DocumentCode :
2772870
Title :
Fast predictive inverse neurocontrol: Comparative simulation and experiment
Author :
Zmeu, K.V. ; Notkin, B.S. ; Dyachenko, P.A. ; Kovalev, V.A.
Author_Institution :
Ind. Eng. Dept., Far Eastern Fed. Univ., Vladivostok, Russia
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
7
Abstract :
There has been proposed a new approach to a neurocontrol synthesis under conditions of uncertainty. It does not directly use an optimization procedure. In terms of a synthesis technique, the proposed solution is close to inverse neurocontrol, but regarding its functions, the system has properties of a fast predictive control. There have been presented the comparison of the proposed approach with classical and modern proportional-integral-derivative (PID) systems that were obtained based on a numerical simulation and an actual control of complex plants.
Keywords :
control system synthesis; neurocontrollers; numerical analysis; optimisation; predictive control; three-term control; PID; complex plants; fast predictive inverse neurocontrol; neurocontrol synthesis; numerical simulation; optimization procedure; proportional-integral-derivative systems; synthesis technique; Artificial neural networks; Mathematical model; Neurocontrollers; Predictive control; Predictive models; Training; Vectors; PID-control; inverse control; neural network; neurocontrol; predictive control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
ISSN :
2161-4393
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
Type :
conf
DOI :
10.1109/IJCNN.2012.6252567
Filename :
6252567
Link To Document :
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