Fuzzy Controller for Simplified Model of Mobile Vehicle Steering Booster

• Autorzy: Koleda Peter , Naščák Ľubomir , Hrčková Mária , Magdolinič Róbert

Identyfikatory

DOI

10.2478/mape-2021-0004

• Języki publikacji: EN

Abstrakty

EN

The article is aimed on the possibility of fuzzy control of an experimental device that represents the simplified model of automobile steering booster. The main part of model consists of a graphical unit illustrating the control of speed and supporting torque. Based on the structure of fuzzy controller, various counts and configurations of fuzzy sets were defined, whereby the relevant results were achieved by their application on controlled system.

Słowa kluczowe

EN

automobile booster; fuzzy sets; fuzzy controller; speed control

• Wydawca: STE GROUP ; De Gruyter
• Czasopismo: Multidisciplinary Aspects of Production Engineering
• Rocznik: 2021
• Tom: Vol. 4, Iss. 1
• Strony: 40--50
• Opis fizyczny: Bibliogr. 14 poz., fig., tab.

Twórcy

autor

Koleda Peter

• Technical University in Zvolen, Slovak Republic

• https://orcid.org/0000-0003-3996-2621

autor

Naščák Ľubomir

• Technical University in Zvolen, Slovak Republic

autor

Hrčková Mária

• Technical University in Zvolen, Slovak Republic

autor

Magdolinič Róbert

Bibliografia

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• 4. Gacto, M. J., Alcalá, R., Herrera, F. (2011). Interpretability of linguistic fuzzy rule-bases systems: An overview of interpretability measures. Information Sciences, 181. 4340-4360.
• 5. Holiš, M. (2001). Using of Neural-Fuzzy Systems for Control of Continual Processes in Indeterminacy Conditions. Thesis, Slovak Technical University.
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• 7. Kasabov, N. K. (1998). Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering. London, England: MIT Press.
• 8. Kecman, V. (2001). Learning and soft computing: support vector machines, neural networks and fuzzy logic models. London, England: MIT Press.
• 9. Koleda, P., Naščák, Ľ. (2009). Development and using of communication by means of Ethernet. In Perspective technology and materials for technical applications (pp. 116-121). Trnava, Inweld consulting.
• 10. Modrlák, O. (2004). Fuzzy Control and Regulation. Liberec, Czech Republic: TU Liberec.
• 11. Pivonka, P. (2003). Higher Forms of Control. Brno, Czech Republic: VUT.
• 12. Pradhan, S. K., Parhi, D. R., Panda, A. K. (2006). Neuro-fuzzy technique for navigation of multiple mobile robots. Fuzzy Optimization and Decision Making, 5(3), 255-288
• 13. Wang, D., Zeng, X., Keane, J. (2013). A simplified structure evolving method for Mamdami fuzzy system identification and its application to thigh-dimensional problems. Information Sciences, 220, 110-123.
• 14. Yager, R. R., Filev, D. P. (1994). Essentials of Fuzzy Modeling and Control. England: John Wiley & Sons.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).