DocumentCode :
1087315
Title :
Sensorless Control of Induction Machines by a New Neural Algorithm: The TLS EXIN Neuron
Author :
Cirrincione, Maurizio ; Pucci, Marcello ; Cirrincione, Giansalvo ; Capolino, Gerard-Andre
Author_Institution :
Sect. of Palermo, ISSIA-CNR, Palermo
Volume :
54
Issue :
1
fYear :
2007
Firstpage :
127
Lastpage :
149
Abstract :
This paper proposes two speed observers for high-performance induction machine drives, both adopting an online adaptation law based on a new total least-squares (TLS) technique: the TLS EXIN neuron. The first is a model reference adaptive system (MRAS) observer with a neural adaptive integrator in the reference model and a neural adaptive model trained online by the TLS EXIN neuron. This observer, presented in a previous article of the authors, has been improved here in two aspects: first, the neural adaptive integrator has been modified to make its learning factor vary according to the reference speed of the drive, second, a neural adaptive model based on the modified Euler integration has been proposed to solve the discretization instability problem in field-weakening. The second observer is a new full-order adaptive one based on the state equations of the induction machine, where the speed is estimated by means of a TLS EXIN adaptation technique. Both these observers have been provided with an inverter nonlinearity compensation algorithm and with techniques for the online estimation of the stator resistance of the machine. Moreover, a thorough theoretical stability analysis has been developed for them both, with particular reference to the field-weakening region behavior for the TLS MRAS observer and to the regenerating mode at low speeds for the TLS adaptive observer. Both speed observers have been verified in numerical simulation and experimentally on a test setup, and have also been compared experimentally with the BPN MRAS observer, the classic adaptive observer and with an open-loop estimator. Results show that both proposed observers outperform all other three observers in every working condition, with the TLS adaptive observer resulting in a better performance than the TLS MRAS observer
Keywords :
angular velocity control; compensation; induction motor drives; learning (artificial intelligence); least squares approximations; machine vector control; model reference adaptive control systems; neurocontrollers; observers; stability; stators; TLS EXIN neuron; discretization instability problem; field-weakening region; high-performance induction motor drives; inverter nonlinearity compensation algorithm; learning factor; model reference adaptive system; modified Euler integration; neural adaptive integrator; neural algorithm; online adaptation law; online training; regenerating mode; sensorless vector control; speed observers; stability analysis; state equations; stator resistance; total least-squares technique; Adaptive systems; Drives; Induction machines; Inverters; Neurons; Nonlinear equations; Observers; Sensorless control; State estimation; Stators; Field oriented control; Luenberger observer; induction motor; model reference adaptive system (MRAS); neural networks; sensorless control; speed observers; total least-squares (TLS);
fLanguage :
English
Journal_Title :
Industrial Electronics, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0046
Type :
jour
DOI :
10.1109/TIE.2006.888774
Filename :
4084649
Link To Document :
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