Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Neuro-Identifier-Based Tracking Control of Uncertain Chaotic System

  • Conference paper
Advances in Neural Networks - ISNN 2008 (ISNN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5264))

Included in the following conference series:

  • 2960 Accesses

Abstract

A novel neuro-identifier-based tracking control of uncertain nonlinear chaotic system is presented. The algorithm is divided into two contributions. First, a dynamic neural networks is used to identify the unknown chaos, then a dynamic adaptive state feedback controller based on neuro-identifier is derived to direct the unknown chaotic system into desired reference model trajectories. Moreover, the identification error and trajectory error is theoretically verified to be bounded and converge to zero Computer simulations are shown to demonstrate the effectiveness of this proposed methodology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Tanaka, T., Ikeda, T., Wang, H.O.: A Unified Approach to Controlling Chaos via an Lmi-based Fuzzy Control System Design. J. IEEE Transactions on Circuits and Systems 45, 1021–1040 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  2. Wen, T., Yao, N.W.: Adaptive Regulation of Uncertain Chaos with Dynamical Neural Networks. J. Chinese Physics 13, 459–563 (2004)

    Article  Google Scholar 

  3. Chen, G., Dong, X.: From Chaos to Order-Methodologies, Perspective and Applications. World Scientific, Singapore (1998)

    Google Scholar 

  4. Joo, Y.H., Shieh, L.S., Chen, G.: Hybrid State-space Fuzzy Model-based Controller with Dual-rate Sampling for Digital Control of Chaotic Systems. J. IEEE Transactions on Fuzzy Systems 7, 394–408 (1999)

    Article  Google Scholar 

  5. Loria, A., Panteley, E.: Control of the Chaotic Duffing Equation with Uncertainty in All Parameters. J. IEEE Transactions on Circuits and Systems 45, 1252–1255 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  6. Yu, X.: Tracking Inherent Periodic Orbits in Chaotic Dynamic Systems via Adaptive Variable Structure Time-delayed Self Control. J. IEEE Transactions on Circuits and Systems 46, 1408–1411 (1999)

    Article  MATH  Google Scholar 

  7. George, A., Rovithakis Christodoulou, A.: Adaptive Control of Unknown Plants Using Dynamical Neural Networks. J. IEEE Transactions on Systems,Man and Cybernetics 24, 400–412 (1994)

    Article  Google Scholar 

  8. Rouche, N., Habets, P., Laloy, M.: Stability Theory by Lyapunov’s Direct Method. Springer, New York (1977)

    Google Scholar 

  9. Narendra, K.S., Annaswamy, A.M.: Stable Adaptive Systems. Prentice Hall, Englewood Cliffs (1989)

    Google Scholar 

  10. Goodwin, G.C., Mayne, D.Q.: A parameter Estimation Perspective of Continuous Time Model Reference Adaptive Control. J. Automatica 23, 57–70 (1987)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tan, W., Sun, F., Wang, Y., Zhou, S. (2008). Neuro-Identifier-Based Tracking Control of Uncertain Chaotic System. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_81

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-87734-9_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87733-2

  • Online ISBN: 978-3-540-87734-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics