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On-line Trained Neural Speed Controller with Variable Weight Update Period for Direct-Torque-Controlled AC Drive
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| Author(s) |
Lech M. Grzesiak, Vincent Meganck, Jakub Sobolewski, Bartlomiej Ufnalski |
| Abstract |
The paper investigates further improvements of
an adaptive ANN (Artificial Neural Network)-based speed
controller employed in a DTC-SVM (Direct Torque
Controlled - Space Vector Modulated) drive. An on-line
trained ANN serves as a speed controller and does not need
a process model to predict future performance. In
comparison to the previously published solution, autoadjusting
ability has been added to the controller. The
recurrent feedback inside the neural controller has been
also introduced. Adaptive behaviour manifests in robustness
to moment of inertia variation greater than 10 times. This
feature is achieved by the learning algorithm running
during system operation. Mentioned variable update period
refers to one of the parameters connected with learning
algorithm, namely frequency of calling backpropagation
procedure (weights update procedure). Proposed control
algorithm has been tested in simulation and verified
experimentally. The behaviour of the drive has been
compared to the one with previously proposed ANN-based
speed controller with fixed settings of training algorithm. |
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| Filename: | T5-112.pdf |
| Filesize: | 2.58 MB |
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| Type |
Members Only |
| Date |
Last modified 2007-03-13 by System |
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