1 / 1

Torque Ripple Minimization in a Switched Reluctance Drive by Neuro-fuzzy Compensation

Torque Ripple Minimization in a Switched Reluctance Drive by Neuro-fuzzy Compensation. Paulo J. C. Branco Joaquim A. Dente Mechatronics Laboratory Instituto Superior Técnico (IST) Portugal. Luís O. A. P. Henriques Luís G. B. Rolim Walter I. Suemitsu Federal University of Rio de Janeiro

trevor-curtis
Télécharger la présentation

Torque Ripple Minimization in a Switched Reluctance Drive by Neuro-fuzzy Compensation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Torque Ripple Minimization in a Switched Reluctance Drive by Neuro-fuzzy Compensation Paulo J. C. Branco Joaquim A. Dente Mechatronics Laboratory Instituto Superior Técnico (IST) Portugal Luís O. A. P. Henriques Luís G. B. Rolim Walter I. Suemitsu Federal University of Rio de Janeiro COPPE Brasil • Advantages • High efficiency • Low manufacturing cost • Fault tolerant • Reliable • Easy to repair • Disadvantages • Torque ripple • Nonlinear model • Applications • Traction • Heavy-duty applications • Home appliances Switched Reluctance Motor - Complete Simulated System Diagram of proposed SR torque ripple compensation scheme Torque signal without compensation (constant current and velocity) Torque signal with compensation (constant current and velocity) after 10 learning iterations Compensated Torque Harmonics Triangular fuzzy sets Compensated Torque Harmonics Gaussian fuzzy sets CompensatedCurrent pulses after 10 learning iterations Conclusions Support • Neuro-fuzzy compensating mechanism to ripple reduction was investigated • Compensating signal added in current waveform was used to minimize the torque ripple • Bell shape function produces better ripple reduction in all harmonic content • Future investigation: • Application of this concept in an experimental drive • Incorporate another signal to be trained Compensated Torque Harmonics Bell fuzzy sets

More Related