The fuzzy rules are knowledge base of a the fuzzy controller which are determined by using the system internal structure and experience information representing the behavior of the system. However, there is a need for automatic determination methods for the extraction of fuzzy rules, since the experience knowledge will be insufficient for systems with nonlinear or variable behavior. In areas where the control parameters vary, an intelligent system can be developed that uses the adaptive fuzzy method to find the coefficients of the controller in the system to overcome this. In this study, an adaptive fuzzy controller is designed using a learning-based fuzzy inverse model to provide speed control of the electric vehicle. The results obtained in the study showed that the designed inverse model learning-based fuzzy controller model is applicable for the dc motor that will provide speed control of electric vehicles. In the simulation of this model, system behavior has been investigated by way of use constant and variable loads. The obtained results remark that the proposed method satisfies system stability in terms of electric vehicle speed control.

This study focused on the development of a system based on evolutionary Algorithms to obtain the optimum parameters of the fuzzy controller to increase the convergence speed and accuracy of the controller. The aim of the study is to design fuzzy controller without expert’s knowledge by using evolutionary genetic algorithms and carry out on a DC motor. The design is based on optimization of rule bases of fuzzy controller. In the learning stage, the obtained rule base fitness values are measured by working the rule base on the controller. The learning stage is repeated the termination criteria. The proposed fuzzy controller is performed on the dc motor from a PC program using a interface circuit. Note : This article has been accepted for publication in a future issue of ELECTRICA journal, it is now in the early view. Citation information: 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.