Speed Control of Seperately Excited DC motor for Electric Vehicles (EVs) By Inverse Model Based Fuzzy Learning Controller

The adaptation mechanism, which adjusts the controller coefficients according to the parameter changes in the system, ensures that the controller is adaptable. Fuzzy logic can be used to calculate the gain coefficients of the controller in the system by using the adaptive fuzzy method instead of a traditional algorithm for the adaptation mechanism. Normally, the rules of a fuzzy controller system are derived from the system's internal structure and system behavior using expert knowledge that has experienced the system. However, it is not possible to derive fuzzy rules based on expert human knowledge for all systems in this way. It is necessary to use different methods to derive fuzzy rules in highly variable behavior and nonlinear systems. In this study, an adaptive fuzzy controller design for dc motor was made using a learning-based reference model learning algorithm using fuzzy inverse model; It has been shown that it is applicable for dc motors with the results obtained. Simulation of the designed system was carried out using the Matlab program, and the behavior of the system was investigated by using constant and variable loads. The results showed that it is satisfactory to drive a dc motor with adaptive fuzzy controller in terms of system stability.