Skip to main content
Springer Nature Link
Log in

An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators

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

Abstract

The present paper describes the development of a Takagi-Sugeno (TS)-type Neuro-fuzzy system (NFS) for dynamic modeling of robot manipulators. The NFS has been trained by a relatively new combinatorial metaheuristic optimization method, called particle swarm optimization (PSO). The development of such an intelligent, robust, dynamic models for robot manipulators can immensely help in deriving proper position/velocity control strategies in offline situations with these accurately developed models. The proposed PSO-based NFS has been successfully applied to two-link and three-link model robot manipulators.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Spong M, Vidyasagar M, (1989) Robot dynamics and control. Wiley, New York

    Google Scholar 

  2. Craig JJ (1993) Adaptive control of manipulators through repeated trials. In: Spong MW, Lewis FL, Abdallah CT (eds) Robot control: dynamics, motion planning, and analysis. IEEE, IEEE Control Systems Society, pp 206–213

  3. Craig JJ, Hsu P, Sastry SS (1987) Adaptive control of mechanical manipulators. Int J Robotic Res 6(2):16–28

    Google Scholar 

  4. Patino HD, Carelli R, Kuchen BR (2002) Neural networks for advanced control of robot manipulators. IEEE Trans Neural networks 13:343–354

    Article  Google Scholar 

  5. Su C-Y, Stepanenko Y (1995) Hybrid adaptive/robust motion control of rigid-link electrically-driven robot manipulators. IEEE Trans Robotics Automation 11:426–432

    Article  Google Scholar 

  6. Lee CH, Teng CC (2004) Identification and control of dynamic systems using recurrent fuzzy neural networks. IEEE Trans Fuzzy Syst 8:349–366

    Google Scholar 

  7. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of 1999 congress evolutionary computation. IEEE Service Center, Piscataway, NJ, pp 1945–1950

  8. Shi Y, Eberhart RC (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of 2000 congress evolutionary computation. San Diego, pp 84–88

  9. Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: Proceedings 1997 International conference evolutionary computation. Indianapolis, pp 303–308

  10. Angeline P (1998) Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. In: Porto VW, Saravanan N, Waagen D, Eiben AE (eds) Evolutionary programming VII. Springer, Berlin Heidelberg New York, pp 601–610

    Google Scholar 

  11. Clerc M, Kennedy J, (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73

    Article  Google Scholar 

  12. Belarbi K, Titel F, (2000) Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach. IEEE Trans Fuzzy Syst 8:398–405

    Article  Google Scholar 

  13. Conradie A, Nieuwoudt I, Aldrich C, (2000) Nonlinear neurocontroller development with evolutionary reinforcement learning. In: 9th national meeting of SAIChe, Secunda, South Africa

  14. Conradie A, Miikkulainen R, Aldrich C, (2002) Adaptive control utilising neural swarming. In: Proceedings of the genetic and evolutionary computation conference, New York

  15. Ramos MC Jr, Koivo AJ (2002) Fuzzy logic-based optimization for redundant manipulators. IEEE Trans Fuzzy Syst 10:498–509

    Article  Google Scholar 

  16. Chatterjee A, Watanabe K, (2005) An adaptive fuzzy strategy for motion control of robot manipulators. Soft Computing 9:185–193

    Article  Google Scholar 

Download references

Acknowledgements

Amitava Chatterjee would like to thank The Japanese Government Scholarship (Monbukagakusho) authorities for giving him the opportunity to carry out this work in the Department of Advanced System Control Engineering, Graduate School of Science and Engineering, Saga University, Saga, Japan under the guidance of Prof. Keigo Watanabe.

Author information

Authors and Affiliations

  1. Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University, 1 Honjomachi, Saga, 840-8502, Japan

    Amitava Chatterjee & Keigo Watanabe

Authors
  1. Amitava Chatterjee
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Keigo Watanabe
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Keigo Watanabe.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chatterjee, A., Watanabe, K. An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators. Neural Comput & Applic 15, 55–61 (2006). https://doi.org/10.1007/s00521-005-0008-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-005-0008-8

Keywords