Detail publikace

Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period

Originální název

Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period

Anglický název

Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period

Jazyk

en

Originální abstrakt

The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. The new approach to analysis of on-line identification methods based on one-step-ahead prediction clears up their sensitivity to disturbances in control loop. On one hand faster disturbance rejection due to short sampling period can be an advantage but on the other hand it brings us some practical problems. Particularly, quantization error and finite numerical precision of industrial controller must be considered in the real process control. 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.

Anglický abstrakt

The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. The new approach to analysis of on-line identification methods based on one-step-ahead prediction clears up their sensitivity to disturbances in control loop. On one hand faster disturbance rejection due to short sampling period can be an advantage but on the other hand it brings us some practical problems. Particularly, quantization error and finite numerical precision of industrial controller must be considered in the real process control. 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.

BibTex


@inproceedings{BUT22106,
  author="Petr {Pivoňka} and Václav {Veleba}",
  title="Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period",
  annote="The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. The new approach to analysis of on-line identification methods based on one-step-ahead prediction clears up their sensitivity to disturbances in control loop. On one hand faster disturbance rejection due to short sampling period can be an advantage but on the other hand it brings us some practical problems. Particularly, quantization error and finite numerical precision of industrial controller must be considered in the real process control. 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.",
  address="Nanyang Technological University",
  booktitle="9th International Conference on Control, Automation, Robotics and Vision, IEEE ICARCV2006",
  chapter="22106",
  institution="Nanyang Technological University",
  year="2006",
  month="december",
  pages="521",
  publisher="Nanyang Technological University",
  type="conference paper"
}