Content of this journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. 1 Corresponding Author: Mehmet Bulut E-mail: mehmetbulut06@gmail.com Received: March 18, 2021 Accepted: May 8, 2021 Available Online Date: August 5, 2021 DOI: 10.5152/electrica.2021.21033 ORIGINAL ARTICLE Optimal Adjustment of Evolutionary Algorithm-Based Fuzzy Controller for Driving Electric Motor with Computer Interface Mehmet Bulut Electricity Generation Company Inc., General Management, Ankara, Turkey Cite this article as: M. Bulut, "Optimal Adjustment of Evolutionary Algorithm-based Fuzzy Controller for Driving Electric Motor with Computer Interface", Electrica, August 5, 2021. DOI: 10.5152/electrica.2021.21033. ABSTRACT This study focused on the development of a fuzzy system based on evolutionary algorithms (EA) to obtain the optimum parameters of the fuzzy controller and increase the convergence speed and accuracy of the controller. The aim of the study is to design a fuzzy controller without expert knowledge by using evolutionary genetic algorithms, and apply it in a DC motor. The design is based on the optimization of rule bases of the fuzzy controller. In the learning stage, the obtained rule base ftness values are measured by working the rule base on the controller. The learning stage is repeated with the termination criteria. The proposed fuzzy controller is applied on the DC motor from a PC program using an interface circuit. Simulated and experimental results have shown that the designed fuzzy controller provides system responses with high performance, low steady-state error for DC motor control, and low settling time. Index Terms—DC motor driver, evolutionary algorithms, fuzzy control Electrica 2021; XX(XX): 1-10 I. INTRODUCTION Control systems have become widespread in all areas of life, and methods for their optimiza- tion are needed to increase sensitivity and performance. The DC motor is widely used in many applications. Brushless DC motors are widely used in high performance control systems due to advances in power electronics and control technology. Position control without oscillation is also desirable in these motors that are fed from a switched source. Fuzzy controllers are used in the industry as an optimization tool, especially in the control of DC motors, because uncertainty can be defined in the input variables of fuzzy systems and they also provide ease of application. Various methods are proposed in the literature to improve the performance of fuzzy controls. Sun et al. investigated the problem of fuzzy adaptive control of unknown nonlinear fractional order systems with external interference and unknown control directions [1]. When the bound functions of the uncertainties were not available, the adaptive fuzzy logic system was used to approach the uncertain function and the corresponding output feedback controller was designed [2]. In the study of Boulkroune and M'Saad, an observer-based fuzzy adaptive controller was examined for nonlinear systems with an unknown control gain mark, and a tracking error observer was created because the system states were not available for measurement. In this controller, the adaptive fuzzy system was used to approximate unknown nonlinear states [3]. The design of a fuzzy controller is often done by simulation or by perform- ing input–output experiments on a prototype of the existing system [4,5]. The most important part in applications using fuzzy controls is the creation of the rule base of the system. The rule base, where acceptable results can be obtained from a fuzzy-controlled system, can only be defined by an expert who knows the system and has experience about the system. This can only be achieved after a long time and many trials in creating the neces- sary control structure for the system. In recent years, due to these and other similar problems, the necessary rule base for the control system has been automatically learned, or exploratory methods have been used to extract the examples. Evolutionary algorithms (EA) are consid- ered as an alternative that lead to faster results than traditional tuning strategies, to improve