Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Improved Torque Control for In-Wheel AFPMSM Drives Using Fuzzy Logic and Neuro-Fuzzy Controller

Version 1 : Received: 14 June 2023 / Approved: 16 June 2023 / Online: 16 June 2023 (09:51:11 CEST)

How to cite: Ha, V. T. Improved Torque Control for In-Wheel AFPMSM Drives Using Fuzzy Logic and Neuro-Fuzzy Controller. Preprints 2023, 2023061206. https://doi.org/10.20944/preprints202306.1206.v1 Ha, V. T. Improved Torque Control for In-Wheel AFPMSM Drives Using Fuzzy Logic and Neuro-Fuzzy Controller. Preprints 2023, 2023061206. https://doi.org/10.20944/preprints202306.1206.v1

Abstract

This paper will present the torque control design of an AFPMSM, one stator, and one rotor, using an FLC and ANFIS in-wheels fed by a three-level T-type inverter. The Surgeon ambiguous inference file of the FLC controller is built by two input vectors, the stator current error and the derivative of the stator error. These input variables include five membership functions, Negative big (NB), Negative small (NS), Equal zero (ZE), Positive small (PS), and Positive big (PB). The FLC controller is implemented with a 5x5 matrix so that the output stator voltage of the controller is required. The ANFIS controller for the neural network-based feature set and the fuzzy system. The neural network develops the dataset on the stator current error (e) and error integral (∆e). Then, the generated dataset is fed to the fuzzy logic method, and the control rules are developed. This ANFIS controller is caused by the training and testing phases. Finally, the FLC and ANFIS torque controllers are compared with the PI controller. The correctness of the proposed control structured solution is demonstrated by the simulation results of MATLAB/SIMULINK.

Keywords

AFPMSM; Fuzzy Logic Controller; ANN; NFC; FOC.

Subject

Engineering, Electrical and Electronic Engineering

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