Inverse dynamics controllers for robust control: Consequences for neurocontrollers View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1996

AUTHORS

Csaba Szepesvári , András Lörincz

ABSTRACT

It is proposed that controllers that approximate the inverse dynamics of the controlled plant can be used for on-line compensation of changes in the plant's dynamics. The idea is to use the very same controller in two modes at the same time: both for static and dynamic feedback. Implications for the learning of neurocontrollers are discussed. The proposed control mode relaxes the demand of precision and as a consequence, controllers that utilise direct associative learning by means of local function approximators may become more tractable in higher dimensional spaces. More... »

PAGES

791-796

Book

TITLE

Artificial Neural Networks — ICANN 96

ISBN

978-3-540-61510-1
978-3-540-68684-2

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-61510-5_133

DOI

http://dx.doi.org/10.1007/3-540-61510-5_133

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1035620037


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