Proposition of an offline learning current modulation for torque-ripple reduction in switched reluctance motors: design and experimental evaluation

A new offline current modulation using a neuro-fuzzy compensation scheme for torque-ripple reduction in switched reluctance motors is presented. The main advantage of the proposed technique is that the torque signal is unnecessary. The compensating signal is learned prior to normal operation in a self-commissioning run, capturing the necessary current shape to reduce the torque ripple. Simulation results verify first the effects of speed and then load changes on the compensator performance. Implementation of the proposed technique in a laboratory prototype shows the feasibility and accuracy of the respective offline scheme.

[1]  Barry W. Williams,et al.  Switched reluctance motor control via fuzzy adaptive systems , 1995 .

[2]  P. J. Costa Branco,et al.  Two Automatic On-line New Schemes to Compensate the Torque Ripple of Switched Reluctance Machines: With and Without Torque Signal Measurement , 2002 .

[3]  S Bologani,et al.  FUZZY LOGIC CONTROL FOR A SWITCHED RELUCTANCE MOTOR DRIVE , 1996 .

[4]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[5]  P. J. Costa Branco,et al.  Automatic learning of pulse current shape for torque ripple minimisation in switched reluctance machines , 2001, 2001 European Control Conference (ECC).

[6]  I. Husain,et al.  Torque ripple minimization in switched reluctance machines over a wide speed range , 1997, IAS '97. Conference Record of the 1997 IEEE Industry Applications Conference Thirty-Second IAS Annual Meeting.

[7]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[8]  Adrian David Cheok,et al.  Use of fuzzy logic for modeling, estimation, and prediction in switched reluctance motor drives , 1999, IEEE Trans. Ind. Electron..

[9]  J.M.D. Murphy,et al.  Torque ripple minimization in switched reluctance drives using self-learning techniques , 1991, Proceedings IECON '91: 1991 International Conference on Industrial Electronics, Control and Instrumentation.

[10]  F. Soares,et al.  Simulation of a 6/4 switched reluctance motor based on Matlab/Simulink environment , 2001 .

[11]  J.M.D. Murphy,et al.  Neural network based torque ripple minimisation in a switched reluctance motor , 1994, Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics.

[12]  Iqbal Husain,et al.  Torque ripple minimization in switched reluctance motors using adaptive fuzzy control , 1997, IAS '97. Conference Record of the 1997 IEEE Industry Applications Conference Thirty-Second IAS Annual Meeting.