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 7:
8. The system of claim 6, wherein the parameter estimator neural network is a feedforward network comprising an input layer, multiple hidden layers and an output layer, wherein said parameter estimator neural network:
receives a plurality of inputs at the input layer, wherein the plurality of inputs include at least d and q-axis actual stator currents and estimated d and q-axis stator currents from an adjustable IPM motor model and other signals that can be generated from the actual and estimated d and q-axis stator currents, wherein the other signals include error signals between the estimated and actual stator currents and integrals of the error signals; and
outputs estimated motor parameters Ld, Lq, ωe, and ψpm at the output layer, wherein the parameter estimator neural network together with adjustable IPM motor model is configured to optimize the IPM motor parameter estimation based on the plurality of inputs;
wherein the parameter estimator 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 estimated d and q-axis stator currents from the adjustable IPM motor model match the actual d and q-axis stator currents,
wherein the adjustable IPM motor model receives estimated motor parameters Ld, Lq, ωe, and ψpm from the parameter estimator neural network as well as the actual d and q-axis stator voltages of the IPM motor, and outputs the estimated d and q-axis stator currents,
wherein the parameter estimator neural network is trained offline at times when the IPM AC electrical machine is not operating,
wherein from the estimated motor parameters, an electrical rotational speed of the IPM AC electrical machine and a rotor position of the IPM AC electrical machine can be obtained,
wherein the estimated electrical rotational speed and the estimated rotor position can enable sensorless control for an IPM AC electrical machine.