Abstract:
This paper describes how an artificial neural network (ANN) can be employed to improve a model reference adaptive system closed loop flux observer (MRAS-CLFO) used for sp...Show MoreMetadata
Abstract:
This paper describes how an artificial neural network (ANN) can be employed to improve a model reference adaptive system closed loop flux observer (MRAS-CLFO) used for speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the stator resistance which enable the MRAS-CLFO models to work more accurately. The overall effect is an improvement in speed estimation and experimental results are presented to verify this.
Published in: 2000 Eighth International Conference on Power Electronics and Variable Speed Drives (IEE Conf. Publ. No. 475)
Date of Conference: 18-19 September 2000
Date Added to IEEE Xplore: 06 August 2002
Print ISBN:0-85296-729-2
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- Index Terms
- Neural Network ,
- Artificial Neural Network ,
- Induction Motor ,
- Stator Resistance ,
- Resistance Change ,
- Speed Estimation ,
- Flux Observer ,
- Running ,
- Hidden Layer ,
- Rotational Speed ,
- Neurons In Layer ,
- Cyclic Loading ,
- PI Controller ,
- Error Vector ,
- Back Electromotive Force ,
- Ambient Temperature Variation ,
- Rotor Flux ,
- Host PC
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- Index Terms
- Neural Network ,
- Artificial Neural Network ,
- Induction Motor ,
- Stator Resistance ,
- Resistance Change ,
- Speed Estimation ,
- Flux Observer ,
- Running ,
- Hidden Layer ,
- Rotational Speed ,
- Neurons In Layer ,
- Cyclic Loading ,
- PI Controller ,
- Error Vector ,
- Back Electromotive Force ,
- Ambient Temperature Variation ,
- Rotor Flux ,
- Host PC
