Neural network based self-tuning control of a switched reluctance motor drive to maximize torque per ampere

Online self-tuning control is essential as optimize the performance of a switched reluctance motor (SRM) drive in the presence of parameter variations. This paper introduces an advanced adaptive neural network (NN) based control scheme to maximize torque per ampere in the low speed region. The proposed control technique utilizes a heuristic search method to find the change in the optimal excitation instances in case of parameter variations. Based on the results of this heuristic search, the NN employs incremental learning to adapt its network weights. Computer simulations are performed to verify the applicability of the proposed algorithm. Experimental results are provided to demonstrate the working of the self-tuning controller.

[1]  J. H. Lang,et al.  Optimal-efficiency excitation of variable-reluctance motor drives , 1991 .

[2]  P. Tandon,et al.  Self-tuning control of a switched reluctance motor drive with shaft position sensor , 1996, IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting.

[3]  Mehrdad Ehsani,et al.  Direct control strategies based on sensing inductance in switched reluctance motors , 1993 .

[4]  Wendy Foslien,et al.  Incremental supervised learning: localized updates in nonlocalized networks , 1992, Defense, Security, and Sensing.

[5]  F. Blaabjerg,et al.  A new energy optimizing control strategy for switched reluctance motors , 1994, Proceedings of 1994 IEEE Applied Power Electronics Conference and Exposition - ASPEC'94.

[6]  Stephen Grossberg,et al.  Competitive Learning: From Interactive Activation to Adaptive Resonance , 1987, Cogn. Sci..