Neural network modeling of torque estimation and d-q transformation for induction machine
This paper presents a neural network approach in modeling of torque estimation and Parks d-q transformation for an open-loop induction machine. The nonlinear approximation capability of neural networks makes it possible to map the Parks d-q transformation and torque estimation in an induction motor, which would otherwise require extensive complex calculations. The neural network simulation results will be compared to those of directly DSP calculated transformation and estimation. The results show improved performance with the neural network approach. We conclude that machine systems transformations and estimations can take advantage of the neural network technology for improved performance and cost reduction in the long run. (C) 2004 Elsevier Ltd. All rights reserved.
neural network torque estimation feed forward back propagation weights bias parks transformation