Robust adaptive neurocontrol of SISO nonlinear systems preceded by unknown deadzone
type: Item
instance of: scholarly article
title: Robust adaptive neurocontrol of SISO nonlinear systems preceded by unknown deadzone
zbMATH Open document ID: 1264.93107
DOI: 10.1155/2012/342739
author: J. Humberto Pérez-Cruz
author: Eduardo Ruiz-Velazquez
author: Carlos A. De Alba-Padilla
author: José De Jesús Rubio
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: The problem of controlling an unknown SISO nonlinear system in Brunovsky canonical form with unknown deadzone input in such a way that the system output follows a specified bounded reference trajectory is considered. Based on universal approximation property of the neural networks, two schemes are proposed to handle this problem. The first scheme utilizes a smooth adaptive inverse of the deadzone. By means of Lyapunov analyses, the exponential convergence of the tracking error to a bounded zone is proven. The second scheme considers the deadzone as a combination of a linear term and a disturbance-like term. Thus, the estimation of the deadzone inverse is not required. By using a Lyapunov-like analyses, the asymptotic converge of the tracking error to a bounded zone is demonstrated. Since this control strategy requires the knowledge of a bound for an uncertainty/disturbance term, a procedure to find such bound is provided. In both schemes, the boundedness of all closed-loop signals is guaranteed. A numerical experiment shows that a satisfactory performance can be obtained by using any of the two proposed controllers.
Mathematics Subject Classification ID: 93C40
Mathematics Subject Classification ID: 94A12
Mathematics Subject Classification ID: 93B51
zbMATH DE Number: 6173201
Wikidata QID: Q58911452
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/342739
OpenAlex ID: W1990382792
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description: scientific article; zbMATH DE number 6173201