Modeling and Control of DFIG Through Back-to-Back Five Levels Converters Based on Neuro-Fuzzy Controller

2015 ◽  
Vol 26 (5) ◽  
pp. 506-520 ◽  
Author(s):  
Abdelhak Dida ◽  
Djilani Benattous
Keyword(s):  
Fuzzy Controller ◽  
Modeling And Control ◽  
Neuro Fuzzy ◽  
And Control

Fuzzy Control of a Pneumatic Muscle Driven Parallel Robot for Ankle Rehabilitation

2009 ◽  
Author(s):  
Prashant K. Jamwal ◽  
Shane Xie ◽  
Jack Farrant
Keyword(s):  
Fuzzy Control ◽  
Degrees Of Freedom ◽  
Fuzzy Controller ◽  
Parallel Robot ◽  
Modeling And Control ◽  
Pneumatic Muscle ◽  
Linear Behavior ◽  
Improved Performance ◽  
Unpredictable Environment ◽  
And Control

A new wearable parallel robot has been designed and constructed for ankle joint rehabilitation treatments. The robot employs four pneumatic muscle actuators (PMA) together with cables to achieve three rotational degrees of freedom (dof) of its end platform. Parallel topology of the robot, unpredictable environment along with the time varying and non-linear behavior of actuators impose modeling and control challenges which are difficult to comprehend. In this paper an optimal fuzzy dynamic model of the pneumatic muscle has been developed to accurately predict the muscle behavior. The model is capable of mapping the complex relationship in length, force and pressure of the PMA with higher accuracy. This model has been further used to develop a fuzzy control scheme for the ankle robot. Experimental results are obtained to study and model the simultaneous actuation of all the actuators. Comparison with the previous dynamic modeling and control schemes demonstrates an improved performance of the proposed fuzzy controller.

scholarly journals Flutter Suppression of an Airfoil Using Neuro-Fuzzy Control

2021 ◽  
Author(s):  
Chun Meng
Keyword(s):  
Fuzzy Controller ◽  
Equations Of Motion ◽  
Fuzzy Rules ◽  
Matlab Simulation ◽  
Modeling And Analysis ◽  
Flutter Suppression ◽  
Flutter Speed ◽  
Neuro Fuzzy ◽  
Excited Vibration ◽  
And Control

Flutter, a self-excited vibration of wings and control surfaces, can lead to catastrophic failure of aircraft structures. Classical methods have been applied successfully for flutter suppression and for increasing the flutter critical speed. With the demand of higher speed and more flexible aircraft, more advanced active flutter control techniques are required. In this study, a neuro-fuzzy methodology for flutter suppression of a two dimensional airfoil is explored. A MATLAB simulation environment is used for the modeling and analysis. The airfoil model is simulated according to a set of aeroelastic equations of motion. A neuro-fuzzy controller, called NEFCON, is then embedded in the airfoil model for increasing the flutter speed. NEFCON learns from the motion of the airfoil and automatically produces fuzzy rules. The simulation results show that these fuzzy rules can successfully increase the critical flutter speed. The performance of the fuzzy rules is tested with differential airfoil parameters.

The local paradigm for modeling and control: from neuro-fuzzy to lazy learning

2001 ◽  
Vol 121 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Gianluca Bontempi ◽  
Hugues Bersini ◽  
Mauro Birattari
Keyword(s):  
Lazy Learning ◽  
Modeling And Control ◽  
Neuro Fuzzy ◽  
And Control

scholarly journals Neuro-fuzzy modeling and control

1995 ◽  
Vol 83 (3) ◽  
pp. 378-406 ◽  
Author(s):  
J.-S.R. Jang ◽  
Chuen-Tsai Sun
Keyword(s):  
Fuzzy Modeling ◽  
Modeling And Control ◽  
Neuro Fuzzy ◽  
And Control
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