4 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…
Learning stabilising policies for constrained nonlinear systems
Daniele Ravasio, Danilo Saccani, Marcello Farina +1
This work proposes a two-layered control scheme for constrained nonlinear systems represented by a class of recurrent neural networks and affected by additive disturbances. In part…
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…
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…