MATEC Web Conf.
Volume 299, 2019Modern Technologies in Manufacturing (MTeM 2019)
|Number of page(s)||6|
|Published online||02 December 2019|
The effect of the number of inference rules of a fuzzy controller on the quality of control of a mobile robot
Department of Electrical Engineering, Automation and Informatics, Faculty of Engineering, Slovak University of Agriculture in Nitra,
Tr. A. Hlinku 2,
2 Institute of Technology and Business in České Budějovice, Faculty of Technology, Department of Mechanical Engineering, Okružní 10, 370 01 České Budějovice, Czech Republic
* Corresponding author: email@example.com
The application of intelligent control algorithms in the field of autonomous mobile robotics enables effective control of mobile robots with a minimal possible error. At present, most of commonly used systems to control an autonomous mobile robot are, however, too complicated to design. Our goal was to design a fuzzy controller with an optimal number of inference rules in a way to achieve the best possible level of quality of mobile robot control. The proposed controller was implemented in the mobile robot EN20, where the time of regulation and the absolute and the quadratic control surface were used to evaluate quality parameters. The analysis of the quality of control was performed with the use of a fuzzy controller with 9, 25 and 49 inference rules. We found from the results of modelling that the greatest influence on the quality of control of a mathematical model of the mobile robot had the number of inference rules of the fuzzy controller. Mathematical and graphical dependence of the quality of control on the number of inference rules was calculated from the parameters of the quality of control. The results of the research are equations of the curves of the individual parameters of control of quality, which show that for the control of the autonomous mobile robot EN 20, that the optimal fuzzy controller has 49 inference rules, triangular functions of the pertinence of individual linguistic values and it is defuzzificated by Centroid method.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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