Detail publikace

Adaptive Optimal Controller for the Air Flowage System

Originální název

Adaptive Optimal Controller for the Air Flowage System

Anglický název

Adaptive Optimal Controller for the Air Flowage System

Jazyk

en

Originální abstrakt

This article shows the principle of an adaptive linear optimal controller (LQ) based on a pseudo-space model. The described adaptive controller, with on-line adaptation algorithm, is suitable for implementation into industrial controller. One of the reasons is that time for computing is short, because in one sapling period only one iteration of Riccati equation is solved. Secondly, this approach uses only input and output signals from the controlled system. That means we do not need measuring or reconstruction of system states. For the on-line identification of controlled system an artificial neural network is used. Properties of the LQ controller are tested on a real system.

Anglický abstrakt

This article shows the principle of an adaptive linear optimal controller (LQ) based on a pseudo-space model. The described adaptive controller, with on-line adaptation algorithm, is suitable for implementation into industrial controller. One of the reasons is that time for computing is short, because in one sapling period only one iteration of Riccati equation is solved. Secondly, this approach uses only input and output signals from the controlled system. That means we do not need measuring or reconstruction of system states. For the on-line identification of controlled system an artificial neural network is used. Properties of the LQ controller are tested on a real system.

BibTex


@inproceedings{BUT22653,
  author="Vlastimil {Lorenc} and Petr {Pivoňka}",
  title="Adaptive Optimal Controller for the Air Flowage System",
  annote="This article shows the principle of an adaptive linear optimal controller (LQ) based on a pseudo-space model. The described adaptive controller, with on-line adaptation algorithm, is suitable for implementation into industrial controller. One of the reasons is that time for computing is short, because in one sapling period only one iteration of Riccati equation is solved. Secondly, this approach uses only input and output signals from the controlled system. That means we do not need measuring or reconstruction of system states. For the on-line identification of controlled system an artificial neural network is used. Properties of the LQ controller are tested on a real system.",
  address="DAAAM International Viena",
  booktitle="Annals of DAAAM for 2007 & Proceedings of the 18th International DAAAM Symposium",
  chapter="22653",
  institution="DAAAM International Viena",
  year="2007",
  month="october",
  pages="425--426",
  publisher="DAAAM International Viena",
  type="conference paper"
}