Adaptive Control Based on Recurrent Fuzzy Wavelet Neural Network and Its Application on Robotic Tracking Control

  • Wei Sun
  • Yaonan Wang
  • Xiaohua Zhai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


A kind of recurrent fuzzy wavelet neural network (RFWNN) is constructed by using recurrent wavelet neural network (RWNN) to realize fuzzy inference. In the network, temporal relations are embedded in the network by adding feedback connections on the first layer of the network, and wavelet basis function is used as fuzzy membership function. An adaptive control scheme based on RFWNN is proposed, in which, two RFWNNs are used to identify and control plant respectively. Simulation experiments are made by applying proposed adaptive control scheme on robotic tracking control problem to confirm its effectiveness.


Adaptive Control Fuzzy Rule Recurrent Neural Network Cellular Neural Network Robotic Manipulator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Park, Y.M., Choi, M.S., Lee, K.Y.: An Optimal Tracking Neuro-controller for Nonlinear Dynamic Systems. IEEE Trans. on Neural Networks 7, 1099–1110 (1996)CrossRefGoogle Scholar
  2. 2.
    Narendra, K.S., Parthasarathy, K.: Identification and Control of Dynamical Systems Using Neural Networks. IEEE Trans. on Neural Networks 1, 4–27 (1990)CrossRefGoogle Scholar
  3. 3.
    Brdys, M.A., Kulawski, G.J.: Dynamic Neural Controllers for Induction Motor. IEEE Trans. on Neural Networks 10, 340–355 (1999)CrossRefGoogle Scholar
  4. 4.
    Ku, C.C., Lee, K.Y.: Diagonal Recurrent Neural Networks for Dynamic Systems Control. IEEE Trans. on Neural Networks 6, 144–156 (1995)CrossRefGoogle Scholar
  5. 5.
    Ma, S., Ji, C.: Fast Training of Recurrent Neural Networks Based on The EM Algorithm. IEEE Trans. on Neural Networks 9, 11–26 (1998)CrossRefGoogle Scholar
  6. 6.
    Sundareshan, M.K., Condarcure, T.A.: Recurrent Neural-network Training by A Learning Automaton Approach for Trajectory Learning and Control System Design. IEEE Trans. on Neural Networks 9, 354–368 (1998)CrossRefGoogle Scholar
  7. 7.
    Liang, X.B., Wang, J.: A Recurrent Neural Network for Nonlinear Optimization with A Continuously Differentiable Objective Function and Bound Constraints. IEEE Trans. on Neural Networks 11, 1251–1262 (2000)CrossRefGoogle Scholar
  8. 8.
    Lee, C.H., Teng, C.C.: Identification and Control of Dynamic Systems Using Recurrent Fuzzy Neural Networks. IEEE Trans. on Fuzzy Systems 8, 349–366 (2000)CrossRefGoogle Scholar
  9. 9.
    Lin, C.T., Chang, C.L., Cheng, W.C.: A Recurrent Fuzzy Cellular Neural Network System with Automatic Structure and Template Learning. IEEE Trans. on Circuits and Systems 51, 1024–1035 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wei Sun
    • 1
  • Yaonan Wang
    • 1
  • Xiaohua Zhai
    • 1
  1. 1.College of Electrical and Information EngineeringHunan UniversityChangshaP.R. China

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