Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : ANFIS


Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter

Safa S. Olwie; Abdulrahim T. Humod; Fadhil A. Hasan

Engineering and Technology Journal, 2023, Volume 41, Issue 2, Pages 1-15
DOI: 10.30684/etj.2022.132342.1115

Inductance – Capacitance – Inductance (LCL) filter is a very attractive candidate for renewable energy system applications due to its high efficiency. High attenuation of the switching frequency harmonics, small size, low fee, and improving the overall harmonic distortion (THD). This paper presents how voltage is affected by increased loads or voltage sag. Therefore it is necessary to control it with certain controllers. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as an intelligent controller, the voltage constraint as training data for ANFIS obtained from PI. The filter works in a good connection between the inverter and the grid and rewords unwanted harmonics from using the inverter. The mathematical models for the LCL filter are investigated. The proposed approach shows more effective results than previous performance for voltage controlling and harmonic reduction. It gives overshoot (0.5%), steady state error (0.005), settling time (0.03 sec), rise time  (0.005 sec), and improving THD 8.67% to 2.33%  by comparing these results of ANFIS respectively with the results of PI which gave(3%),(0.01),(0.2sec)and( 0.02sec).

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.

Speed Reference Tracking for Separately Excited DC Motor Based ANFIS and Hysteresis Current Control Techniques

Omar T. Mahmood

Engineering and Technology Journal, 2018, Volume 36, Issue 6A, Pages 680-690
DOI: 10.30684/etj.36.6A.13

In this work, a closed loop control system has designed to control the speed of separately excited direct current motor (SEDCM) using Fuzzy (Mamdani and Sugeno) and an Adaptive Neuro - Fuzzy techniques (ANFIS). The action of these control techniques is to produce the reference armature current that has fed to the hysterics current controller (HCC) that produces the required gating signal to a Buck chopper .Different load conditions has been applied to the motor to obtain many mode of operation, the speed held constant at the required references values using both fuzzy (Mamdani-type and Sugeno-type) and ANFIS techniques. The results has collected and compered with a classical PID controller using MATLAB/Simulink. Step response for the speed has drawn and the control parameters for this response have evaluated. According to the results, the Mamdani fuzzy controller technique is better than as compared with the other controllers. There are many applications for this plant such as production process that need to fill or Packaging any product or used in the autopilot channels. The new goal for this proposed system is to get robust speed controllers that track the speed at any mode of operation using three artificial intelligent techniques.

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.