Title

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

Abstract

An example method for controlling an AC electrical machine can include providing a PWM converter operably connected between an electrical power source and the AC electrical machine and providing a neural network vector control system operably connected to the PWM converter. The control system can include a current-loop neural network configured to receive a plurality of inputs. The current-loop neural network can be configured to optimize the compensating dq-control voltage. The inputs can be d- and q-axis currents, d- and q-axis error signals, predicted d- and q-axis current signals, and a feedback compensating dq-control voltage. The d- and q-axis error signals can be a difference between the d- and q-axis currents and reference d- and q-axis currents, respectively. The method can further include outputting a compensating dq-control voltage from the current-loop neural network and controlling the PWM converter using the compensating dq-control voltage.

Department(s)

Electrical and Computer Engineering

Research Center/Lab(s)

Center for High Performance Computing Research

Patent Application Number

US 14/451,768

Patent Number

US20150039545 A1

Document Type

Patent

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2015 City University of London and Missouri University Of Science And Technology, All rights reserved.

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