Control-Oriented Neural State-Space Models for State-Feedback Linearization and Pole Placement - Ecole Centrale de Nantes Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Control-Oriented Neural State-Space Models for State-Feedback Linearization and Pole Placement

Résumé

Starting from a data set consisting of input-output measurements of a dynamical process, this paper presents a training procedure for a specifically control-oriented model. The considered dynamic model adopts a particular neural state-space representation: its structure guarantees its linearizability by state feedback. Moreover, the linearizing control law follows trivially from the parameters of the learned model. The method relies on a parameterized continuous-time neural state-space model whose structure is inspired from well-known exact linearization. The feasibility and efficiency of the approach is illustrated on a nonlinear identification benchmark, namely the Silverbox one. The quality of learning and linearizing feature of the control design are validated on two nonlinear models by comparing the input-output behavior of each closed-loop and its best linear approximation.
Fichier principal
Vignette du fichier
ICSC22_Hache.pdf (462.79 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03864595 , version 1 (21-11-2022)

Identifiants

Citer

Alexandre Hache, Maxime Thieffry, Mohamed Yagoubi, Philippe Chevrel. Control-Oriented Neural State-Space Models for State-Feedback Linearization and Pole Placement. ICSC'2022: 10th International Conference on Systems and Control, Nov 2022, Marseille, France. ⟨10.1109/ICSC57768.2022.9993820⟩. ⟨hal-03864595⟩
60 Consultations
211 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More