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.
Recommended Citation
S. Li et al., "Systems, Methods and Devices for Vector Control of Permanent Magnet Synchronous Machines using Artificial Neural Networks," U.S. Patents, Feb 2015.
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Patent Application Number
US14/451,768
Patent Number
US20150039545A1
Document Type
Patent
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2015 City University of London and Missouri University of Science And Technology, All rights reserved.
Publication Date
05 Feb 2015
Included in
Electrical and Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons
