The effect of the number of inference rules of a fuzzy controller on the quality of control of a mobile robot

Abstract

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.

Approximation Possibilities of Fuzzy Control Surfaces for Purpose of Implementation into Microcontrollers

“…The choice of mentioned fuzzy controller parameters was first tested in simulations. The effect of improvement of fuzzy controller control quality decreased with an increasing number of rules as was presented by [15]. The same trend with the increasing number of rules had inference type.…”

“…The optimized controller outperformed all compared controllers. The control quality is mainly influenced by the number of fuzzy membership functions and fuzzy inference rules [15]. Furthermore, with an appropriate selection of fuzzy membership functions shapes and rules formulation, better control results can be obtained [16].…”