Performance Trade-Offs in AI-Based Speed Control of PMDC Motors: A Comparative Study of Fuzzy Logic and Neural Network Controllers

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This paper aims to compare the speed control of DC motors using two distinct artificial intelligence controllers: fuzzy logic controllers (FLC) and artificial feedforward neural networks (AFFNN). This study presents the first comprehensive comparison of FLC and ANN under identical test conditions, offering actionable guidelines for industrial applications. The driving system has been designed and tested using MATLAB/Simulink. Simulation results show that the AFFNN controller’s rise time at 130 (rad/s) is 14.9 ms, whereas the fuzzy logic controller’s is 32.4 ms. Furthermore, the neural network controller and fuzzy logic controller overshoot by 2.6e-06% and 0%, respectively. However, the neural controller takes 213.5 ms to reach its peak, whereas the fuzzy controller achieves this level earlier, at 94.6 ms. AFFNN gives a faster rise time and minimal settling time. However, FLC gives a faster peak response with zero overshoot for effective PMDC motor control.