Title :
A neural network adaptive observer for field oriented control
Author :
Theocharis, J. ; Petridis, V.
Author_Institution :
Dept. of Electr. Eng., Aristotelian Univ. of Thessaloniki, Greece
Abstract :
A neural based adaptive observer is proposed for field oriented asynchronous machine control systems. It comprises two artificial neural networks (ANN) which are trained to learn the rotor flux and stator voltage dynamics respectively. After training, the ANN adjusts the slip signal command, thus providing decoupling of the flux terms. The ANN are subject to online training performed on the basis of voltage errors. Simulation results show that the proposed observer behaves very satisfactorily under rotor time constant changing conditions
Keywords :
State estimation; adaptive control; asynchronous machines; control system synthesis; digital control; learning (artificial intelligence); machine control; neural nets; rotors; slip (asynchronous machines); state estimation; stators; asynchronous machines; control system synthesis; decoupling; field oriented control; machine control; neural network adaptive observer; rotor flux; slip; state estimation; stator voltage dynamics; time constant; training; Adaptive control; Adaptive systems; Artificial neural networks; Multi-layer neural network; Neural networks; Neurons; Predictive models; Programmable control; Stators; Voltage;
Conference_Titel :
Industrial Electronics, 1992., Proceedings of the IEEE International Symposium on
Conference_Location :
Xian
Print_ISBN :
0-7803-0042-4
DOI :
10.1109/ISIE.1992.279581
