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Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results
- Source :
- IEEE Transactions on Cybernetics. 50:3218-3230
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- This paper focuses on current control in a permanent-magnet synchronous motor (PMSM). This paper has two main objectives: the first objective is to develop a neural-network (NN) vector controller to overcome the decoupling inaccuracy problem associated with the conventional proportional-integral-based vector-control methods. The NN is developed using the full dynamic equation of a PMSM, and trained to implement optimal control based on approximate dynamic programming. The second objective is to evaluate the robust and adaptive performance of the NN controller against that of the conventional standard vector controller under motor parameter variation and dynamic control conditions by: 1) simulating the behavior of a PMSM typically used in realistic electric vehicle applications and 2) building an experimental system for hardware validation as well as combined hardware and simulation evaluation. The results demonstrate that the NN controller outperforms conventional vector controllers in both simulation and hardware implementation.
- Subjects :
- QA75
Vector control
business.product_category
Artificial neural network
business.industry
Computer science
020208 electrical & electronic engineering
02 engineering and technology
Decoupling (cosmology)
Optimal control
Computer Science Applications
Human-Computer Interaction
Dynamic programming
Control and Systems Engineering
Control theory
Electric vehicle
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Electrical and Electronic Engineering
business
Synchronous motor
Software
Computer hardware
Decoupling (electronics)
Information Systems
Subjects
Details
- ISSN :
- 21682275 and 21682267
- Volume :
- 50
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Cybernetics
- Accession number :
- edsair.doi.dedup.....015fd6b641cb3dbd377e5174831f0870