Control relevant long-range plant identification using recurrent neural networks

Jennie Si, Guian Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In adaptive critic control systems design as well as other control systems design schemes, e.g., model-based predictive control, the plant model has to be iterated to predict many time steps ahead into the future. A commonly used implementation is to employ a parallel identification structure. It has been justified for linear estimation that (assume that estimates to the real plant are biased) long-range prediction models are less sensitive to high frequency noise, whether actual noise or caused by model-plant mismatch. We address feasibilities of using recurrent neural networks for long-range plant identification. We examine the existence, training and performance of such recurrent neural network identifiers.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
Pages857-861
Number of pages5
Volume1
StatePublished - 1995
EventProceedings of the 1995 American Control Conference. Part 1 (of 6) - Seattle, WA, USA
Duration: Jun 21 1995Jun 23 1995

Other

OtherProceedings of the 1995 American Control Conference. Part 1 (of 6)
CitySeattle, WA, USA
Period6/21/956/23/95

ASJC Scopus subject areas

  • Control and Systems Engineering

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  • Cite this

    Si, J., & Zhou, G. (1995). Control relevant long-range plant identification using recurrent neural networks. In Proceedings of the American Control Conference (Vol. 1, pp. 857-861)