On Development of Fuzzy Controller: The Case of Gaussian and Triangular Membership Functions
Vincent O. S. Olunloyo, Abayomi M. Ajofoyinbo, Oye Ibidapo-Obe
.
DOI: 10.4236/jsip.2011.24036   PDF    HTML     5,761 Downloads   11,427 Views   Citations

Abstract

In recent years, the use of Fuzzy set theory has been popularised for handling overlap domains in control engineering but this has mostly been within the context of triangular membership functions. In actual practice however, such domains are hardly triangular and in fact for most engineering applications the membership functions are usually Gaussian and sometimes cosine. In an earlier paper, we derived explicit Fourier series expressions for systematic and dynamic computation of grade of membership in the overlap and non-overlap regions of triangular Fuzzy sets. In another paper, we extended the methodology to cover cases of cosine, exponential and Gaussian Fuzzy sets by presenting explicit Fourier series representation for encoding fuzziness in the overlap and non-overlap domains of Fuzzy sets. This current paper presents the development of a “Fuzzy Controller” device, which incorporates the formal mathematical representation for computing grade of membership of Gaussian and triangular Fuzzy sets. It is shown that triangular approximation of Gaussian membership function in Fuzzy control can lead to wrong linguistic classification which may have adverse effects on operational and control decisions. The development of the Fuzzy controller demonstrates that the proposed technique can indeed be incorporated in engineering systems for dynamic and systematic computation of grade of membership in the overlap and non-overlap regions of Fuzzy sets; and thus provides a basis for the design of embedded Fuzzy controller for mission critical applications.

Share and Cite:

V. Olunloyo, A. Ajofoyinbo and O. Ibidapo-Obe, "On Development of Fuzzy Controller: The Case of Gaussian and Triangular Membership Functions," Journal of Signal and Information Processing, Vol. 2 No. 4, 2011, pp. 257-265. doi: 10.4236/jsip.2011.24036.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] L. A. Zadeh, “Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,” IEEE Transaction on Systems, Man, and Cybernetics, Vol. SMC-3, No. 1, 1973, pp. 28-44. doi:10.1109/TSMC.1973.5408575
[2] R. E. Bellman and L. A. Zadeh, “Decision-Making in a Fuzzy Environment,” Management Sciences, Vol. 17, No. 4, 1970, pp. 141-164. doi:10.1287/mnsc.17.4.B141
[3] H. R. Berenji and P. Khedkar, “Clustering in Product Space for Fuzzy Inference,” 2nd IEEE International Conference on Fuzzy Systems, San Francisco, 1993, pp. 1402-1407.
[4] D. Ruan and P. F. Fantoni (Eds.), “Power Plant Surveillance and Diagnostics—Applied Research with Artificial Intelligence,” Springer, Heidelberg, 2002.
[5] V. O. S. Olunloyo and A. M. Ajofoyinbo, “Fuzzy-Stochastic Maintenance Model: A Tool for Maintenance Optimization,” International Conference on Stochastic Models in Reliability, Safety, Security and Logistics, Beer Sheva, 15-17 February 2005, pp. 266-271.
[6] J. E. Araujo, S. A. Sandri and E. E. N Macau, “A New Class of Adaptive Fuzzy Control System Applied in Industrial Thermal Vacuum Process,” Proceedings of 8th IEEE International Conference on Emerging Technologies and Factory Automation, Vol. 1, 2001, pp. 426-431.
[7] R. Marinke, and E. Araujo, “Neuro-Fuzzy Modeling for Forecasting Future Dynamical Behaviors of Vibration Testing in Satellites Qualification,” 59th International Astronautical Congress, Glasgow, 2008 (pre-print).
[8] P. C. Moura, L. Rodrigues and E. Araujo, “A Fuzzy System Applied to Sputtering Glass Production,” Proceedings of Simpósio Brasileiro de Automacao Inteligente (SBAI), Florianópolis, 2007, CD (in Portuguese).
[9] J. B. Savkovic-Stevanovic, “Fuzzy Logic Control Systems Modelling,” International Journal of Mathematical Models and Methods in Applied Sciences, Vol. 3, No. 4, 2009, pp. 327-334.
[10] X. Ji and W. Wang, “A Neural Fuzzy System for Vibration Control in Flexible Structures,” Intelligent Control and Automation, Vol. 2, 2011, pp. 258-266. doi:10.4236/ica.2011.23031
[11] V. O. S. Olunloyo, A. M. Ajofoyinbo and A. B. Badiru, “NeuroFuzzy Mathematical Model for Monitoring Flow Parameters of Natural Gas,” Applied Mathematics and Computation, Vol. 149, No. 3, 2004, pp. 747-770. doi:10.1016/S0096-3003(03)00177-2
[12] V. O. S. Olunloyo and A. M. Ajofoyinbo, “A New Approach for Treating Fuzzy Sets’ Intersection and Union: A Basis for Design of Intelligent Machines,” 5th International Conference on Intelligent Processing and Manufacturing of Materials, California, 19-23 July 2005, Proceedings in CD-ROM.
[13] P. J. King and E. H. Mamdani, “The Application of Fuzzy Control Systems to Industrial Processes,” Automatica, Vol. 13, No. 3, 1977, pp. 235-242. doi:10.1016/0005-1098(77)90050-4
[14] H. J. Zimmermann, “Fuzzy Set Theory and Its Applications,” 2nd Edition, Kluwer Academic Publishers, Boston, 1991.
[15] T. J. Ross, “Fuzzy Logic with Engineering Applications,” John Wiley & Sons Ltd, Chichester, 2007.
[16] N. Watanabe, “Statistical Methods for Estimating Membership Functions,” Japanese Journal of Fuzzy Theory and Systems, Vol. 5, No. 4, 1979, pp. 833-846.
[17] I. B. Turksen, “Measurement of Membership Functions and Their Acquisition,” Fuzzy Sets and Systems, Vol. 40, No. 1, 1991, pp. 5-38. doi:10.1016/0165-0114(91)90045-R
[18] D. L. Meredith, C. L. Karr and K. Krishnakumar, “The Use of Genetic Algorithms in the Design of Fuzzy Logic Controllers,” 3rd Workshop on Neural Networks, Auburn, 1992, pp. 549-545.
[19] C. Karr, “Design of an Adaptive Fuzzy Logic Controller Using a Genetic Algorithm,” Proceeding of 4th International Conference on Genetic Algorithms, San Mateo, 1991, pp. 450-457.
[20] M. A. Lee and H. Takagi, “Integrating Design Stages of Fuzzy Systems, Using Genetic Algorithms,” Second IEEE International Conference on Fuzzy Systems, Vol. 1, 1993, pp. 612-617. doi:10.1109/FUZZY.1993.327418
[21] T. J. Ross, “Fuzzy Logic with Engineering Applications,” John Wiley & Sons Ltd, Chichester, 2007.
[22] V. O. S. Olunloyo, A. M. Ajofoyinbo, and A. B. Badiru, “An Alternative Approach for Computing the Union and Intersection of Fuzzy Sets: A Basis for Design of Robust Fuzzy Controller,” Proceedings of 2008 Conference of World Scientific and Engineering Academy and Society, University of Cambridge, 20-24 February 2008, pp. 301-308 (Best Student Paper).
[23] A. M. Ajofoyinbo, “Representation and Encoding of Fuzziness in Engineering Systems: The Case of Fuzzy Controllers,” Ph. D. Thesis, University of Lagos, Nigeria, 2008, unpublished.
[24] V. O. S. Olunloyo, A. M. Ajofoyinbo and O. Ibidapo-Obe, “Design and Implementation of Embedded Fuzzy Controllers Based on Fourier Computation of Membership Functions,” Proceedings of the 8th World Scientific and Engineering Academy and Society International Conference on Electronics, Hardware, Wireless and Optical Communications, University of Cambridge, 21-23 February 2009, pp. 133-142 (Best Paper of the Conference).
[25] I. Abramowitz and I. A. Stegun, “Handbook of Mathematical Functions,” Dover Publications Inc., New York, 1964.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.