An Optimal Iterative Learning Scheme for Dynamic Neural Network Modelling

  • Lei Guo
  • Hong Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


In this paper, iterative learning control is presented to a dynamic neural network modelling process for nonlinear (stochastic) dynamical systems. When (B-spline) neural networks are used to model a nonlinear dynamical functional such as the output stochastic distributions in the repetitive processes or the batch processes, the parameters in the basis functions will be tuned using the generalized iterative learning control (GILC) scheme. The GILC scheme differs from the classical model-based ILC laws, with no dynamical input-output differential or difference equations given a prior. For the “model free” GILC problem, we propose an optimal design algorithm for the learning operators by introducing an augmented difference model in state space. A sufficient and necessary solvable condition can be given where a robust optimization solution with an LMI-based design algorithm can be provided.


Linear Matrix Inequality Iterative Learning Control Repetitive Process Performance Index Function Iterative Learning Control Scheme 
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.
    Chen, S., Wu, Y., Luk, B.L.: Combined Genetic Algorithm Optimization and Regularized Orthogonal Least Squares Learning for RBF Networks. IEEE Trans. on Neural Networks 10, 1239–1244 (1999)CrossRefGoogle Scholar
  2. 2.
    Hong, X., Brown, M., Chen, S., Harris, C.J.: Sparse Model Identification Using Orthogonal Forward Regression with Basis Pursuit and D-Optimality. IEE Proc.-Control Theory Appl. 151, 491–498 (2004)CrossRefGoogle Scholar
  3. 3.
    Wang, H.: Bounded Dynamic Stochastic Systems: Modelling and Control. Springer, London (2000)MATHGoogle Scholar
  4. 4.
    Guo, L., Wang, H.: PID Controller Design for Output PDFs of Stochastic Systems using Linear Matrix Inequalities. IEEE Trans on Systems, Man and Cybernetics, Part-B 35, 65–71 (2005)CrossRefGoogle Scholar
  5. 5.
    Guo, L., Wang, H.: Applying Constrained Nonlinear Generalized PI Strategy to PDF Tracking Control through Square Root B-Spline Models. International Journal of Control 77, 1481–1492 (2004)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Guo, L., Wang, H.: Fault Detection and Diagnosis for General Stochastic Systems Using B-Spline Expansions and Nonlinear Filters. IEEE Trans. on Circuits and Systems-I: Regular Papers 52, 1644–1652 (2005)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Wang, H., Zhang, J.F., Yue, H.: Iterative Learning Control of Output Shaping in Stochastic Systems. In: Wang, H., Zhang, J.F., Yue, H. (eds.) Proceedings of the 2005 IEEE International Symposium on Intelligent Control, Cyprus, pp. 1225–1230 (2005)Google Scholar
  8. 8.
    Xu, J.X., Tan, Y.: Linear and Nonlinear Iterative Control. Springer, London (2003)MATHGoogle Scholar
  9. 9.
    Norrlof, M., Gunnarsson, S.: Experimental Comparison of Some Classical Iterative Learning Control Algorithms, Technical report from the Control & Communication group in Linkoping (2002),
  10. 10.
    Amann, N., Owens, D.H., Rogers, E.: Predictive Optimal Iterative Learning Control. International Journal of Control 69, 203–226 (1998)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lei Guo
    • 1
    • 2
  • Hong Wang
    • 2
  1. 1.Research Institute of AutomationSoutheast UniversityNanjingChina
  2. 2.Control Systems CentreThe University of ManchesterManchesterUK

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