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Two adaptive approaches of nonlinear system control

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dc.title Two adaptive approaches of nonlinear system control en
dc.contributor.author Sámek, David
dc.contributor.author Chalupa, Petr
dc.relation.ispartof 2008 3rd International Symposium on Communications, Control and Signal Processing, Vols 1-3
dc.identifier.isbn 978-1-4244-1687-5
dc.date.issued 2008
dc.citation.spage 334
dc.citation.epage 339
dc.event.title 3rd IEEE International Symposium on Control, Communications and Signal Processing (ISCCSP 2008)
dc.event.location St Julians
utb.event.state-en Malta
utb.event.state-cs Malta
dc.event.sdate 2008-03-12
dc.event.edate 2008-03-14
dc.type conferenceObject
dc.language.iso en
dc.publisher The Institute of Electrical and Electronics Engineers (IEEE) en
dc.identifier.doi 10.1109/ISCCSP.2008.4537245
dc.relation.uri http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4537245
dc.subject adaptive control en
dc.subject real-time systems en
dc.subject neural networks en
dc.subject predictive control en
dc.subject self-tuning control en
dc.description.abstract Generally the artificial neural networks (ANN) are regarded as highly computational demanding method. The usage of ANN in model predictive control as an adaptive predictor is mostly impossible. The aim of this paper is to present and compare one possible way how to reduce computational costs of adaptive predictors based on artificial neural networks. This paper presents real-time system control by two adaptive control methods. The first method is based on the model predictive method with adaptive artificial neural network as a predictor. This artificial neural network offers interesting solution of the computation time problem while using artificial neural network as an adaptive (online) predictor. The second method is established on self-tuning approach. Both these methods are applied to a problem of control liquid level in interconnected tanks. Real-time experiments are performed using Amira DTS200 - Three Tank System. This system is characterized by non-linear behavior. en
utb.faculty Faculty of Technology
dc.identifier.uri http://hdl.handle.net/10563/1001888
utb.identifier.rivid RIV/70883521:28110/08:63507416!RIV09-MSM-28110___
utb.identifier.obdid 18553156
utb.identifier.scopus 2-s2.0-50649109622
utb.identifier.wok 000257934100065
utb.source d-wok
dc.date.accessioned 2011-08-09T07:34:09Z
dc.date.available 2011-08-09T07:34:09Z
utb.contributor.internalauthor Sámek, David
utb.contributor.internalauthor Chalupa, Petr
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