Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 812
- Type
- D - Journal article
- DOI
-
10.1109/TCYB.2019.2897653
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 3218
- Volume
- 50
- Issue
- 7
- ISSN
- 2168-2267
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A neural network vector controller for permanent-magnet synchronous motors (PMSM) to overcome the decoupling inaccuracy problem associated with conventional PI-based vector-control methods is presented in this output. The results demonstrate that our controller outperforms conventional vector controllers in both simulation and hardware implementations. The work resulted in the patent "Systems, Methods and Devices for Vector Control of Permanent Magnet Synchronous Machines using Artificial Neural Networks", United States Patent; Patent No.: US-9754204-B2; Date Sep. 5, 2017.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
