Skip to main content

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 655))

Abstract

The controllers are interesting type of information systems. Their structure and parameters for atypical applications usually are selected by trial and error method, which can be time-consuming. In this paper a controller structure, which is an ensemble of PID controller and fuzzy system, and an automatic evolutionary method for its construction is presented. The significant feature of proposed method is that the controller elements, that increase its complexity but do not improve the controller precision in the sense of the adopted evaluation function, can be reduced. Moreover, this method allows to use the knowledge of the controlled object. A typical control problem has been used to test the authors approach.

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 EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alia, M.A.K., Younes, T.M., Alsabbah, S.A.: A design of a PID self-tuning controller using LabVIEW. J. Softw. Eng. Appl. 4, 161–171 (2011)

    Article  Google Scholar 

  2. Borzemski, L., Kliber, M., Nowak, Z.: Using data mining algorithms in web performance prediction. Cybern. Syst. Int. J. 40(2), 176–187 (2009)

    Article  MATH  Google Scholar 

  3. Boyd, S., Hast, M., Åström, K.J.: MIMO PID tuning via iterated LMI restriction. Int. J. Robust Nonlinear Control 26, 1718–1731 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  4. Cheng, S., Li, C.W.: Fuzzy PDFF-IIR controller for PMSM drive systems. Control Eng. Pract. 19, 828–835 (2011)

    Article  Google Scholar 

  5. Cpałka, K.: Design of Interpretable Fuzzy Systems. Springer, Heidelberg (2017)

    Google Scholar 

  6. Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Springer, New York (2000)

    Book  MATH  Google Scholar 

  7. Lan, K., Sekiyama, K.: Autonomous viewpoint selection of robot based on aesthetic evaluation of a scene. J. Artif. Intell. Soft Comput. Res. 6(4), 255–265 (2016)

    Article  Google Scholar 

  8. Łapa, K., Szczypta, J., Saito, T.: Aspects of evolutionary construction of new flexible PID-fuzzy controller. Artif. Intell. Soft Comput. 9692, 450–464 (2016)

    Google Scholar 

  9. Łapa, K., Cpałka, K., Przybył, A., Saito, T.: Fuzzy PID controllers with FIR filtering and a method for their construction. In: Artificial Intelligence and Soft Computing. Springer (2017). (in print)

    Google Scholar 

  10. Leva, A., Papadopoulos, A.V.: Tuning of event-based industrial controllers with simple stability guarantees. J. Process Control 23, 1251–1260 (2013)

    Article  Google Scholar 

  11. Maggio, M., Bonvini, M., Leva, A.: The PID + p controller structure and its contextual autotuning. J. Process Control 22, 1237–1245 (2012)

    Article  Google Scholar 

  12. Melanie, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  13. Pamar, K., Arvapalli, R., Sadhu, Y., Viswaraju, S.: Cascaded PID controller design for heating furnace temperature control. IOSR J. Electron. Commun. Eng. 5(3), 76–83 (2013)

    Google Scholar 

  14. Przybył, A., Łapa, K., Szczypta, J., Wang, L.: The method of the evolutionary designing the elastic controller structure. In: Artificial Intelligence and Soft Computing, vol. 9692, pp. 476–492 (2016)

    Google Scholar 

  15. Ribica, A.I., Mataušek, M.R.: A dead-time compensating PID controller structure and robust tuning. J. Process Control 22, 1340–1349 (2012)

    Article  Google Scholar 

  16. Rutkowski, L.: Computational Intelligence. Springer, Heidelberg (2008)

    Google Scholar 

  17. Segovia, R.V., Hägglund, T., Aström, K.J.: Noise filtering in PI and PID control. In: American Control Conference, pp. 1763–1770 (2013)

    Google Scholar 

Download references

Acknowledgment

The project was financed by the National Science Centre (Poland) on the basis of the decision number DEC-2012/05/B/ST7/02138.

The authors would like to thank the reviewers for very helpful suggestions and comments in the revision process.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Krystian Łapa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Łapa, K., Cpałka, K. (2018). PID-Fuzzy Controllers with Dynamic Structure and Evolutionary Method for Their Construction. In: Borzemski, L., Świątek, J., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. ISAT 2017. Advances in Intelligent Systems and Computing, vol 655. Springer, Cham. https://doi.org/10.1007/978-3-319-67220-5_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67220-5_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67219-9

  • Online ISBN: 978-3-319-67220-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics