Nonlinear Observer Design Methods Based on High-Gain Methodology and LMIs with Application to Vehicle Tracking

H. Bessafa, C. Delattre, Z. Belkhatir, A. Zemouche, R. Rajamani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The main objective of this work is to propose a solution to observer design for triangular systems where additional output measurements are available, which may improve the estimation quality. In fact, such additional measurements prevent the standard high-gain observer methodology to provide solutions for the estimation problem. In this paper, motivated by this issue, we propose two novel observer design methods to handle the additional output measurements. The first one can be viewed as an extension of the standard high-gain observer by introducing a weighting matrix as a tuning parameter, while the second method, which can be viewed as an alternative method, exploits jointly the high-gain methodology and the LPV/LMI technique to overcome some limitations related to the first design method. The proposed methods are applied to a vehicle trajectory estimation problem using the well-known kinematic model. The efficiency of the estimation using both proposed methods and a comparative study between them are provided.

Original languageEnglish (US)
Title of host publication2023 American Control Conference, ACC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4735-4740
Number of pages6
ISBN (Electronic)9798350328066
DOIs
StatePublished - 2023
Event2023 American Control Conference, ACC 2023 - San Diego, United States
Duration: May 31 2023Jun 2 2023

Publication series

NameProceedings of the American Control Conference
Volume2023-May
ISSN (Print)0743-1619

Conference

Conference2023 American Control Conference, ACC 2023
Country/TerritoryUnited States
CitySan Diego
Period5/31/236/2/23

Bibliographical note

Funding Information:
1 Université de Lorraine, CRAN CNRS UMR 7039, 54400 Cosnes et Romain, France (email: ali.zemouche@univ-lorraine.fr). 2Department of Electronics and Computer Science, University of Southampton, University Road, Southampton, SO17 1BJ, UK (email: z.belkhatir@soton.ac.uk) 3 Faculty of Computing, Engineering and Media, De Montfort University, The Gateway, Leicester, LE1 9BH, UK. 4 Laboratory for Innovations in Sensing, Estimation, and Control, Department of Mechanical Engineering, University of Minnesota, Minneapolis, USA (email: rajamani@umn.edu). This work is funded by the ANR agency under the project ArtISMo ANR-20-CE48-0015.

Publisher Copyright:
© 2023 American Automatic Control Council.

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