An MRAS-based sensorless high-performance induction motor drive with a predictive adaptive model
Cirrincione, M.
Pucci, M.
Inst. di Studi sui Sistemi Intelligenti per l'Automazione, Palermo, Italy;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: April 2005
Volume: 52,
Issue: 2
On page(s): 532- 551
ISSN: 0278-0046
INSPEC Accession Number: 8351114
Digital Object Identifier: 10.1109/TIE.2005.844247
Current Version Published: 2005-04-04
Abstract
This paper presents a new model reference adaptive system (MRAS) speed observer for high-performance field-oriented control induction motor drives based on adaptive linear neural networks. It is an evolution and an improvement of an MRAS observer presented in the literature. This new MRAS speed observer uses the current model as an adaptive model discretized with the modified Euler integration method. A linear neural network has been then designed and trained online by means of an ordinary least-squares (OLS) algorithm, differently from that in the literature which employs a nonlinear backpropagation network (BPN) algorithm. Moreover, the neural adaptive model is employed here in prediction mode, and not in simulation mode, as is usually the case in the literature, with a consequent quicker convergence of the speed estimation, no need of filtering the estimated speed, higher bandwidth of the speed loop, lower estimation errors both in transient and steady-state operation, better behavior in zero-speed operation at no load, and stable behavior in field weakening. A theoretical analysis of some stability issues of the proposed observer has also been developed. The OLS MRAS observer has been verified in numerical simulation and experimentally, and in comparison with the BPN MRAS one presented in the literature.
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