Stabilization of Aperiodic Sampled-data Linear Systems with Input Constraints: a Low Complexity Polyhedral Approach - Université Grenoble Alpes Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Stabilization of Aperiodic Sampled-data Linear Systems with Input Constraints: a Low Complexity Polyhedral Approach

Résumé

The stabilization problem of aperiodic sampleddata linear systems subject to input constraints is dealt with. A state feedback control law is designed to optimize the size of a polyhedral estimate of the region of attraction of the origin (RAO) of the closed-loop system. The control law is derived from the computation of a controlled contractive polytope for the dynamics between two successive sampling instants. The polytope is of low complexity as its number of vertices is fixed a priori. As shown in the numerical example, the polyhedral estimate of the RAO associated with the proposed feedback control is larger than the ones obtained with other approaches in the literature.
Fichier principal
Vignette du fichier
manuscript.pdf (326.29 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03361476 , version 1 (01-10-2021)

Identifiants

Citer

Daniel Denardi Huff, Mirko Fiacchini, João Manoel Gomes da Silva. Stabilization of Aperiodic Sampled-data Linear Systems with Input Constraints: a Low Complexity Polyhedral Approach. CDC 2021 - 60th IEEE Conference on Decision and Control, Dec 2021, Austin, Texas, United States. ⟨10.1109/CDC45484.2021.9683136⟩. ⟨hal-03361476⟩
53 Consultations
78 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More