Abstract
This paper considers the implementation of high performance control for switched reluctance motors (SRMs) and presents a novel approach to the accurate on-line modeling of an SRM. An adaptive B-spline neural network is used to learn the non-linear flux-linkage, torque, incremental inductance, and back EMF characteristics of an SRM. The training of the B-spline neural network is accomplished on-line and in real-time. The system does not require a priori knowledge of the machine's electromagnetic characteristics. The potential of the method is demonstrated in simulation and experimentally using a 550W 8/6 4-phase SRM. © 2004 Heriot-Watt University, UK.
Original language | English |
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Pages (from-to) | 763-768 |
Number of pages | 6 |
Journal | IEE Conference Publication |
Volume | 2 |
Publication status | Published - 2004 |
Event | Second International Conference on Power Electronics, Machines and Drives, PEMD 2004 - Edinburgh, United Kingdom Duration: 31 Mar 2004 → 2 Apr 2004 |
Keywords
- B-spline neural work
- On-line modeling
- Switched reluctance motor