Using the iterative learning algorithm as data source for ANFIS training

A methodology for building the rule-base of a fuzzy logic controller (FLC), using the iterative learning algorithm and ANFIS training is tested both in simulation and practical conditions. The methodology is aiming to bring in the intelligent characteristic to controller design procedure, by implying methods that simulates human actions as learning and adapting. The iterative self-learning algorithm is used to gather useful and trustful control data. These are subsequently used as training data for the ANFIS structure. The presented methodology is verified in two steps: i) by running simulations using Matlab environment, and ii) by constructing the rule-base of a fuzzy controller for a DC drive. The reason for developing a DC drive control structure is to analyze method's viability by comparing the results with some already known.