Defuzzification Method for a Faster and More Accurate Control

TL;DR

This paper introduces a defuzzification method aimed at enhancing the speed and accuracy of fuzzy logic controllers, which are increasingly used in industrial and consumer applications due to their simplicity and human-like reasoning.

Contribution

The paper proposes a novel defuzzification technique that improves the performance of fuzzy logic controllers by making them faster and more precise compared to existing methods.

Findings

Fuzzy logic controllers respond better to imprecise data.

The new defuzzification method increases control speed.

Enhanced accuracy in control tasks was demonstrated.

Abstract

Today manufacturers are using fuzzy logic in everything from cameras to industrial process control. Fuzzy logic controllers are easier to design and so are cheaper to produce. Fuzzy logic captures the impreciseness inherent in most input data. Electromechanical controllers respond better to imprecise input if their behavior was modeled on spontaneous human reasoning. In a conventional PID controller, what is modeled is the system or process being controlled, whereas in the Fuzzy logic controller, the focus is the human operator behavior. In the first case, the system is modeled analytically by a set of differential equations and their solutions tells the PID controllers how to adjust the system's control parameters for each type of behavior required 3. In the Fuzzy controller these adjustments are handled by a Fuzzy rule based expert system. A logical model of the thinking process a person might go through in the course of manipulating the system. This shift in focus from process to person involved changes the entire approach to automatic control problems.