Detail publikace
Adaptive Controller with Identification Based on Neural Network for Systems with Rapid Sampling Rates
Originální název
Adaptive Controller with Identification Based on Neural Network for Systems with Rapid Sampling Rates
Anglický název
Adaptive Controller with Identification Based on Neural Network for Systems with Rapid Sampling Rates
Jazyk
en
Originální abstrakt
In this paper ability of three identification methods to parameter estimation of the dynamic plant with great ratio of its time constant to sampling periods is compared. We concentrate our attention on dealing with adverse effects that work on real-time identification of process, especially quantization. It is shown, that a neural network applied to on-line identification process produces more stable solution in the rapid sampling domain. Taking advantage of this result, we propose here an adaptive controller with a neural network as on-line estimator. Simple heuristic synthesis based on modified Ziegler-Nichols open loop method (Z-N 1) are discussed, that deals with bad-estimated model of a plant and gives numerically stable parameters of the PID discrete controller.
Anglický abstrakt
In this paper ability of three identification methods to parameter estimation of the dynamic plant with great ratio of its time constant to sampling periods is compared. We concentrate our attention on dealing with adverse effects that work on real-time identification of process, especially quantization. It is shown, that a neural network applied to on-line identification process produces more stable solution in the rapid sampling domain. Taking advantage of this result, we propose here an adaptive controller with a neural network as on-line estimator. Simple heuristic synthesis based on modified Ziegler-Nichols open loop method (Z-N 1) are discussed, that deals with bad-estimated model of a plant and gives numerically stable parameters of the PID discrete controller.
BibTex
@article{BUT46303,
author="Václav {Veleba} and Petr {Pivoňka}",
title="Adaptive Controller with Identification Based on Neural Network for Systems with Rapid Sampling Rates",
annote="In this paper ability of three identification methods to parameter estimation of the dynamic plant with great ratio of its time constant to sampling periods is compared. We concentrate our attention on dealing with adverse effects that work on real-time identification of process, especially quantization. It is shown, that a neural network applied to on-line identification process produces more stable solution in the rapid sampling domain. Taking advantage of this result, we propose here an adaptive controller with a neural network as on-line estimator. Simple heuristic synthesis based on modified Ziegler-Nichols open loop method (Z-N 1) are discussed, that deals with bad-estimated model of a plant and gives numerically stable parameters of the PID discrete controller.",
chapter="46303",
number="4",
volume="4",
year="2005",
month="june",
pages="385",
type="journal article - other"
}