • 被引用被引用:1
  • 點閱點閱:140
  • 評分評分:
  • 下載下載:16
  • 收藏至我的研究室書目清單書目收藏:0
本文乃利用李亞普諾方法推導出一個能確保非線性大型延遲系統漸進穩定的準則。基於此相依於延遲的穩定準則,平行分散式補償控制架構及類神經網路的方法,合成一組模糊控制器經由平行分散式補償的技術,來穩定非線性大型延遲系統。最後,以一個數值模擬的例子來說明此結果。

The stabilization problem is considered in this study for a nonlinear multiple time-delay large-scale system via neural-network (NN)-based approach. First, the NN model is first employed to approximate each nonlinear multiple time-delay subsystem. Then, a linear difference inclusion (LDI) state-space representation is established for the dynamics of each NN model. According to the LDI state-space representation, a robustness design of fuzzy control is proposed to overcome the effect of modeling errors between the nonlinear multiple time-delay subsystems and the NN models. In terms of Lyapunov's direct method, a delay-dependent stability criterion is hence derived to guarantee the asymptotic stability of nonlinear multiple time-delay large-scale systems. Subsequently, based on this criterion and the decentralized control scheme, a set of fuzzy controllers is synthesized to stabilize the nonlinear multiple time-delay large-scale system. Finally, a numerical example with simulations is given to illustrate the results.

Ⅰ. Introduction
Ⅱ. System Description
2.1. Neural Network (NN) Model
2.2. T-S Fuzzy Control
Ⅲ. Robustness Design of Fuzzy Control and Stability Analysis
3.1 Modeling Error
3.2 Stability in the Presence of Modeling Error
IV. Algorithm
V. Example
VI. Conclusions
Appendix
Reference

[1] M. Ikeda, and T. Ashida, “Stabilization of linear systems with time-varying delay,” IEEE Trans. Automat. Contr., vol. 24, pp. 369-370, 1979
[2] T. Mori, N. Fukama, and M. Kumahara, ”Simple stability criteria for single and composite linear systems with time delays”, Int. J. Control, vol. 34, pp. 1175-1184, 1981
[3] T. Mori, “Criteria for asymptotic stability of linear time delay systems,” IEEE Trans. Automat. Contr., vol. 30, pp. 158-162, 1985.
[4] B. S. Chen, C. S. Tseng, and H. J. Uang, “Robustness design of nonlinear dynamic systems via fuzzy linear control,” IEEE Trans. Fuzzy Syst., vol. 7, pp. 571-585, 1999.
[5] K. Tanaka, “Stability and stabilization of fuzzy-neural-linear control systems,” IEEE Trans. Fuzzy Systems, vol. 3, pp. 438-447, 1995.
[6] K. Tanaka, “An approach to stability criteria of neural-network control systems,” IEEE Trans. Neural Networks, vol. 7, pp. 629-643, 1996.
[7] S. Limanond, and J. Si, “Neural-network-based control design: An LMI approach,” IEEE Trans. Neural Networks, vol. 9, pp. 1422-1429, 1998.
[8] L. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Trans. Syst., Man, Cybern., vol. 3, pp. 28-44, 1973.
[9] R. E. Mohammad, I. B. Turksen, and A. G. Andrew, “Development of a systematic methodology of fuzzy logic modeling,” IEEE Trans. Fuzzy Syst., vol. 6, pp. 346-360, 1998.
[10] J. Yen, and L. Wang, “Simplifying fuzzy rule-based models using orthogonal transformation methods,” IEEE Trans. Syst., Man, Cybern., part B, vol. 29, pp. 13-24, 1999.
[11] K. Tanaka, and M. Sugeno, “Stability analysis and design of fuzzy control system,” Fuzzy Sets and Syst., vol. 45, pp. 135-156, 1992.
[12] H. O. Wang, K. Tanaka, and M. F. Griffin, “An approach to fuzzy control of nonlinear systems: stability and design issues,” IEEE Trans. Fuzzy Syst., vol. 4, pp. 14-23, 1996.
[13] G. Feng, S. G. Cao, N. W. Rees, and C. K. Chak, “Design of fuzzy control systems with guaranteed stability,” Fuzzy Sets and Syst., vol. 85, pp. 1-10, 1997.
[14] X. J. Ma, Z. O. Sun, and Y. Y. He, “Analysis and design of fuzzy controller and fuzzy observer,” IEEE Trans. Fuzzy Syst., vol. 6, pp. 41-51, 1998.
[15] W. J. Wang, and H. R. Lin, “Fuzzy control design for the trajectory tracking on uncertain nonlinear systems,” IEEE Trans. Fuzzy Syst., vol. 7, pp. 53-62, 1999.
[16] K. Kiriakidis, “Fuzzy Model-Based Control of Complex Plants,” IEEE Trans. Fuzzy Syst., vol. 6, pp. 517-529, 1998.
[17] S. Boyd, L. E. Ghaoui, E. Feron, and V. Balakrishnan, Linear matrix inequalities in system and control theory, Philadelphia, PA: SIAM, 1994.
[18] W. J. Wang, and C. F. Cheng, “Stabilising controller and observer synthesis for uncertain large-scale systems by the Riccati equation approach,” IEE Proceeding — D, vol. 139, pp. 72-78, 1992.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
1. 計惠卿(民84),電腦輔助學習的允諾與問題。教學科技與媒體,21,38-46。
2. 林奇賢(民87),網路學習環境的設計與應用。資訊與教育,67,34-50。
3. 何榮桂、王緒溢和徐蕙君(民87),網際網路教學活動設計。資訊與教育,65,39-42。
4. 沈中偉(民87),即時群播遠距教學之教學設計與教學策略探討。遠距教育,7,13-19。
5. 李麗君(民83),科技在美國教育上的運用。教學科技與媒體,15,26-30。
6. 李進寶、韓慧文、鄒景平、洪世家和莊淑閔(民87),資訊網教育訓練的現況與趨勢,遠距教育,6,30-37。
7. 文志超、陳侯君和陳景章(民86),應用ATM網路實現遠距教學。遠距教育,1,29-33。
8. 王千倖(民87),Web-based網路教學管理系統。資訊與教育,67,3-12。
9. 陳姚真(民88),國際遠距教育學程的入學制度與資格鑑定。遠距教育,11,6-15。
10. 陳恆順(民86),台灣大學遠距教學先導系統。遠距教育,1,11-14。
11. 溫嘉榮(民88),資訊與電腦網路科技對教師的衝擊。資訊與教育,72,10-14。
12. 陳立祥(民87),教育部推動有關終身學習與遠距教學之現況。遠距教育,8,38-46。
13. 許秀影、趙榮耀、劉虎城、簡肇胤和林慶懋(民87),虛擬團隊應用於發展網路遠距教學課程軟體之研究。遠距教育,9,15-19。
14. 唐文華(民87),網路小學虛擬教室建立方法研究。遠距教育,5,5-18。
15. 蘇正芬(民86b),遠距教學在交大。遠距教育,1,25-29。