A Self-Learning Based Fuzzy Controller for DC Drive Control

Abstract

A self-learning-based methodology for building the rule base of a fuzzy logic controller (FLC) is presented and verified in a practical experiment. The methodology is a simplified version of those presented in available research papers. Some aspects are intentionally ignored as they rarely appear in control system engineering and a SISO process is considered here. The fuzzy inference system obtained is a table-based Sugeno-Takagi type. The system’s desired performance is defined by a reference model and rules are extracted from recorded data after the correct control actions are learned. The presented algorithm is tested for a DC drive control application.