Torque Control of an In-Wheel Axial Fux Permanent Magnet Synchronous Motor (AFPMSM) Using Adaptive Neuro-Fuzzy Inference System (ANFIS) for Electrical Vehicles Applications - Đào Duy Hoàng,; Trần Trọng Minh,; Võ Thanh Hà, (Journal of Technical Education Science, 2024)
This paper presents the design of a torque controller for an in-wheel axial flux permanent magnet synchronous motor based on the adaptive neuro-fuzzy inference system algorithm. This neural network algorithm consists of 5 layers trained based on the Takagi–Sugeno fuzzy logic method. The input layer consists of the error vector and the error derivative of the stator current. The second layer is the fuzzy layer that determines the function of the input vectors. The third layer performs system computations according to fuzzy rules with 5x5 matrices. The fourth layer is the defuzzification layer. The last layer will have the required stator voltage response to the voltage source inverter. Sustainability control evaluation for the AFPMSM using the ANFIS algorithm will be compared with the PI controller if the AFPMSM is unaffected by noise and the AFPMSM parameters change. Simulation MATLAB/SIMULINK performs the results of the evaluation and analysis.