5 papers
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…
A new approach for combined model class selection and parameters learning for auto-regressive neural models
Corrado Sgadari, Alessio La Bella, Marcello Farina
This work introduces a novel approach for the joint selection of model structure and parameter learning for nonlinear dynamical systems identification. Focusing on a specific Recur…
Development of a velocity form for a class of RNNs, with application to offset-free nonlinear MPC design
Daniele Ravasio, Bestem Abdulaziz, Marcello Farina +1
This paper addresses the offset-free tracking problem for nonlinear systems described by a class of recurrent neural networks (RNNs). To compensate for constant disturbances and gu…
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…
Regional stability conditions for recurrent neural network-based control systems
Alessio La Bella, Marcello Farina, William D'Amico +1
In this paper we propose novel global and regional stability analysis conditions based on linear matrix inequalities for a general class of recurrent neural networks. These conditi…