A Data-Driven Trajectory Representation for Nonlinear Systems under quasi-Linear Parameter Varying Embeddings - Pôle Automatique et Diagnostic Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2022

A Data-Driven Trajectory Representation for Nonlinear Systems under quasi-Linear Parameter Varying Embeddings

Résumé

Recent literature has shown how linear timeinvariant (LTI) systems can be represented through trajectorybased features, relying on a single measured input-output (IO) trajectory dictionary, as long as the input is persistently exciting. The so-called behavioural framework is a promising alternative for controller synthesis without the necessity of system identification. In this paper, we extend and translate previous results to a wide class of nonlinear systems, using quasi-Linear Parameter Varying (qLPV) embeddings along suitable IO coordinates. Accordingly, we show how nonlinear data-driven simulation and predictions can be made based on the proposed qLPV approach. A parameter-dependent dissipativity analysis verification setup is also given. Realistic results are included to demonstrate the effectiveness of the tools. (Submitted to the 61st IEEE Conference on Decision and Control)
Fichier principal
Vignette du fichier
CDC22_NonlinearqLPVTrajectory-3.pdf (356.8 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03613735 , version 1 (18-03-2022)
hal-03613735 , version 2 (11-04-2022)

Identifiants

  • HAL Id : hal-03613735 , version 1

Citer

Marcelo Menezes Morato, Julio Normey-Rico, Olivier Sename. A Data-Driven Trajectory Representation for Nonlinear Systems under quasi-Linear Parameter Varying Embeddings. 2022. ⟨hal-03613735v1⟩
97 Consultations
72 Téléchargements

Partager

Gmail Facebook X LinkedIn More