Field Oriented Control of AFPMSM for Electrical Vehicle Using Adaptive Neuro-Fuzzy Inference System (ANFIS)


Axial Flux Permanent Magnet Synchronous Motor (AFPMSM) are very attractive candidates for driving applications due to their high efficiency, high torque-to-weight ratio, high power density, small magnetic thickness, and simplicity of construction. On the other hand, AFPMSM produces undesirable torque ripple in the developed electromagnetic torque, affecting their output performance. An intelligent control method is proposed in this paper to reduce torque ripple and improve the dynamic performance of AFPMSM. The vector control, employing the Field Oriented Control (FOC) technique, was used to improve the dynamic performance of the AFPMSM. The speed and torque controllers are achieved using the decoupling method. The intelligent control was designed to improve the performance of AFPMSM obtained from PI-PSO. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was used as an Intelligent controller to integrate both the speed and torque constraints in a single training procedure. Training data for ANFIS was obtained from PI-PSO with a multi-objective cost function that includes the torque ripple and speed response criteria . The approach gave great results in terms of speed performance in different operating conditions and in tracking the required speed in load and no-load. In addition, the torque ripple was reduced by 10.04% and 46.67% compared with PI-PSO and Multi-objective cost function of speed, respectively.