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Deterministic learning and nonlinear observer design

Wang, Cong; Hill, David


A " deterministic learning " (DL) theory was recently proposed for identification of nonlinear system dynamics under full-state measurements. In this paper, for a class of nonlinear systems undergoing periodic or recurrent motions with only output measurements, firstly, it is shown that locally-accurate identification of nonlinear system dynamics can still be achieved. Specifically, by using a high gain observer and a dynamical radial basis function network (RBFN), when state estimation is...[Show more]

CollectionsANU Research Publications
Date published: 2010
Type: Journal article
Source: Asian Journal of Control
DOI: 10.1002/asjc.248


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