scholarly journals Robust ANFIS Vector Control of Induction Motor Drive for High-Performance Speed Control Supplied by a Photovoltaic Generator

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Vector Control ◽  
Induction Motor ◽  
High Performance ◽  
Fuzzy Inference ◽  
Speed Control ◽  
Inference System ◽  
Proportional Integral ◽  
Rotor Resistance ◽  
Resistance Variation ◽  
Drive Systems

This paper presents the vector control of Induction Motor (IM) supplied by a photovoltaic generatorwhich is controlled by an adaptive Proportional-Integral (PI) speed controller. The proposed solution is used toovercome the induction motor rotor resistance variation problem, which can affect negatively the performanceof the speed control. To overcome the rotor resistance variation, an adaptive Proportional-Integral controller isdeveloped with gains adaptation based on Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to guaranteea high performances of electric drive systems against the parametric variations. The proposed control algorithmis tested by Matlab-Simulink. Analysis of the obtained results shows the characteristic robustness to disturbancesof the load torque and to rotor resistance variation compared to the classical PI control and Model ReferenceAdaptive System (MRAS) rotor resistance observers.

Neuro-Fuzzy-Based Auto-Tuning Proportional Integral Controller for Induction Motor Drive

2015 ◽  
Vol 14 (03) ◽  
pp. 1550016 ◽  
Author(s):  
Moulay Rachid Douiri ◽  
Ouissam Belghazi ◽  
Mohamed Cherkaoui
Keyword(s):  
Induction Motor ◽  
High Performance ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Proportional Integral ◽  
Neuro Fuzzy ◽  
Integral Controller ◽  
Proportional Integral Controller ◽  
Auto Tuning ◽  
Drive Systems

This study presents a novel neuro-fuzzy (NF)-based auto-tuning proportional integral controller (NFATPI) for accurate speed control, and to ensure optimal drive performances of the indirect field controlled induction motor drive, under system disturbances and uncertainties. The training mechanism of the proposed NF have been developed and illustrated through mathematical formulations. Then, the NF parameters have been updated on-line using a suitable training algorithm. The learning rates of the NF are derived on the basis of the discrete Lyapunov function is also illustrated, in order to confirm the stability and the performance of prediction of the proposed NFATPI. The simulation results confirm the effectiveness of the strategy NFATPI as a robust controller for high performance industrial motor drive systems.

Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions

2014 ◽  
Vol 9 (12) ◽  
pp. 1226-1234
Author(s):  
Kadir Temizel ◽  
Mehmet Odabas ◽  
Nurettin Senyer ◽  
Gokhan Kayhan ◽  
Sreekala Bajwa ◽  
...  
Keyword(s):  
High Performance ◽  
Fuzzy Inference ◽  
High Water ◽  
Stress Conditions ◽  
Ann Model ◽  
Inference System ◽  
Leaf Reflectance ◽  
Neuro Fuzzy ◽  
Salinity Levels ◽  
Artificial Neural Network Ann

AbstractLack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John’s wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that the constructed ANN model exhibited a high performance than multiple regression and ANFIS in estimating leaf reflectance accurately.

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