786 citations · 2.4k across the 106 of their papers we have counts for
8 papers · 1 filter
Open-loop contraction design
Jin Gyu Lee, Thiago B. Burghi, Rodolphe Sepulchre
Given a non-contracting trajectory of a nonlinear system, we consider the question of designing an input perturbation that makes the perturbed trajectory contracting. This paper st…
Loop Shaping with Scaled Relative Graphs
Thomas Chaffey, Fulvio Forni, Rodolphe Sepulchre
The Scaled Relative Graph (SRG) is a generalization of the Nyquist diagram that may be plotted for nonlinear operators, and allows nonlinear robustness margins to be defined graphi…
On the incremental form of dissipativity
Rodolphe Sepulchre, Thomas Chaffey, Fulvio Forni
Following the seminal work of Zames, the input-output theory of the 70s acknowledged that incremental properties (e.g. incremental gain) are the relevant quantities to study in non…
Splitting algorithms and circuit analysis
Thomas Chaffey, Amritam Das, Rodolphe Sepulchre
The splitting algorithms of monotone operator theory find zeros of sums of relations. This corresponds to solving series or parallel one-port electrical circuits, or the negative f…
Rapid and robust synchronization via weak synaptic coupling Extended arXiv version
Jin Gyu Lee, Rodolphe Sepulchre
This paper examines how weak synaptic coupling can achieve rapid synchronization in heterogeneous networks. The assumptions aim at capturing the key mathematical properties that ma…
Affine-Invariant Midrange Statistics
Cyrus Mostajeran, Christian Grussler, Rodolphe Sepulchre
We formulate and discuss the affine-invariant matrix midrange problem on the cone of positive definite Hermitian matrices , which is based on the Thompso…
Adaptive Conductance Control
Raphael Schmetterling, Thiago Burghi, Rodolphe Sepulchre
Neuromodulation is central to the adaptation and robustness of animal nervous systems. This paper explores the classical paradigm of indirect adaptive control to design neuromodula…
Distributed online estimation of biophysical neural networks
Thiago B. Burghi, Timothy O'Leary, Rodolphe Sepulchre
In this work, we propose a distributed adaptive observer for a class of nonlinear networked systems inspired by biophysical neural network models. Neural systems learn by adjusting…