Patent ID: 11876464
Assignee: THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ALABAMA
Field: Electrical machinery, apparatus, energy (Electrical engineering)
Classification: CPC H  G | IPC G  H

Claim 6:
7. The system of claim 6, wherein the controller neural network is a feedforward network comprising an input layer, multiple hidden layers and an output layer, wherein the controller neural network:
receives a plurality of inputs at the input layer, wherein the plurality of inputs include at least reference d and q-axis stator currents from the flux-weakening and MTPA neural network, d and q-axis actual stator currents, and other signals that can be generated from the reference d and q-axis stator currents and actual d and q-axis stator currents, wherein the other signals include error signals between the reference and actual stator currents and integrals of the error signals;
outputs a compensating dq-control voltage at the output layer, wherein the controller neural network is configured to optimize the compensating dq-control voltage based on the plurality of inputs; and
controls the SVPWM converter using the compensating dq-control voltage,
wherein the controller neural network is trained as a recurrent network to minimize a cost function of the ADP algorithm using a forward accumulation through time (“FATT”) algorithm,
wherein the cost function can be used to measure how close the actual d and q-axis stator currents match the reference d and q-axis stator currents,
wherein the controller neural network is trained offline at times when the IPM AC electrical machine is not operating.