Title :
Neural network based self-tuning control of a switched reluctance motor drive to maximize torque per ampere
Author :
Rajarathnam, A.V. ; Fahimi, B. ; Ehsani, M.
Author_Institution :
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
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
Keywords :
adaptive control; control system analysis computing; electric machine analysis computing; machine control; machine testing; neurocontrollers; optimal control; reluctance motor drives; self-adjusting systems; torque control; SRM; computer simulation; heuristic search method; network weights adaptation; neural net-based self-tuning control; optimal excitation; parameter variations; switched reluctance motor drive; torque per ampere optimisation; Adaptive control; Adaptive systems; Computer simulation; Neural networks; Optimal control; Programmable control; Reluctance machines; Reluctance motors; Search methods; Torque control;
Conference_Titel :
Industry Applications Conference, 1997. Thirty-Second IAS Annual Meeting, IAS '97., Conference Record of the 1997 IEEE
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-4067-1
DOI :
10.1109/IAS.1997.643075
