2 papers
eess.SY2026
Physics-informed structured learning of a class of recurrent neural networks with guaranteed properties
Daniele Ravasio, Claudia Sbardi, Marcello Farina +1
This paper proposes a physics-informed learning framework for a class of recurrent neural networks tailored to large-scale and networked systems. The approach aims to learn control…
eess.SY2026
Recurrent neural network-based robust control systems with regional properties and application to MPC design
Daniele Ravasio, Alessio La Bella, Marcello Farina +1
This paper investigates the design of output-feedback schemes for systems described by a class of recurrent neural networks. We propose a procedure based on linear matrix inequalit…