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Generalization of Fuzzy Inference System Based on Boolean and Kleenean Relations FIS-BKR for Modelling and Control

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Applied Computer Sciences in Engineering (WEA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 742))

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Abstract

Fuzzy logic emerges as an important tool in modelling control systems. For this reason, it is necessary to find methodologies involving fuzzy inference and allow to obtain levels of accuracy and interpretability accordance with design requirements. This paper proposes the generalization of a Fuzzy Inference System based on Boolean and Kleenean relations (FIS-BKR) from the conceptual expansion of the virtual actuator.

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References

  1. Cavallo, A., Natale, C., Pirozzi, S., Visone, C.: Limit cycles in control systems employing smart actuators with hysteresis. IEEE/ASME Trans. Mechatron. 10, 172–180 (2005)

    Article  Google Scholar 

  2. Quality Transmission Components: Section 14 (2010). http://www.qtcgears.com/Q410/PDF/techsec14.pdf

  3. Corradini, M., Orlando, G., Parlangeli, G.: Robust control of nonlinear uncertain systems with sandwiched backlash. In: 44th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2005, pp. 8112–8117 (2005). doi:10.1109/CDC.2005.1583475. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1583475

  4. Esbrook, A., Tan, X., Khalil, H.: Self-excited limit cycles in an integral-controlled system with backlash. In: American Control Conference (ACC), pp. 4736–4741 (2013). http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6580570

  5. Espitia, H.E.: Aplicación del Concresor Basado en Relaciones Booleanas para Sistemas de Lógica Difusa Tipo Dos. Universidad Distrital Franciso José de Caldas, Bogotá, Colombia. Master’s degree Thesis (2009)

    Google Scholar 

  6. Liu, W., Song, X., Zhang, Q.: (T) Fuzzy Integral of Multi-dimensional Function. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (2010)

    Google Scholar 

  7. Nordin, M., Gutman, P.O.: Controlling mechanical systems with backlash - a survey. Automatica 38(10), 1633–1649 (2002). http://www.sciencedirect.com/science/article/pii/S000510980200047X

  8. Salazar, O.: Método de Diseñ y Optimizaciòn de Controladores Difusos FIS-BBR Cuasi-Estándar por medio de Lógicas Clásica y Trivalente de Kleene. Universidad Distrital Franciso José de Caldas. Master’s degree Thesis (2014)

    Google Scholar 

  9. Soriano, J.J., González, O.L., Munar, F.V., Ramos, A.A.: Propuesta de concresor basado en relaciones booleanas. Ingeniería 10 (2001)

    Google Scholar 

  10. Stein, E.M., Shakarchi, R.: Real Analysis: Measure Theory, Integration, and Hilbert Spaces. Princeton University Press, Princeton (2009)

    MATH  Google Scholar 

  11. Tarbouriech, S., Queinnec, I., Prieur, C.: Stability analysis and stabilization of systems with input backlash. IEEE Trans. Autom. Control 59(2), 488–494 (2014). doi:10.1109/TAC.2013.2273279. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6558840

  12. Vukic, Z.: Nonlinear Control Systems. CRC Press, New York (2003)

    Book  Google Scholar 

  13. Yang, M., Tang, S., Tan, J., Xu, D.: Study of on-line backlash identification for PMSM servo system. In: IECON 2012–38th Annual Conference on IEEE Industrial Electronics Society, pp. 2036–2042 (2012). doi:10.1109/IECON.2012.6388745. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6388745

  14. Ying, H., Ding, Y., Li, S., Shao, S.: Comparison of necessary conditions for typical Takagi-Sugeno and Mamdani fuzzy systems as universal approximators. IEEE Trans. Syst. Man Cybernet. Part A Syst. Hum. 29(5), 508–514 (1999). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=784177

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Correspondence to Erika Zutta .

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Zutta, E., Gantiva, J., Soriano, J. (2017). Generalization of Fuzzy Inference System Based on Boolean and Kleenean Relations FIS-BKR for Modelling and Control. In: Figueroa-García, J., López-Santana, E., Villa-Ramírez, J., Ferro-Escobar, R. (eds) Applied Computer Sciences in Engineering. WEA 2017. Communications in Computer and Information Science, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-319-66963-2_32

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  • DOI: https://doi.org/10.1007/978-3-319-66963-2_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66962-5

  • Online ISBN: 978-3-319-66963-2

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