Abstract
This paper presents a novel approach to adapting the weights of a CMAC neural network-based controllers for torque ripple reduction in switched reluctance motors. The proposed method modifies the conventional LMS algorithm using a varying learning rate which, for the present application, is defined as a function of the rotor angle of the motor under control. Simulation results demonstrate that developing CMAC network based adaptive controllers following this approach affords lower torque ripple with high power efficiency, whilst offering rapid learning convergence in system adaptation.
Original language | English |
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Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
Subtitle of host publication | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4); Washington, DC, USA; ; 3 June 1996 through 6 June 1996 |
Pages | 2078-2083 |
Number of pages | 6 |
Volume | 4 |
Publication status | Published - 1996 |
Event | 1996 IEEE International Conference on Neural Networks - Washington, DC, USA Duration: 3 Jun 1996 → 6 Jun 1996 |
Conference
Conference | 1996 IEEE International Conference on Neural Networks |
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City | Washington, DC, USA |
Period | 3/06/96 → 6/06/96 |