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Real-Time Implementation of a Discrete Reduced Order Neural Observer: Linear Induction Motors Application


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Abstract

This paper deals with a discrete-time neural observer for flux estimation of a linear induction motor, which is based on a Recurrent High Order Neural Network (RHONN). The RHONN weights are tuned on-line, with no off-line learning phase required, using an extended Kalman filter based algorithm. The observer stability and boundedness of the state estimation and NN weights are proven using the Lyapunov approach. Knowledge of the system model is not required. The applicability of this observer is illustrated by real-time implementation for flux estimation of a linear induction motor benchmark.


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Pages

Total Pages: 8
Pages: 491-498

DOI
10.1080/10798587.2015.1097014


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Published

Volume: 22
Issue: 3
Year: 2015

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References

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JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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