DocumentCode :
3425005
Title :
Fractional order hold tuning using neural networks
Author :
Bárcena, R. ; de la Sen, M. ; Garrido, A.J.
Author_Institution :
Dept. de Electronica y Telecomunicaciones, Univ. del Pais Vasco, Bilbao, Spain
Volume :
5
fYear :
2001
fDate :
2001
Firstpage :
3872
Abstract :
A connectionist method for autotuning the free parameter of a Fractional order hold (FROH) in order to improve the stability properties of the resulting discrete-time zeros is proposed. Such a technique employs multilayer perceptrons to approximate the mapping between the sampling period/continuous-time parameters of the plant and the optimal values of the FROH parameter. The neural networks are designed to adapt on-line to changing system structures, parameter values and sampling periods. To achieve this objective, the network weighting coefficients are determined during an off-line training phase. In this training phase, the optimal values of the FROH parameter are obtained by applying the classical generalised root locus procedure. Simulation results are presented to illustrate the properties of the complete regression system
Keywords :
intelligent control; multilayer perceptrons; neurocontrollers; poles and zeros; robust control; FROH; connectionist method; discrete-time LQR; discrete-time zeros; intelligent control; multilayer perceptrons; multirate sampling control systems; neural networks; stability properties; time-varying plants; zeros; Control system synthesis; Control systems; Intelligent control; Multilayer perceptrons; Network synthesis; Neural networks; Optimal control; Sampling methods; Stability; Telecommunications;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, 2001. Proceedings of the 2001
Conference_Location :
Arlington, VA
ISSN :
0743-1619
Print_ISBN :
0-7803-6495-3
Type :
conf
DOI :
10.1109/ACC.2001.946244
Filename :
946244
Link To Document :
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