Skip to main content

The Potential of Fuzzy Logic Applications in Industry

  • Chapter
Fuzzy Logic Foundations and Industrial Applications

Part of the book series: International Series in Intelligent Technologies ((ISIT,volume 8))

Abstract

In this paper an overview is given of the various ways fuzzy logic can be used in industry. The application of fuzzy logic in control is illustrated by four case studies. The first example shows how fuzzy logic, incorporated in the hardware of an industrial controller, helps to improve a classical linear PID controller by reducing its overshoot. In the second example the overshoot of a PID controller is drastically reduced by scheduling of the set-point by means of fuzzy logic. A third study describes how fuzzy logic may be used to fine-tune a PID controller, without the operator having any a priori knowledge of the system to be controlled. The last example in the field of control is from process industry. Here, fuzzy logic supervisory control is implemented in software and enhances the operation of a sintering oven through a subtle combination of priority management and deviation-controlled timing. Finally the key areas of research in fuzzy logic control are discussed. First the paper discusses how fuzzy logic control can be combined with other methods. By properly separating the a priori model knowledge of the process under control, a hierarchy of non-linear control methods is established. After a short discussion of how to optimise an intuitive fuzzy rule base, we show that it is possible to derive conditions for asymptotic stability and robustness for fuzzy logic controllers, using classical non-linear analysis. Next, a completely different application area of fuzzy logic is discussed: sensor fusion. After a short overview on the various types of sensor fusion methods two case studies in this field are treated: the fuzzy human body detector and the earth quake detector. The review of the main industrial application fields of fuzzy logic is concluded with a case study on a fuzzy Health Management expert systems.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. A. J. van der Wal and T.C. Mattaar, Performance Fine-Tuning of a PID Controller by Fuzzy Logic, Joint Japanese-European Symposium on Fuzzy Systems, Berlin, 1992, Vol. 8 (1994) 245ā€“255.

    Google ScholarĀ 

  2. T. Zhao, A. J. van der Wal and T. Virvalo, Robust Hydraulic Position Control by a Fuzzy State Controller, Proc. First Int. FLINS workshop (1994) 91ā€“95.

    Google ScholarĀ 

  3. B.W. Grant and A.J. van der Wal, Auto MF: A neural Network Tool for the Generation and Tuning of Fuzzy Knowledge, Proc. First Int. FLINS workshop (1994) 103ā€“108.

    Google ScholarĀ 

  4. H. Choe and J. B. Jordan, On the Optimal Choice of Parameters in a Fuzzy C-Means Algorithm, Proc. IEEE Int. Conf. Fuzzy Systems, San Diego (1992) 349ā€“354.

    Google ScholarĀ 

  5. W. J. M. Kickert and E. H. Mamdani, Analysis of a fuzzy logic controller, Fuzzy Sets and Systems 1 (1978) 29ā€“44.

    ArticleĀ  MATHĀ  Google ScholarĀ 

  6. M. Sugeno, T. Murofushi, T. Mori, T. Tatematsu and J. Tanaka, Fuzzy algorithmic control of a model car by oral instructions, Fuzzy Sets and Systems 32 (1989) 207ā€“219.

    ArticleĀ  Google ScholarĀ 

  7. Y.-M. Pok and J.-X. Xu, Why is Fuzzy Control Robust?, Proc. Third Int. Conf. Fuzzy Systems, Orlando (1994) 1018ā€“1022.

    Google ScholarĀ 

  8. J. J.E. Slotine and W. Li, Applied Non-Linear Control, Prentice Hall International (1991) 276ā€“284.

    Google ScholarĀ 

  9. H.F. Durrant-Whyte, Integration, co-ordination and control of multi-sensor robot systems, Kluwer (1988).

    Google ScholarĀ 

  10. E. Waltz and J. Llinas, Multisensor data fusion, Artech House (1990).

    Google ScholarĀ 

  11. E. Prugovecki, Can. J. Phys. 45 (1967) 2173ā€“2219.

    ArticleĀ  MATHĀ  Google ScholarĀ 

  12. A. J. van der Wal, The role of fuzzy set theory in the conceptual foundations of quantum mechanics: an early application of fuzzy measures, Proc. FAPTā€™95, 234ā€“245, edited by G. de Cooman, D. Ruan, and E. Kerre. Advances in Fuzzy Systems Vol. 8, World Scientific Singapore.

    Google ScholarĀ 

  13. A. Dempster, Upper and lower probabilities induced by a multivalued mapping, The Annals of Mathematical Statistics 38 (1967) 325ā€“339.

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  14. G. Shafer, A mathematical theory of evidence, Princeton University Press, Princeton (1976).

    MATHĀ  Google ScholarĀ 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

Ā© 1996 Kluwer Academic Publishers

About this chapter

Cite this chapter

van der Wal, A.J. (1996). The Potential of Fuzzy Logic Applications in Industry. In: Ruan, D. (eds) Fuzzy Logic Foundations and Industrial Applications. International Series in Intelligent Technologies, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1441-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-4613-1441-7_14

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8627-1

  • Online ISBN: 978-1-4613-1441-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics