研究生: |
楊建宏 |
---|---|
論文名稱: |
適應性倒階類神經濾波控制器與其在伺服馬達控制上之應用 Adaptive Backstepping Neural Network Controller with filters and its Applications in Server Motors |
指導教授: | 呂藝光 |
學位類別: |
碩士 Master |
系所名稱: |
工業教育學系 Department of Industrial Education |
論文出版年: | 2009 |
畢業學年度: | 97 |
語文別: | 中文 |
論文頁數: | 87 |
中文關鍵詞: | 類神經網路 、適應控制 、倒階控制(Backstepping) 、濾波器 、直流伺服馬達 |
英文關鍵詞: | Neural networks, adaptive control, backstepping control, filters, DC servo motors |
論文種類: | 學術論文 |
相關次數: | 點閱:341 下載:20 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
本論文針對一未知非線性控制系統,提出一個以輻狀基底函數(radial basis functions )類神經網路(neural networks)的適應性倒階(Backstepping)控制器。在適應性倒階控制器設計中,將使用輻狀基底函數類神經網路近似未知非線性函數。一般的倒階控制器設計過程中,必須要對虛擬控制輸入微分,因而導致輻狀基底函數類神經網路在近似過程中需要執行多次微分運算。因此,為了避免輻狀基底函數類神經網路在適應性倒階控制器設計中需多次微分,本論文使用濾波器取代微分運算,以減少計算複雜度。此外,藉由李亞普諾夫函數分析整體閉迴路系統的穩定度。
最後,本文利用數個電腦模擬範例和直流伺服馬達實驗來驗証所提出方法效能與應用性,其中直流伺服馬達實驗包括具有正負電壓輸出之切換式直流電壓轉換電路設計、電壓回授電路設計與脈波寬度調變(Pulse Width Modulation)控制器設計等。
In this thesis, a radial basis function (RBF) neural adaptive backstepping controller for a class of nonlinear system with unknown nonlinearities is proposed. In backstepping design procedure, the RBF neural networks are used to approximate unknown nonlinear functions. In general, based on backstepping design technique, virtual controls must be differentiated. For this reason, differentiating the RBF neural networks is required. In order to avoid the requirement of the nth derivative of the RBF neural networks, first-order filters are added into backstepping design such that the computation burden can
be effectively alleviated. In addition, the stability of the closed-loop system with first-order filters is analyzed by Lyapunov functions.
Finally, simulation results and experiment results are provided to demonstrate the effectiveness and applicability of the proposed method. The experiment is composed of a DC servo motor, a switch DC-DC converter, voltage feedback circuits, and PWM (Pulse Width Modulation) controller.
[1] M.Hojati and S.Gazor, ”Hybrid Adaptive Fuzzy Identification and Control of Nonlinear Systems”. IEEE Transactions On Fuzzy Systems, vol. 10, no. 2, April 2002
[2] Choi, J.Y.; Farrell, J.A.”Nonlinear adaptive control using networks of piecewise linear approximators”.Proceedings of 38thConference on Decision & Control Phoenix, Arizona USA. December 1999
[3] L.X.Wang,”Stable adaptive fuzzycontrollers with application to Inverted pendulum tracking “IEEE Transactions On Fuzzy Systems, Man, And Cybernetics-Part B: Cybernetics, vol. 26, no. 5, October 1996
[4] A. Isidori, Nonlinear Control System. New York: Springer-Verlag, 1989.
[5] M. Krstic, I. Kanellakopoulos, and P.V.Kokotovic, Nonlinear and Adaptive Control Design. New York: Wiley, 1995.
[6] I. Kanellakopoulos, P. V. Kokotovic, and A. S. Morse, “Systematic design of adaptive controller for feedback linearizable system,” IEEE Transactions Automat. Contr., vol. 36, pp. 1241–1253, 1991.
[7] C. Kwan and F. L. Lewis, “Robust backstepping control of nonlinear systems using neural networks,” IEEE Transactions Syst., Man, Cybern. A, vol. 30, pp. 753–765, 2000.
[8] T. Knohl and H. Unbehauen, “ANNNAC—extension of adaptive backstepping algorithm with artificial neural networks,” Inst. Elect. Eng. Proc. Contr. Theory Appl., vol. 147, pp. 177–183, 2000.
[9] C. M. Kwan and F. L. Lewis, “Robust backstepping control of induction motors using neural networks,” IEEE Transactions Neural Networks, vol. 11, pp. 1178–1187, 2000.
[10] J. Y. Choi and J. A. Farrell, “Adaptive observer backstepping control using neural networks,” IEEE Transactions Neural Networks, vol. 12, pp. 1103–1112, 2001.
[11] Y. Zhang, P.Y. Peng, and Z.P. Jiang, “Stable Neural Controller Design for Unknown Nonlinear Systems Using Backstepping,” IEEE Transactions on Neural Networks, vol. 11, no. 6, November 2000
[12] C.F. Hsu, C.M. Lin, and T.T. Lee, “Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems,” IEEE Transactions on Neural Networks, vol. 17, no. 5, September 2006.
[13] Y.Zhang, P.Y.Peng, Z.P.Jiang, “Stable Neural Controller Design for Unknown Nonlinear Systems Using Backstepping,” IEEE Transactions on Neural Networks, vol. 11, no. 6, November 2000.
[14] Y.Li, S.Qiang, X.Zhuang, and O.Kaynak, “Robust and Adaptive Backstepping Control for Nonlinear Systems Using RBF Neural Networks,” IEEE Transactions on Neural Networks, vol. 15, no. 3, May 2004.
[15] S.S.Ge, C.Wang, “Adaptive Neural Control of Uncertain MIMO Nonlinear Systems,” IEEE Transactions on Neural Networks, vol. 15, no.3, may 2004.
[16] D.Wang and J.Hung, “Neural Network-Based Adaptive Dynamic Surface Control for a Class of Uncertain Nonlinear Systems in Strict-Feedback Form,” IEEE Transactions on Neural Networks, vol. 15, no.1, january 2005.
[17] C.H.Wang, W.Y.Wang, T.T.Lee, and P.S.Tseng, “Robust and Adaptive Backstepping Control for Nonlinear Systems Using RBF Neural Networks,” IEEE Transactions on Neural Networks, VOL. 15, no. 3, May 2004.
[18] 張斐章、張麗秋(2006):類神經網路,東華書局。
[19] Haykin S. 1996.Adaptive Filter Tbeory, 3rd edition. Upper Saddle River NJ: Prentice-Hall.
[20] 徐正育(2004):應用FPGA於電動機車驅動系統之分析及設計,私立大葉大學機電自動化研究所碩士論文。
[21] 王醴(2002):工業電子學,全威圖書有限公司。
[22] 廖東成、王順忠(2004):電力電子學,滄海書局。