ELM-based sensorless speed control of permanent magnet synchronous machine
by Vikas Kumar; Prerna Gaur; A.P. Mittal; Bhim Singh
International Journal of Vehicle Autonomous Systems (IJVAS), Vol. 11, No. 2/3, 2013

Abstract: 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.

Online publication date: Tue, 30-Sep-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Autonomous Systems (IJVAS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com