Deep-Learning-Based Identification of LPV Models for Nonlinear Systems

Autor:	Verhoek, Chris, Beintema, Gerben I., Haesaert, Sofie, Schoukens, Maarten, th, Roland Tó
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Systems and Control Mathematics - Dynamical Systems Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	The Linear Parameter-Varying (LPV) framework provides a modeling and control design toolchain to address nonlinear (NL) system behavior via linear surrogate models. Despite major research effort on LPV data-driven modeling, a key shortcoming of the current identification theory is that often the scheduling variable is assumed to be a given measured signal in the data set. In case of identifying an LPV model of a NL system, the selection of the scheduling map, which describes the relation to the measurable scheduling signal, is put on the users' shoulder, with only limited supporting tools available. This choice however greatly affects the usability and complexity of the resulting LPV model. This paper presents a deep-learning-based approach to provide joint estimation of a scheduling map and an LPV state-space model of a NL system from input-output data, and has consistency guarantees under general innovation-type noise conditions. Its efficiency is demonstrated on a realistic identification problem. Comment: Accepted for presentation at the 61st IEEE Conference on Decision and Control
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.04060 Zobrazit plný text záznamu View this record from Arxiv