Authors:
Tien-Chi Chen
and
Wei-Chung Wang
Affiliation:
Kun Shan University, Taiwan
Keyword(s):
Induction Motor, Encoder, Speed Sensorless Control, RFNN, Fuzzy Neural Network Speed Estimation, Steepest Descent Algorithm, Back-propagation Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Enterprise Information Systems
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Multi-Agent Intelligent Systems and Applications
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The field-oriented control (FOC) of induction motor has high static and dynamic performance. In order to achieve the speed loop feedback control, precise rotor speed information is important for induction motor control. In the past, encoder was widely used to obtain the speed information of induction motor. However, speed sensor would increase the cost of entire system and reduce the system reliability. In addition, for some special applications such as very high speed motor drives, some difficulties are encountered in mounting these speed sensors. The speed sensorless control would overcome these problems. This paper proposes a fuzzy neural network speed estimation for induction motor speed sensorless control. The speed estimation is based on the deduction of rotor flux and estimated rotor flux, which is calculated by fuzzy neural network. The fuzzy neural network includes a four-layer network. The steepest descent algorithm and back-propagation algorithm are used to adjust the para
meters of fuzzy neural network in order to minimize the error between the rotor flux and the estimated rotor flux, which is implied to enable precise estimation of the rotor speed.
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