Robust adaptive observer design for uncertain systems with bounded disturbances

Vahram Stepanyan; Naira Hovakimyan

doi:10.1109/TNN.2007.895837

Robust adaptive observer design for uncertain systems with bounded disturbances

Vahram Stepanyan
, Naira Hovakimyan

Research output: Contribution to journal › Article › peer-review

Abstract

This paper presents a robust adaptive observer design methodology for a class of uncertain nonlinear systems in the presence of time-varying unknown parameters with absolutely integrable derivatives, and nonvanishing disturbances. Using the universal approximation property of radial basis function (RBF) neural networks and the adaptive bounding technique, the developed observer achieves asymptotic convergence of state estimation error to zero, while ensuring boundedness of parameter errors. A comparative simulation study is presented by the end.

Original language	English (US)
Pages (from-to)	1392-1403
Number of pages	12
Journal	IEEE Transactions on Neural Networks
Volume	18
Issue number	5
DOIs	https://doi.org/10.1109/TNN.2007.895837
State	Published - Sep 2007
Externally published	Yes

Keywords

Adaptive bounding
Asymptotic observers
Nonlinear systems
Radial basis functions (RBFs) approximation

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

Online availability

10.1109/TNN.2007.895837

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keywords = "Adaptive bounding, Asymptotic observers, Nonlinear systems, Radial basis functions (RBFs) approximation",

author = "Vahram Stepanyan and Naira Hovakimyan",

note = "Manuscript received March 22, 2006; revised November 9, 2006; accepted January 7, 2007. This work was supported in part by the United States Air Force under Contract FA9550-05-1-0157 and by the Multidisciplinary University Research Initiative (MURI) under Subcontract F49620-03-1-0401.",

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AU - Hovakimyan, Naira

N1 - Manuscript received March 22, 2006; revised November 9, 2006; accepted January 7, 2007. This work was supported in part by the United States Air Force under Contract FA9550-05-1-0157 and by the Multidisciplinary University Research Initiative (MURI) under Subcontract F49620-03-1-0401.

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AB - This paper presents a robust adaptive observer design methodology for a class of uncertain nonlinear systems in the presence of time-varying unknown parameters with absolutely integrable derivatives, and nonvanishing disturbances. Using the universal approximation property of radial basis function (RBF) neural networks and the adaptive bounding technique, the developed observer achieves asymptotic convergence of state estimation error to zero, while ensuring boundedness of parameter errors. A comparative simulation study is presented by the end.

KW - Adaptive bounding

KW - Asymptotic observers

KW - Nonlinear systems

KW - Radial basis functions (RBFs) approximation

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Robust adaptive observer design for uncertain systems with bounded disturbances

Abstract

Keywords

ASJC Scopus subject areas

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