Neural Rotor Time Constant Estimation for Indirect Vector Controlled Induction Motor Drives
In this paper a back-propagation neural networks is employed for indirect field oriented control drive systems to identify the rotor time constant using measurements of the stator voltages and currents and the rotor speed of the induction motor. The neural network model outputs are compared to the desired values, and the total error between the desired and the estimated state variable is then back-propagated to adjust the weights (rotor time constant) of the neural model, so that the output of this model will coincide with the desired value. The back-propagation mechanism is easy to derive and the estimated rotor time constant tracks precisely the actual motor rotor time constant. The theoretical analysis as well as the simulation results to verify the effectiveness of the new approach is described in this paper.
Keywordsartificial neural networks indirect field oriented control induction motor drives rotor time constant
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