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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) [Paper under corrections, a new version will be uploaded soon.]
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Dates et versions

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

Identifiants

  • HAL Id : hal-03613735 , version 2

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-03613735v2⟩
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