Tuning of controller parameters using Pythagorean fuzzy similarity measure and optimization algorithms

Classical proportional-integral-derivative (PID) controllers have become the standart in industrial control systems due to their simple design, ease of implementation, and ability to solve emergent problems effectively. Classical PID controllers with integer order were, however, insufficient for controlling increasingly complex systems. In order to control these systems, higher-performance controllers were required; accordingly, fractional-degree PID and PI-PD controllers were suggested. Tuning the controller parameters in accordance with the required dynamics and performance of the controlled systems is a crucial concern. In this thesis, a method based on the use of pythagorean fuzzy similarity measure as a cost function in Ant Colony Optimization for Continuous Domains and Artificial Bee Colony algorithms to determine the optimal parameters of fractional-order PID and PI-PD controllers controlling three different processes is proposed. When the proposed method is compared to the commonly used ITAE, ITSE, IAE, and ISE performance criteria, it has been determined that it improves system response characteristics.