High gain observer for a class of nonlinear systems with coupled structure and sampled output measurements: application to a quadrotor - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue International Journal of Simulation : Systems, Science and Technology Année : 2019

High gain observer for a class of nonlinear systems with coupled structure and sampled output measurements: application to a quadrotor

Résumé

This paper proposes a new high gain observer for a class of non-uniformly observable nonlinear systems with coupled structure driven by sampled outputs. The considered class of systems is particularly constituted by several subsystems where each subsystem is associated to a subset of the output variables. The observer design is carried out through two steps. First, a high-gain observer is proposed in the continuous-time output case under the assumption that an adequate persistent excitation condition is satisfied by each subsystem. Then, the proposed observer is redesigned to handle the case of sampled outputs leading thereby to a continuous-discrete time observer. The latter property is achieved thanks to the approach pursued along the convergence analysis. The effectiveness of the proposed observer is emphasised in a realistic simulation framework involving a mathematical model of a quadrotor which is diffeomorphic to the proposed class of considered systems.
Fichier principal
Vignette du fichier
HAL High gain observer.pdf (2.5 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02407030 , version 1 (29-11-2023)

Identifiants

Citer

Omar Hernandez, Maria Eusebia Guerrero-Sánchez, M Farza, T Menard, Mohamed M’saad, et al.. High gain observer for a class of nonlinear systems with coupled structure and sampled output measurements: application to a quadrotor. International Journal of Simulation : Systems, Science and Technology, 2019, 50 (5), pp.1089-1105. ⟨10.1080/00207721.2019.1589596⟩. ⟨hal-02407030⟩
123 Consultations
12 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More