Advertisement

Hierarchical Multiple Models Neural Network Decoupling Controller for a Nonlinear System

  • Xin Wang
  • Hui Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

For a nonlinear discrete-time Multi-Input Multi-Output (MIMO) system, a Hierarchical Multiple Models Neural Network Decoupling Controller (HMMNNDC) is designed in this paper. Firstly, the nonlinear system’s working area is partitioned into several sub-regions by use of a Self-Organizing Map (SOM) Neural Network (NN). In each sub-region, around every equilibrium point, the nonlinear system can be expanded into a linear term and a nonlinear term. Therefore the linear term is identified by a BP NN trained offline while the nonlinear term by a BP NN trained online. So these two BP NNs compose one system model. At each instant, the best sub-region is selected out by the use of the SOM NN and the corresponding multiple models set is derived. According to the switching index, the best model in the above model set is chosen as the system model. To realize decoupling control, the nonlinear term and the interaction of the system are viewed as measurable disturbance and eliminated using feedforward strategy. The simulation example shows that the better system response can be got comparing with the conventional NN decoupling control method.

Keywords

Nonlinear System Equilibrium Point Nonlinear Term Induction Motor Global Asymptotic Stability 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wang, X., Li, S.Y., et al.: Multi-model Direct Adaptive Decoupling Control with Application to the Wind Tunnel. ISA Transactions 44, 131–143 (2005)CrossRefGoogle Scholar
  2. 2.
    Lin, Z.L.: Almost Disturbance Decoupling with Global Asymptotic Stability for Nonlinear Systems with Disturbance-affected Unstable Zero Dynamics. Systems & Control Letters 33, 163–169 (1998)CrossRefMATHMathSciNetGoogle Scholar
  3. 3.
    Lin, Z.L., Bao, X.Y., Chen, B.M.: Further Results on Almost Disturbance Decoupling with Global Asymptotic Stability for Nonlinear Systems. Automatica 35, 709–717 (1999)CrossRefMATHGoogle Scholar
  4. 4.
    Ansari, R.M., Tade, M.O.: Nonlinear Model-based Process Control: Applications in Petroleum Refining. Springer, London (2000)MATHGoogle Scholar
  5. 5.
    Khail, H.K.: Nonlinear Systems. Prentice-Hall, New Jersey (2002)Google Scholar
  6. 6.
    Germani, A., Manes, C., Pepe, P.: Linearization and Decoupling of Nonlinear Delay Systems. In: Proceedings of the American Control Conference, pp. 1948–1952 (1998)Google Scholar
  7. 7.
    Wang, W.J., Wang, C.C.: Composite Adaptive Position Controller for Induction Motor Using Feedback Linearization. IEE Proceedings D Control Theory and Applications 45, 25–32 (1998)Google Scholar
  8. 8.
    Wai, R.J., Liu, W.K.: Nonlinear Decoupled Control for Linear Induction Motor Servo-Drive Using The Sliding-Mode Technique. IEE Proceedings D Control Theory and Applications 148, 217–231 (2001)CrossRefGoogle Scholar
  9. 9.
    Balchen, J.G., Sandrib, B.: Elementary Nonlinear Decoupling Control of Composition in Binary Distillation Columns. Journal of Process Control 5, 241–247 (1995)CrossRefGoogle Scholar
  10. 10.
    Haykin, S.S.: Neural Networks: A Comprehensive Foundations. Prentice-Hall, New Jersey (1999)Google Scholar
  11. 11.
    Ho, D.W.C., Ma, Z.: Multivariable Nnternal Model Adaptive Decoupling Controller with Neural Network for Nonlinear Plants. In: Proceedings of the American Control Conference, pp. 532–536 (1998)Google Scholar
  12. 12.
    Yue, H., Chai, T.Y.: Adaptive Decoupling Control of Multivariable Nonlinear Non-Minimum Phase Systems Using Neural Networks. In: Proceedings of the American Control Conference, pp. 513–514 (1998)Google Scholar
  13. 13.
    Kohonen, T.: Self-Organizing Feature Maps. Springer, New York (1995)Google Scholar
  14. 14.
    Hornik, K., Stinchcombe, M., White, H.: Universal Approximation of an Unknown Mapping and Its Derivatives using Multilayer Feedforward Networks. Neural Networks 3, 551–560 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xin Wang
    • 1
    • 2
  • Hui Yang
    • 2
  1. 1.Center of Electrical & Electronic TechnologyShanghai Jiao Tong UniversityShanghaiP.R. China
  2. 2.School of Electrical & Electronic EngineeringEast China Jiao Tong University

Personalised recommendations