Skip to main content
Log in

Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Though the control performances of the fuzzy neural network controller are acceptable in many previous published papers, the applications are only parameter learning in which the parameters of fuzzy rules are adjusted but the number of fuzzy rules should be determined by some trials. In this paper, a Takagi–Sugeno-Kang (TSK)-type self-organizing fuzzy neural network (TSK-SOFNN) is studied. The learning algorithm of the proposed TSK-SOFNN not only automatically generates and prunes the fuzzy rules of TSK-SOFNN but also adjusts the parameters of existing fuzzy rules in TSK-SOFNN. Then, an adaptive self-organizing fuzzy neural network controller (ASOFNNC) system composed of a neural controller and a smooth compensator is proposed. The neural controller using the TSK-SOFNN is designed to approximate an ideal controller, and the smooth compensator is designed to dispel the approximation error between the ideal controller and the neural controller. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived based on the Lyapunov stability theory, thus not only the system stability can be achieved but also the convergence of tracking error can be speeded up. Finally, the proposed ASOFNNC system is applied to a chaotic system. The simulation results verify the system stabilization, favorable tracking performance, and no chattering phenomena can be achieved using the proposed ASOFNNC system.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Slotine JJE, Li WP (1991) Applied nonlinear control. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  2. Lin CM, Peng YF (2005) Missile guidance law design using adaptive cerebellar model articulation controller. IEEE Trans Neural Netw 16(3):636–644

    Article  Google Scholar 

  3. Duarte-Mermoud MA, Suarez AM, Bassi DF (2005) Multivariable predictive control of a pressurized tank using neural networks. Neural Comput Appl 15(1):18–25

    Google Scholar 

  4. Hsu CF, Lin CM, Lee TT (2006) Wavelet adaptive backstepping control for a class of nonlinear systems. IEEE Trans Neural Netw 17(5):1175–1183

    Article  Google Scholar 

  5. Wang Z, Zhang Y, Fang H (2008) Neural adaptive control for a class of nonlinear systems with unknown deadzone. Neural Comput Appl 17(4):339–345

    Article  Google Scholar 

  6. Hsu CF (2009) Design of intelligent power controller for DC-DC converters using CMAC neural network. Neural Comput Appl 18(1):93–103

    Article  Google Scholar 

  7. Lin CT, Lee CSG (1996) Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  8. Lin CM, Hsu CF (2004) Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings. IEEE Trans Fuzzy Syst 12(5):733–742

    Article  Google Scholar 

  9. Leu YG, Wang WY, Lee TT (2005) Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems. IEEE Trans Neural Netw 16(4):853–861

    Article  Google Scholar 

  10. Cheng KH, Hsu CF, Lin CM, Lee TT, Li C (2007) Fuzzy-neural sliding-mode control for DC-DC converters using asymmetric Gaussian membership functions. IEEE Trans Ind Electron 54(3):1528–1536

    Article  Google Scholar 

  11. Da F (2007) Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction. Neural Comput Appl 17(5):531–539

    Google Scholar 

  12. Chen CS, Chen HH (2009) Robust adaptive neural-fuzzy-network control for the synchronization of uncertain chaotic systems. Nonlinear Anal Real World Appl 10(3):1466–1479

    Article  MathSciNet  MATH  Google Scholar 

  13. Juang CF, Lin CT (1998) An on-line self-constructing neural fuzzy inference network and its applications. IEEE Trans Fuzzy Syst 6(1):12–32

    Article  Google Scholar 

  14. Lin CT, Cheng WC, Liang SF (2005) An on-line ICA-mixture-model-based self-constructing fuzzy neural network. IEEE Trans Circuits Syst I 52(1):207–221

    Article  MathSciNet  Google Scholar 

  15. Juang CF, Wang CY (2009) A self-generating fuzzy system with ant and particle swarm cooperative optimization. Expert Syst with Appl 36(3):5362–5370

    Article  Google Scholar 

  16. Gao Y, Er MJ (2003) Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems. IEEE Trans Fuzzy Syst 11(4):462–477

    Article  Google Scholar 

  17. Lin FJ, Lin CH (2004) A permanent-magnet synchronous motor servo drive using self-constructing fuzzy neural network controller. IEEE Trans Energy Conversion 19(1):66–72

    Article  Google Scholar 

  18. Hsu CF (2007) Self-organizing adaptive fuzzy neural control for a class of nonlinear systems. IEEE Trans Neural Netw 18(4):1232–1241

    Article  Google Scholar 

  19. Lin D, Wang X (2010) Observer-based decentralized fuzzy neural sliding mode control for interconnected unknown chaotic systems via network structure adaptation. Fuzzy Sets Syst 161(15):2066–2080

    Article  MATH  Google Scholar 

  20. Cheng KH (2009) Auto-structuring fuzzy neural system for intelligent control. J Franklin Inst 346(3):267–288

    Article  MathSciNet  MATH  Google Scholar 

  21. Lin CM, Chen TY (2009) Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems. IEEE Trans Neural Netw 20(9):1377–1384

    Article  Google Scholar 

  22. Wang LX (1994) Adaptive fuzzy systems and control: design and stability analysis. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  23. Golea N, Golea A, Benmahammed K (2002) Fuzzy model reference adaptive control. IEEE Trans Fuzzy Syst 10(4):436–444

    Article  Google Scholar 

  24. Hsu CF, Chung CM, Lin CM, Hsu CY (2009) Adaptive CMAC neural control of chaotic systems with a PI-type learning algorithm. Expert Syst with Appl 36(9):11836–11843

    Article  Google Scholar 

  25. Chen G, Dong X (1993) On feedback control of chaotic continuous time systems. IEEE Trans Circuits Syst I 40(9):591–601

    Article  MathSciNet  MATH  Google Scholar 

  26. Chen HK (2002) Chaos and chaos synchronization of a symmetric gyro with linear-plus-cubic damping. J Sound Vibr 255(4):719–740

    Article  MATH  Google Scholar 

  27. Yan JJ, Shyu KK, Lin JS (2005) Adaptive variable structure control for uncertain chaotic systems containing dead-zone nonlinearity. Chaos Solit Frac 25(2):347–355

    Article  MathSciNet  MATH  Google Scholar 

  28. Lin CM, Chen CH (2006) Adaptive RCMAC sliding mode control for uncertain nonlinear systems. Neural Comput Appl 15(1):253–267

    Google Scholar 

  29. Peng YF (2009) Robust intelligent sliding model control using recurrent cerebellar model articulation controller for uncertain nonlinear chaotic systems. Chaos Solit Fract 39(1):150–167

    Article  MATH  Google Scholar 

Download references

Acknowledgments

The authors appreciate partial support from the National Science Council of Republic of China under grant NSC 98-2221-E-216-040. The authors would like to express their gratitude to the reviewers for their valuable comments and suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chun-Fei Hsu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kao, CH., Hsu, CF. & Don, HS. Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems. Neural Comput & Applic 21, 1243–1253 (2012). https://doi.org/10.1007/s00521-011-0537-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0537-2

Keywords

Navigation