Scientific Bulletin of the ''Petru Maior" University of Tîrgu Mureș (Dec 2009)
Rule-bases construction through self-learning for a table-based Sugeno-Takagi fuzzy logic control system
Abstract
A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC) is presented and verified, aiming to engage intelligent characteristics to a fuzzy logic control systems. The methodology is a simplified version of those presented in today literature. Some aspects are intentionally ignored since it rarely appears in control system engineering and a SISO process is considered here. The fuzzy inference system obtained is a table-based Sugeno-Takagi type. 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 in constructing the rule-base of a fuzzy controller for a DC drive application. System’s performances and method’s viability are analyzed.
