engleski

A New Approach to Design of an Adaptive Fuzzy Logic Control System

As it is well known, enough fuzzy rules can approximate any unknown function. Meanwhile, there are applications such as robot control, videorich multimedia and virtual reality, which can require many more rules of greater complexity operating in milliseconds. It becomes necessary to suggest ways to cope with a vexing problem in fuzzy logic: the exponential growth in rules as the number of variables increases. This motivation is utilized in the paper for introducing of an aptive analytic function that determines the positions of centres of output fuzzy sets in each mapping process, instead of definition of rule base So, the paper approach is without any fuzzy logic rule. -Just one: the rule that there are no fuzzy logic rules. We usually chose position error e(t). velocity error c(t), and previous control variable u(t) as the input variables of FLC systems in robotics (or generally in mechanical systems [ 121 ). The output variable is a robot control u(t+l), where t-is a discrete time. The applications of FLC systems in robotics are presented in the references [ 16 to 181. Following the ideas, suggestions and motivations from the previous paragraphs this paper is written with one principal objective: to introduce the adaptive analytic function that determines the positions of centres of output fuzzy sets in each mapping process. instead of definition of rule base. The adaptation has been done by employing of a parameter P-adaptation scheme.

fuzzy logic; control system;Adaptive Control; Fuzzy Logic Control; Robot Control

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