Patent Document ID: 9754204
Application ID: 14451768
Patent Flag: 1

Claim One:
1. A method for controlling an AC electrical machine, comprising: providing a pulse-width modulated (“PWM”) converter operably connected between an electrical power source and the AC electrical machine; providing a neural network vector control system operably connected to the PWM converter, the neural network vector control system comprising a current-loop neural network and a speed-loop neural network, wherein at least one of the current-loop neural network and the speed-loop neural network is configured to implement a dynamic programming (“DP”) algorithm; receiving a plurality of inputs at the current-loop neural network, wherein the plurality of inputs comprise: a d-axis current, i sd , and a q-axis current, i sq , a d-axis error signal and a q-axis error signal, wherein the d-axis error signal comprises a difference between i sd and a reference d-axis current, i sd *, and the q-axis error signal comprises a difference between i sq and a reference q-axis current, i sq *, an integral of d-axis error signal and an integral of q-axis error signal; a predicted d-axis current signal and a predicted q-axis current signal; and a feedback compensating dq-control voltage; outputting a compensating dq-control voltage from the current-loop neural network, wherein the current-loop neural network is configured to optimize the compensating dq-control voltage based on the plurality of inputs; and controlling the PWM converter using the compensating dq-control voltage, wherein at least one of the current-loop neural network and the speed-loop neural network is trained to minimize a cost function of the DP algorithm using a backpropagation through time (“BPTT”) algorithm by unrolling a trajectory of the neural network vector control system from an initial state and training the current-loop neural network or the speed-loop neural network based on the cost function of the DP algorithm and the BPTT algorithm.