跳到主要內容

臺灣博碩士論文加值系統

(44.222.218.145) 您好!臺灣時間:2024/02/26 22:14
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:郭名峰
研究生(外文):Ming-Feng Kuo
論文名稱:多輸入多輸出非線性系統之類神經網路適應性控制器設計
論文名稱(外文):RBF Neural Network Adaptive Backstepping Controllers for MIMO Nonlinear Systems
指導教授:王偉彥王偉彥引用關係
指導教授(外文):Wei-Yen Wang
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:應用電子科技學系
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:2009
畢業學年度:97
語文別:英文
論文頁數:61
中文關鍵詞:輻狀基底類神經網路適應性控制多輸入多輸出系統倒階控制
外文關鍵詞:Radial basis function (RBF) neural networks (NNs)adaptivebacksteppingMIMO nonlinear systems
相關次數:
  • 被引用被引用:0
  • 點閱點閱:330
  • 評分評分:
  • 下載下載:41
  • 收藏至我的研究室書目清單書目收藏:0
本文將針對多輸入多輸出的典型與非典型的未知非線性系統,提出一個輻狀基底類神經網路適應性控制器,在針對典型未知非線性系統的控制器的架構中,我們運用輻狀基底類神經網路來近似未知的非線性函數,並且利用適應律來調整輻狀基底類神經網路的權重值,在運用倒階法來設計控制器時會造成基底多次微分的問題,而這個問題會往往使得設計出來的控制器無法運用在高階的系統,也因此我們在設計控制器的過程中加入一階濾波器來避免這類問題的發生,在針對非典型未知非線性的控制器的設計上,運用的方法與針對典型未知非線性系統的控制器相似,都是需要用到輻狀基底類神經網路來近似未知的非線性函數,以及用適應律來調整輻狀基底類神經網路的權重值,最後再加入一階濾波器來避免基底多次微分的問題,不過在這之前我們利用均值定理的特性,將控制器與虛擬控制器從非典型的函數中分離出來,才可利用設計好的控制器加以控制,當然我們也會運用李亞普諾夫定理來證明此控制器可以使系統達到穩定。最後附上一些範例模擬,從模擬圖中可得知系統輸出值會盡追蹤到參考訊號。
This thesis proposes a radial basis function neural network adaptive backstepping controller (RBFNN_ABC) for multiple-input multiple-output (MIMO) affine and nonaffine nonlinear systems in block-triangular form. The control scheme incorporates the adaptive neural backstepping design technique with a first-order filter at each step of the backstepping design to avoid the higher-order derivative problem, which is generated by the backstepping design. This problem may produce an unpredictable and unfavorable influence on control performance because higher-order derivative term errors are introduced into the neural approximation model. Finally, simulation results demonstrate that the output tracking error between the plant output and the desired reference output can be made arbitrarily small.
Keywords: Radial basis function (RBF) neural networks (NNs), adaptive, backstepping, MIMO nonlinear systems.
ABSTRACT (In Chinese) i
ABSTRACT (In English) ii
ACKNOWLEDGEMENT iii
CONTENTS iv
LIST OF FIGURES v

Chapter 1 Introduction 1
Chapter 2 Radial Basis Function Neural Network 3
2.1 Introduction of Artificial Neural Networks 3
2.2 Learning rule of ANNs 4
2.2.1 General weight learning rule 5
2.2.2 Perceptron learning rule 6
2.2.3 Widrow-Hoff learning rule 6
2.2.4 Hebbian learning rule 7
2.3 Radial Basis Function Neural Network 7
2.4 Simulation Results 10
Chapter 3 RBFNN Adaptive Backstepping Controllers for MIMO Affine Nonlinear Systems 13
3.1 Problem Formulation 13
3.2 Design of RBFNN_ABC 14
3.3 Simulation Results 24
Chapter 4 RBFNN Adaptive Backstepping Controllers for MIMO Nonaffine Nonlinear Systems 34
4.1 Problem Formulation 34
4.2 Design of RBFNN_ABC 35
4.3 Simulation Results 48
Chapter 5 Conclusion 57

References 58
Autobiography 61
[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 Fuzzy Controllers with Application to Inverted Pendulum Tracking “IEEE Transactions On Fuzzy Systems, Man, And Cybernetics-Part B: Cybernetics, vol. 26, no. 5, October 1996.
[4] J. Y. Choi and J. A. Farrell, “Adaptive Observer Backstepping Control Using Neural Networks,” IEEE Transactions Neural Netw., vol. 12, no. 5, pp. 1103–1112, September. 2001.
[5] 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.
[6] C. H. Lee, B. R. Chung, F. K. Chang, S. K. Chang, “Adaptive Backstepping Control For A Class of Nonlinear Uncertain Systems Using Fuzzy Neural Networks,” IEEE International Conference on Machine Learning and Cybernetics, 19-22, August, 2007.
[7] S. C. Tong, “Indirect Adaptive Fuzzy Backstepping Control for Nonlinear Systems,” IEEE International Conference on Machine Learning and Cybernetics, 13-16, August, 2006.
[8] 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.
[9] F. J. Lin, P. H. Shieh, and P. H. Chou, “ Robust Adaptive Backstepping Motion Control of Linear Ultrasonic Motors Using Fuzzy Neural Network,” IEEE Transactions on Fuzzy System, vol. 16, no. 3, June 2008.
[10] Y. Zhang, B. Fidan, and P. A. Ioannou, “Backstepping Control of Linear Time-VaryingSystems With Known and Unknown Parameters,” IEEE Transactions on Automatic Control, vol. 11, no. 11, November 2003.
[11] M. Smaoui, X. Brun, and D. Thomasset, “Systematic Control of an Electropneumatic System:Integrator Backstepping and Sliding Mode Control,” IEEE Transaction on Control Systems Technology, Vol. 14, no. 5, September 2006.
[12] A. Isidori, Nonlinear Control System. New York: Springer-Verlag, 1989.
[13] M. Krstic, I. Kanellakopoulos, and P.V.Kokotovic, Nonlinear and Adaptive Control Design. New York: Wiley, 1995.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] F. J.Lin, P.H. Shen and S.P. Hsu, “Adaptive Backstepping Sliding mode Control for linear induction motor drive,” IEE Proceeding on Electric Power Applications, vol. 149, no. 3, June 2008.
[20] M. Zhihong, H. R.Wu, and M. Palaniswami, “An Adaptive Tracking Controller Using Neural Networks for A Class of Nonlinear Systems,” IEEE Transactions. Neural Networks, vol. 9, pp. 947–1031, September. 1998.
[21] S. S. Ge, C. C. Hang, and T. Zhang, “Adaptive Neural Network Control of Nonlinear Systems by State and Output Feedback,” IEEE Transactions. Syst., Man, Cybern. B, vol. 29, pp. 818–828, 1999.
[22] M. M. Polycarpou, “Stable Adaptive Neural Control Scheme for Nonlinear Systems,” IEEE Transactions Autom. Control, vol. 41, no. 3, pp. 447–451, March. 1996.
[23] T. Zhang, S. S. Ge, and C. C. Hang, “Adaptive Neural Network Control for Strict-Feedback Nonlinear Systems Using Backstepping Design,” American Control Conference, 2-4 June 1999.
[24] S. Ferrari, “Multiobjective Algebraic Synthesis of Neural Control Systems by Implicit Model Following,” IEEE Transactions. Neural Networks, vol. 20, no. 3, March 2009.
[25] H. Deng, and H. X. Li, “Novel Neural Approximate Inverse Control for Unknown Nonlinear Discrete Dynamical Systems,” IEEE Transactions. Syst., Man, Cybern. B, vol. 35, No. 1, February 2005.
[26] H. Deng, H. X. Li, and Y. H. Wu, “Feedback-Linearization-Based Neural Adaptive Control for Unknown Nonaffine Nonlinear Discrete-Time Systems,” IEEE Transactions. Neural Networks, vol. 19, no. 9, September 2008.
[27] H. E. Psillakis, “Sampled-Data Adaptive NN Tracking Control of Uncertain Nonlinear Systems,” IEEE Transactions. Neural Networks, vol. 20, no. 2, February 2009.
[28] X. B. Liang , J. Wang, “A Recurrent Neural Network for Nonlinear Optimization with a Continuously Differentiable Objective Function and Bound Constraints,” IEEE Transactions. Neural Networks, vol. 11, no. 6, November 2000.
[29] M. Forti, “Some Extensions of a New Method to Analyze Complete Stability of Neural Networks,” IEEE Transactions. Neural Networks, vol. 13, no. 5, September 2002.
[30] S.S.Ge, C.Wang, “Adaptive Neural Control of Uncertain MIMO Nonlinear Systems,” IEEE Transactions on Neural Networks, vol. 15, no.3, may 2004.
[31] 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.
[32] Mculloch, W.S., and W. Pitts, A logical calculus of idea immanent in nervous activity. Bull. Math Biophys.
[33] C.T Lin and C.S George Lee, Neural Fuzzy Systems. Taiwan, 2003
[34] S.I Grossman, and W.R. Derrick, Advanced Engineering Mathematics, Happer & Row, 1998.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top