Skip to main content
Kent Academic Repository

Robust Observer Design for a Class of Nonlinear Systems Using the System Internal Dynamics Structure

Diao, Z.F., Yan, Xinggang (2008) Robust Observer Design for a Class of Nonlinear Systems Using the System Internal Dynamics Structure. Journal of Optimization Theory and Applications, 138 (2). pp. 175-187. ISSN 0022-3239. (doi:10.1007/s10957-008-9388-0) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:27728)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1007/s10957-008-9388-0

Abstract

In this paper, an observer design strategy is presented for a class of nonlinear systems with structural uncertainty. The modern geometric approach is exploited to simplify the system structure. Then, based on the Lyapunov direct method, a robust observer is proposed using the system internal dynamics structure and the distribution of the uncertainty structure. The bound on the uncertainty, which is employed in the observer design, is allowed to be nonlinear and have a more general form. Simulation shows that the proposed approach is effective.

Item Type: Article
DOI/Identification number: 10.1007/s10957-008-9388-0
Uncontrolled keywords: Nonlinear systems, Differential geometric approach, Robust observers, Lyapunov direct method
Subjects: T Technology > TJ Mechanical engineering and machinery > Control engineering
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: J. Harries
Date Deposited: 26 Apr 2011 09:50 UTC
Last Modified: 05 Nov 2024 10:08 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/27728 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.