https://doi.org/10.1140/epjp/i2016-16292-2
Regular Article
Neural-network-based speed controller for induction motors using inverse dynamics model
1
Department of Computer Engineering, Faculty of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
2
Department of Power Electronic, Electronic Research Institute, Giza, Egypt
3
Department of Mathematics, Faculty Science, Taibah University, Medina, Saudi Arabia
4
Department of Mathematics, Faculty of Science, New Valley, Assiut University, Assiut, Egypt
* e-mail: hassanein_ms@hotmail.com
Received:
10
March
2016
Accepted:
26
July
2016
Published online:
30
August
2016
Artificial Neural Networks (ANNs) are excellent tools for controller design. ANNs have many advantages compared to traditional control methods. These advantages include simple architecture, training and generalization and distortion insensitivity to nonlinear approximations and nonexact input data. Induction motors have many excellent features, such as simple and rugged construction, high reliability, high robustness, low cost, minimum maintenance, high efficiency, and good self-starting capabilities. In this paper, we propose a neural-network-based inverse model for speed controllers for induction motors. Simulation results show that the ANNs have a high tracing capability.
© Società Italiana di Fisica and Springer-Verlag Berlin Heidelberg, 2016
