Advertisement

Comparison of binary and fuzzy logic in feedback control of dynamic systems

  • Sérgio N. Silva
  • Matheus F. Torquato
  • Marcelo A. C. Fernandes
Article
  • 41 Downloads

Abstract

The purpose of this paper is to present a performance comparison of expert systems with production rules, based on classical binary logic and fuzzy logic, for feedback control of dynamic systems. The expert system based on binary logic, called ES-PR-BL, was developed in Prolog language, and the system based on fuzzy logic, called ES-PR-FL, was implemented using a Mamdani type inference process. The work presents simulation results for three types of dynamic system: level control in a tank, control of the angular velocity of a DC motor, and control of the linear velocity of a vehicle. The results demonstrate the specificities of each technique and could be used to guide the development of new hybrid control methods, with the aim of improving efficiency in the control of dynamic processes employing expert systems based on production rules. The findings were highly satisfactory and demonstrated the specificities and applicabilities of the two expert systems studied.

Keywords

Binary logic Prolog Fuzzy logic Feedback control 

Notes

Acknowledgements

The authors wishes to acknowledge the financial support of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for their financial support.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Shi A, Yan M, Li J, Xu W, Shi Y (2011) The research of fuzzy PID control application in dc motor of automatic doors. In: 2011 international conference on electrical and control engineering (ICECE), pp 1354–1358Google Scholar
  2. 2.
    Wang HP (2011) Design of fast fuzzy controller and its application on position control of dc motor. In: 2011 international conference on consumer electronics, communications and networks (CECNet), pp 4902–4905Google Scholar
  3. 3.
    Kalavathi M, Reddy C (2012) Performance evaluation of classical and fuzzy logic control techniques for brushless dc motor drive. In: 2012 IEEE international power modulator and high voltage conference (IPMHVC), pp 488–491Google Scholar
  4. 4.
    Xiao Q, Zou D, Wei P (2010) Fuzzy adaptive pid control tank level. In: 2010 international conference on multimedia communications (Mediacom), pp 149–152Google Scholar
  5. 5.
    Perez J, Milanes V, Onieva E, Godoy J, Alonso J (2011) Longitudinal fuzzy control for autonomous overtaking. In: 2011 IEEE international conference on mechatronics (ICM), pp 188–193Google Scholar
  6. 6.
    Jha S, Nair S (2012) A logic programming interface for multiple robots. In: 2012 3rd national conference on emerging trends and applications in computer science (NCETACS), pp 152–156Google Scholar
  7. 7.
    Yim S, Ananthakumar H, Benabbas L, Horch A, Drath R, Thornhill N (2006) Using process topology in plant-wide control loop performance assessment. Comput Chem Eng 31(2):86–99CrossRefGoogle Scholar
  8. 8.
    Matyasik P, Nalepa GJ, Zięcik P (2007) Prolog-based real-time intelligent control of the hexor mobile robot. In: Hertzberg J, Beetz M, Englert R (eds) KI 2007: advances in artificial intelligence, vol 4667. Lecture notes in computer science. Springer, Berlin, pp 485–488CrossRefGoogle Scholar
  9. 9.
    Kunze L, Dolha M, Beetz M (2011) Logic programming with simulation-based temporal projection for everyday robot object manipulation. In: 2011 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 3172–3178Google Scholar
  10. 10.
    Dubinin V, Vyatkin V, Hanisch HM (2006) Modelling and verification of IEC 61499 applications using prolog. In: IEEE conference on emerging technologies and factory automation, 2006. ETFA ’06, pp 774–781Google Scholar
  11. 11.
    Munoz-Hernandez S, Pablos-Ceruelo V, Strass H (2011) Rfuzzy: syntax, semantics and implementation details of a simple and expressive fuzzy tool over prolog. Inf Sci 181(10):1951–1970 Special issue on information engineering applications based on latticesMathSciNetCrossRefGoogle Scholar
  12. 12.
    Guadarrama S, Noz SM, Vaucheret C (2004) Fuzzy prolog: a new approach using soft constraints propagation. Fuzzy Sets Syst 144(1):127–150 (Possibilistic logic and related issues)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Pamplona Filho C, Cunha M, De Azevedo F, Ferrari G (2010) Intellec system: shell for expert systems creation with fuzzy inference machine developed in prolog. In: 2010 international conference on system science and engineering (ICSSE), pp 521–524Google Scholar
  14. 14.
    Le Vh, Liu F, Dk Tran (2009) Fuzzy linguistic logic programming and its applications. Theory Pract Log Progr 9(3):309–341MathSciNetCrossRefGoogle Scholar
  15. 15.
    Liao SH (2005) Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Syst Appl 28(1):93–103MathSciNetCrossRefGoogle Scholar
  16. 16.
    Sahin S, Tolun M, Hassanpour R (2012) Hybrid expert systems: a survey of current approaches and applications. Expert Syst Appl 39(4):4609–4617CrossRefGoogle Scholar
  17. 17.
    Khodadadi H, Ghadiri H (2018) Self-tuning PID controller design using fuzzy logic for half car active suspension system. Int J Dyn Control 6(1):224–232MathSciNetCrossRefGoogle Scholar
  18. 18.
    Nagarale RM, Patre BM (2016) Exponential function based fuzzy sliding mode control of uncertain nonlinear systems. Int J Dyn Control 4(1):67–75MathSciNetCrossRefGoogle Scholar
  19. 19.
    Ogata K (2001) Modern control engineering, 4th edn. Prentice Hall PTR, Upper Saddle RiverzbMATHGoogle Scholar
  20. 20.
    Mathworks (2014) Matlab/simulink. www.mathworks.com. Accessed 20 June 2018
  21. 21.
    SWI-Prolog (2014) Swi-prolog site. www.swi-prolog.org. Accessed 20 June 2018
  22. 22.
    Fernandes MAC (2012) Expert system with prolog to simulink with the SWI-prolog. www.mathworks.com/matlabcentral/fileexchange/36516-expert-system-with-prolog-to-simulink. Accessed 20 June 2018

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Engineering and AutomationFederal University of Rio Grande do Norte (UFRN)NatalBrazil
  2. 2.College of EngineeringSwansea UniversitySwanseaWales, UK

Personalised recommendations