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 language | English (US) |
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Title of host publication | 2023 American Control Conference, ACC 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4735-4740 |
Number of pages | 6 |
ISBN (Electronic) | 9798350328066 |
DOIs | |
State | Published - 2023 |
Event | 2023 American Control Conference, ACC 2023 - San Diego, United States Duration: May 31 2023 → Jun 2 2023 |
Publication series
Name | Proceedings of the American Control Conference |
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Volume | 2023-May |
ISSN (Print) | 0743-1619 |
Conference
Conference | 2023 American Control Conference, ACC 2023 |
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Country/Territory | United States |
City | San Diego |
Period | 5/31/23 → 6/2/23 |
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.