Data-Driven Nonlinear System Identification of a Throttle Valve Using Koopman Representation

Nicolas Bongiovanni (1) , Bojan Mavkov (1) , Renato Martins (2, 3) , Guillaume Allibert (1)

1 Laboratoire I3S - SIS - Signal, Images et Systèmes
2 ICB - Laboratoire Interdisciplinaire Carnot de Bourgogne
3 TANGRAM - Recalage visuel avec des modèles physiquement réalistes

Electrical Throttle Bodies (ETBs) are massively used in the automotive industry and their modeling and control are challenging because of their high nonlinearity and stochasticity. In this paper, we present a data-driven method grounded on the Koopman operator for the identification of a real ETB valve. The model obtained is control-oriented and represented in a quasi- linear-parameter varying framework. Different experiments are performed to evaluate the performance of the proposed method, including the comparison with two classical nonlinear system identification methods.

Mots clés: System identification; Koopman operator theory; neural networks; Electronic Throttle Body

Domaines: Automatique / Robotique

Nicolas Bongiovanni, Bojan Mavkov, Renato Martins, Guillaume Allibert. Data-Driven Nonlinear System Identification of a Throttle Valve Using Koopman Representation. American Control Conference (ACC 2024), Jul 2024, Toronto, Canada. ⟨hal-04562773⟩