Abstract
The application of an associative memory neural network to the problem of torque ripple minimisation in a switched reluctance motor is presented. Conventional techniques for torque linearisation and decoupling are reviewed, after which the application of neural techniques to the problem is described. An instrumented test rig based around a 4 kW IGBT converter and a four phase switched reluctance motor has been constructed. Results obtained experimentally and by simulation demonstrate the effectiveness of the approach. The neural network has been implemented using both digital signal processor and field programmable gate array technologies.
| Original language | English |
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| Title of host publication | Plenary Session, Emerging Technologies, and Factory Automation |
| Pages | 200-206 |
| Number of pages | 7 |
| Volume | 1 |
| Publication status | Published - 1993 |
| Event | Proceedings of the 19th International Conference on Industrial Electronics, Control and Instrumentation - Maui, Hawaii, USA Duration: 15 Nov 1993 → 18 Nov 1993 |
Conference
| Conference | Proceedings of the 19th International Conference on Industrial Electronics, Control and Instrumentation |
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| City | Maui, Hawaii, USA |
| Period | 15/11/93 → 18/11/93 |