Controller design using fractional fuzzy inference system

Fuzzy reasoning methods can be expressed as approaches that focus on solving problems involving uncertainty. Nowadays, there is an increasing interest in making fuzzy reasoning methods more effective. In a previous study, Fractional Fuzzy Inference System (FFIS) was proposed as a method based on fractional reasoning, suggesting that it can increase the performance of fuzzy logic applications by appropriately adjusting fractional indices. In the studies carried out in this thesis, controllers were designed for two different systems using FFIS and comparisons were made with the Mamdani Fuzzy Inference System (FIS), where the universal cluster ranges of the input and output variables were adjusted by two different methods. In the first study, position control of the Direct Current (DC) motor was carried out using Mamdani FIS, which was adjusted by trial method. Then, the existing Mamdani FIS was changed to the FFIS obtained using different fractional indices and the control results were compared. In the second study, a control application was carried out with Mamdani FIS adjusted by the Genetic algorithm (GA) optimization method for a high-order system that is thought to be difficult to control with classical methods. Then, the Mamdani FIS adjusted with GA was replaced with the FFIS created by selecting appropriate fractional indices and the control results were compared. As a result, the control applications using the FFIS have achieved more successful results than the Mamdani FIS-based control applications.