Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : ANFIS

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

Nagham S. Farhan; Abdulrahim T. Humod; Fadhil Hasan

Engineering and Technology Journal, 2021, Volume 39, Issue 10, Pages 1571-1582
DOI: 10.30684/etj.v39i10.1969

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.

Evaporation Estimation Using Adaptive Neuro-Fuzzy Inference System and Linear Regression

Ali H. Al-Aboodi

Engineering and Technology Journal, 2014, Volume 32, Issue 10, Pages 2465-2474

Evaporation is important for water planning, management and hydrological practices, and it plays an influential role in the management and development of water resources. This study demonstrates the application of two different models, adaptive neuro-fuzzy inference system (ANFIS), and linear regression (LR) models for estimating monthly pan evaporation in Basrah City, south of Iraq. In the first part of this study, the ANFIS model is used twice, in the first one, the temperature is used as input data only, and in the second one, the temperature and relative humidity are used as input data for predicting the evaporation. A verification test is added to check the model correctness by matching the calculated evaporation with the once observed in Basrah city for the period (1980-2009). In the second part of the study, the results obtained by ANFIS models are compared with results of linear regression model. The comparison reveals that the ANFIS models give better accuracy in estimating monthly pan evaporation than the linear regression model. The accuracy is improved about 5% in correlation coefficient (R) and determination coefficient (R2). The results proved that monthly pan evaporation could be successfully estimated through the use of ANFIS models.