scholarly journals Adaptive Control of the Electrical Drives with the Elastic Coupling using Kalman Filter

10.5772/6507 ◽  
2009 ◽  
Author(s):  
Krzysztof Szabat ◽  
Teresa Orlowska-Kowalsk
Keyword(s):  
Adaptive Control ◽  
Kalman Filter ◽  
Elastic Coupling ◽  
Electrical Drives

Synchronization of a Network of Chaotic Neurons Using Adaptive Control in Noisy Environment

1998 ◽  
Vol 08 (09) ◽  
pp. 1821-1830 ◽  
Author(s):  
Bernard Cazelles
Keyword(s):  
Adaptive Control ◽  
Kalman Filter ◽  
Chaotic Systems ◽  
Control Mechanism ◽  
Control Method ◽  
Rhythmic Activity ◽  
Noisy Environment ◽  
Network Switch ◽  
Clustering Behavior

The synchronization of chaotic systems have received an increasing interest in the last few years. In an attempt to understand some of the possible mechanisms of synchronization of neurons in a noisy environment, the present study extends a control method which is based on the Kalman filter. This adaptive control mechanism is able to modulate the frequency and the clustering behavior of a network of neurons and can thus make the network switch dynamically to different rhythmic activity, leading to different coding possiblity.

scholarly journals An Extender Kalman Filter-based Induction Machines Faults Detection

2016 ◽  
Vol 6 (2) ◽  
pp. 535 ◽  
Author(s):  
Abdelghani Chahmi ◽  
Mokhtar Bendjebbar ◽  
Bertrand Raison ◽  
Mohamed Benbouzid
Keyword(s):  
Experimental Data ◽  
Kalman Filter ◽  
Induction Machines ◽  
Simulation Tools ◽  
Electrical Drives ◽  
Efficient Simulation ◽  
Processing Techniques ◽  
Detection And Localization ◽  
Signal Processing Techniques ◽  
Diagnosis Approach

This paper deals with the detection and localization of electrical drives faults, especially those containing induction machines. First, the context of the study is presented and an Extended Kalman Filter is described for induction machines fault detection. Then the modeling procedure under faulty conditions is shown, and the machine diagnosis methods are developed. The proposed diagnosis approach requires only little experimental data, and more importantly it provides efficient simulation tools that allow characterizing faulty behavior.Fault detection uses signal processing techniques in known operating phases (fixed speed), considering and locating malfunctions.

scholarly journals An Extender Kalman Filter-based Induction Machines Faults Detection

2016 ◽  
Vol 6 (2) ◽  
pp. 535
Author(s):  
Abdelghani Chahmi ◽  
Mokhtar Bendjebbar ◽  
Bertrand Raison ◽  
Mohamed Benbouzid
Keyword(s):  
Experimental Data ◽  
Kalman Filter ◽  
Induction Machines ◽  
Simulation Tools ◽  
Electrical Drives ◽  
Efficient Simulation ◽  
Processing Techniques ◽  
Detection And Localization ◽  
Signal Processing Techniques ◽  
Diagnosis Approach

This paper deals with the detection and localization of electrical drives faults, especially those containing induction machines. First, the context of the study is presented and an Extended Kalman Filter is described for induction machines fault detection. Then the modeling procedure under faulty conditions is shown, and the machine diagnosis methods are developed. The proposed diagnosis approach requires only little experimental data, and more importantly it provides efficient simulation tools that allow characterizing faulty behavior.Fault detection uses signal processing techniques in known operating phases (fixed speed), considering and locating malfunctions.

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