6 papers
Physics-Informed Neural Networks for Nonlinear Output Regulation
Sebastiano Mengozzi, Giovanni B. Esposito, Michelangelo Bin +3
This work addresses the full-information output regulation problem for nonlinear systems, assuming the states of both the plant and the exosystem are known. In this setting, perfec…
On the Contraction of Excitable Systems
Alessandro Cecconi, Michelangelo Bin, Lorenzo Marconi +1
We study the contraction of Hodgkin-Huxley model and its role in the reliability of spike timings. Without input, the model is contractive in the region of physiological interest.…
Reliability entails input-selective contraction and regulation in excitable networks
Michelangelo Bin, Alessandro Cecconi, Lorenzo Marconi
The animal nervous system offers a model of computation combining digital reliability and analog efficiency. Understanding how this sweet spot can be realized is a core question of…
On an Abstraction of Lyapunov and Lagrange Stability
Michelangelo Bin, David Angeli
This paper studies a set-theoretic generalization of Lyapunov and Lagrange stability for abstract systems described by set-valued maps. Lyapunov stability is characterized as the p…
Event disturbance rejection: a case study
Alessandro Cecconi, Michelangelo Bin, Rodolphe Sepulchre +1
This article introduces the problem of robust event disturbance rejection. Inspired by the design principle of linear output regulation, a control structure based on excitable syst…
Regulation without calibration
Rodolphe Sepulchre, Alessandro Cecconi, Michelangelo Bin +1
This article revisits the importance of the internal model principle in the literature of regulation and synchronization. Trajectory regulation, the task of regulating continuous-t…