DC motor speed control and analysing of disturbance responses using fuzzy logic control methods in single board computers

In this study, the performances of Mamdani and Sugeno inference systems were compared by using fuzzy logic-based inspection methods for speed control of direct current (DC) motors. Both 100 rpm and 300 rpm geared DC motors were used in the applications carried out with encoder feedback on the Raspberry Pi-based experimental infrastructure. With different types of membership functions (triangular, trapezoidal, Gaussian, etc.) and rule bases (3×3, 5×5, 7×7, 9×9), the reactions of the motors, both under no load and under load, were examined. Performance criteria such as root mean square error (RMSE), maximum overshoot (Mp), rise time (Tr), and settling time (Ts) were calculated. The results obtained showed that the Sugeno method offers more stable and faster responses, especially in experiments under load, while the Mamdani method produces higher overshoot values in some cases, despite its flexible structure.