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contributor authorChia-Shang Liu
contributor authorHuei Peng
date accessioned2017-05-08T23:56:04Z
date available2017-05-08T23:56:04Z
date copyrightDecember, 1998
date issued1998
identifier issn0022-0434
identifier otherJDSMAA-26251#524_1.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/120135
description abstractThis paper presents an adaptive algorithm to estimate states and unknown parameters simultaneously for nonlinear time invariant systems which depend affinely on the unknown parameters. The system output signals are filtered and reparameterized into a regression form from which the least squares error scheme is applied to identify the unknown parameters. The states are then estimated by an observer based on the estimated parameters. The major difference between this algorithm and existing adaptive observer algorithms is that the proposed algorithm does not require any special canonical forms or rank conditions. However, an output measurement condition is imposed. The stability and performance limit of this scheme are analyzed. Two examples are then presented to show the effectiveness of the proposed schemes.
publisherThe American Society of Mechanical Engineers (ASME)
titleA State and Parameter Identification Scheme for Linearly Parameterized Systems
typeJournal Paper
journal volume120
journal issue4
journal titleJournal of Dynamic Systems, Measurement, and Control
identifier doi10.1115/1.2801496
journal fristpage524
journal lastpage528
identifier eissn1528-9028
keywordsStability
keywordsAlgorithms
keywordsErrors AND Signals
treeJournal of Dynamic Systems, Measurement, and Control:;1998:;volume( 120 ):;issue: 004
contenttypeFulltext


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