An Optimized Multi-Output Fuzzy Logic Controller for Real-Time Control

Noel S. Gunay; Elmer P. Dadios

doi:10.20965/jaciii.2008.p0370

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JACIII Vol.12 No.4 pp. 370-376

doi: 10.20965/jaciii.2008.p0370

(2008)

Paper:

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An Optimized Multi-Output Fuzzy Logic Controller for Real-Time Control

Noel S. Gunay and Elmer P. Dadios

Department of Manufacturing Engineering and Management, De La Salle University, 2401 Taft Avenue, Manila, 1004 Philippines

Received:

April 23, 2007

Accepted:

June 28, 2007

Published:

July 20, 2008

Keywords:

fuzzy control, multiple-fuzzy logic controllers, multi-output fuzzy logic controller, simulation, object-oriented software development

Abstract

Any real-time control application run by a digital computer (or any sequential machine) demands a very fast processor in order to make the time-lag from data sensing to issuance of a control action closest to zero. In some instances, the algorithm used requires a relatively large primary memory which is crucial especially when implemented in a microcontroller. This paper presents a novel implementation of a multi-output fuzzy controller (which is known in this paper as MultiOFuz), which utilizes lesser memory and executes faster than a type of an existing multiple single-output fuzzy logic controllers. The design and implementation of the developed controller employed the object-oriented approach with program level code optimizations. MultiOFuz is a reusable software component and the simplicity of how to interface this to control applications is presented. Comparative analyses of algorithms, memory usage and simulations are presented to support our claim of increased efficiency in both execution time and storage use. Future directions of MultiOFuz are also discussed.

Cite this article as:

N. Gunay and E. Dadios, “An Optimized Multi-Output Fuzzy Logic Controller for Real-Time Control,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.4, pp. 370-376, 2008.

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References

[1] E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” Int. J. Man-Mach. Studies, Vol.7, No.1, pp. 1-13, 1975.
[2] H. Takagi and I. Hayashi, “NN-Driven Fuzzy Reasoning,” Int. J. of Approximate Reasoning, Vol.5, No.3, pp. 191-212, 1991.
[3] I. Hayashi, H. Nomura, H. Yamasaki, and N. Wakami, “Construction of Fuzzy Inference Rules by NDF and NDFL,” Int. J. of Approximate Reasoning, Vol.6, pp. 241-266, 1992.
[4] E. P. Dadios and N. S. Gunay, “A Fuzzy Logic Based Neural Controller for Highly Nonlinear Systems,” Int. J. of Knowledge-Based Intelligent Engineering Systems, Vol.4, No.4, p. 254, October 2000.
[5] E. P. Dadios and D. J. Williams, “Multiple fuzzy logic systems: a controller for the flexible pole-cart balancing problem,” Proc. of the 1996 IEEE Int. Conf. on Robotics and Automation, Minneapolis, Minneota, USA, April 24-26, 1996, Vol.3, p. 2276, 1996.
[6] E. P. Dadios and O. A. Maravillas, “Fuzzy Logic Controller for Micro-Robot Soccer Game,” Proc. of the IEEE Industrial Electronics Conf. 2001 (IECON’01), Denver, Colorado, USA, pp. 2154-2159, Nov. 29-Dec. 2, 2001.
[7] N. H. Reyes and E. P. Dadios, “Dynamic Color Object Recognition,” Journal of Advanced Computational Intelligence, Vol.8, No.1, pp. 29-38, 2004.
[8] L. H. Tsoukalas and R. E. Uhrig, “Fuzzy and Neural Approaches in Engineering,” John Wiley and Sons, Inc., NY, 1997.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” Int. J. Man-Mach. Studies, Vol.7, No.1, pp. 1-13, 1975.

[2] [2] H. Takagi and I. Hayashi, “NN-Driven Fuzzy Reasoning,” Int. J. of Approximate Reasoning, Vol.5, No.3, pp. 191-212, 1991.

[3] [3] I. Hayashi, H. Nomura, H. Yamasaki, and N. Wakami, “Construction of Fuzzy Inference Rules by NDF and NDFL,” Int. J. of Approximate Reasoning, Vol.6, pp. 241-266, 1992.

[4] [4] E. P. Dadios and N. S. Gunay, “A Fuzzy Logic Based Neural Controller for Highly Nonlinear Systems,” Int. J. of Knowledge-Based Intelligent Engineering Systems, Vol.4, No.4, p. 254, October 2000.

[5] [5] E. P. Dadios and D. J. Williams, “Multiple fuzzy logic systems: a controller for the flexible pole-cart balancing problem,” Proc. of the 1996 IEEE Int. Conf. on Robotics and Automation, Minneapolis, Minneota, USA, April 24-26, 1996, Vol.3, p. 2276, 1996.

[6] [6] E. P. Dadios and O. A. Maravillas, “Fuzzy Logic Controller for Micro-Robot Soccer Game,” Proc. of the IEEE Industrial Electronics Conf. 2001 (IECON’01), Denver, Colorado, USA, pp. 2154-2159, Nov. 29-Dec. 2, 2001.

[7] [7] N. H. Reyes and E. P. Dadios, “Dynamic Color Object Recognition,” Journal of Advanced Computational Intelligence, Vol.8, No.1, pp. 29-38, 2004.

[8] [8] L. H. Tsoukalas and R. E. Uhrig, “Fuzzy and Neural Approaches in Engineering,” John Wiley and Sons, Inc., NY, 1997.