http://www.cnr.it/ontology/cnr/individuo/prodotto/ID95082
AN MRAS BASED SPEED ESTIMATION METHOD WITH A LINEAR NEURON FOR HIGH PERFORMANCE INDUCTION MOTOR DRIVES AND ITS EXPERIMENTATION (Contributo in atti di convegno)
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- Label
- AN MRAS BASED SPEED ESTIMATION METHOD WITH A LINEAR NEURON FOR HIGH PERFORMANCE INDUCTION MOTOR DRIVES AND ITS EXPERIMENTATION (Contributo in atti di convegno) (literal)
- Anno
- 2003-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/IEMDC.2003.1211326 (literal)
- Alternative label
M. Cirrincione, M. Pucci (2003)
AN MRAS BASED SPEED ESTIMATION METHOD WITH A LINEAR NEURON FOR HIGH PERFORMANCE INDUCTION MOTOR DRIVES AND ITS EXPERIMENTATION
in IEEE, International Electric Machines and Drives Conference, Madison (Wisconsin)
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- M. Cirrincione, M. Pucci (literal)
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- ISSIA-CNR, uos di Palermo (literal)
- Titolo
- AN MRAS BASED SPEED ESTIMATION METHOD WITH A LINEAR NEURON FOR HIGH PERFORMANCE INDUCTION MOTOR DRIVES AND ITS EXPERIMENTATION (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- Abstract
- This paper presents a new speed observer for high application of an MRAS speed observer based on Artificial
??performance FOC (Field Oriented Control) and DTC (Direct
Torque Control) induction motor drives. It is an MRAS (Model
Reference Adaptive Systems) observer which employs a linear
ANN (Artificial Neural Network) for the estimation of both the
rotor speed and the flux-linkage. The training of the ANN based
adaptive model of the MRAS observer is performed on-line by
means of an OLS (Ordinary Least-Squares) algorithm. The
sensorless algorithm has been verified experimentally both in a
FOC and in a DTC control system. The experimental results neural network model, for stability reason, is employed not as show that the dynamical performances of the sensorless drive a simulator as in [10][11][12] but as a predictor. Finally this are definitely comparable to those obtainable with the
corresponding FOC and DTC drives with encoders in medium to high speed ranges. With regard to low speed ranges, this sensorless algorithm works properly down to a speed as much as 10rad/s. (literal)
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