Systems, methods and devices for vector control of permanent magnet synchronous machines using artificial neural networks

A system for controlling an AC electrical machine, comprising:

• a pulse-width modulated (“PWM”) converter operably connected between an electrical power source and the AC electrical machine;
• 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, said current-loop neural network configured to;
• receive a plurality of inputs at the current-loop neural network, wherein the plurality of inputs comprise:
• a d-axis current, isd, and a q-axis current, isq,
• a d-axis error signal and a q-axis error signal, wherein the d-axis error signal comprises a difference between isd and a reference d-axis current, isd*, and the q-axis error signal comprises a difference between isq and a reference q-axis current, isq*,
• 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;
• and
• output 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 wherein neural network vector control system controls 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.