ELM–based sensorless speed control of permanent magnet synchronous machine
This paper deals with Extreme Learning Machine (ELM) based sensorless speed estimation and speed control of Permanent Magnet Synchronous Machines (PMSMs). ELM, first proposed by G.B. Huang as a new class of learning algorithm for Single–Hidden Layer Feedforward Neural Networks (SLFNs), is extremely fast and accurate, and has better generalisation performance than the traditional gradient–based training methods. To implement Field–Oriented Control (FOC) in PMSMs, the stator magnetic field is always kept 90 degrees ahead of the rotor. This requires rotor position information all the time. This information is accurately obtained with an ELM–based observer without the position sensor for PMSMs, and hence, the cost of the system is reduced, while the problems associated with the sensors are minimised.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/50135871
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Supplemental Notes:
- Copyright © 2013 Inderscience Enterprises Ltd.
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Authors:
- Kumar, Vikas
- Gaur, Prerna
- Mittal, A P
- Singh, Bhim
- Publication Date: 2013
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References;
- Pagination: pp 190-204
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Serial:
- International Journal of Vehicle Autonomous Systems
- Volume: 11
- Issue Number: 2-3
- Publisher: Inderscience Enterprises Limited
- ISSN: 1471-0226
- Serial URL: http://www.inderscience.com/jhome.php?jcode=ijvas
Subject/Index Terms
- TRT Terms: Automated vehicle control; Backpropagation; Machine learning; Neural networks; Permanent magnets; Speed control; Synchronous motors
- Subject Areas: Design; Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment; I91: Vehicle Design and Safety;
Filing Info
- Accession Number: 01483523
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 11 2013 9:03AM