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"
}