A Practical Review of a Design Method for Fuzzy Controllers Based On Self-Learning Algorithm

Abstract

A method for building the rule-base of a fuzzy controller, using iterative learning and adaptive neural fuzzy training is tested in practical conditions. This method aims to engage intelligent features to control design procedures, by implying concepts and techniques from artificial intelligence as learning or adapting. An iterative self-learning algorithm is used to gather useful and trustful control data for the process. These are subsequently used as training data for the ANFIS structure. The method is verified by constructing the rule-base of a fuzzy controller for a DC drive. System’s performances and method’s viability are analyzed.