10 citations · 25 across the 26 of their papers we have counts for
6 papers · 1 filter
From Time Series to Affine Systems
A. Padoan, J. Eising, I. Markovsky
The paper extends core results of behavioral systems theory from linear to affine time-invariant systems. We characterize the behavior of affine time-invariant systems via kernel,…
Convergence Analysis of Distributed Optimization: A Dissipativity Framework
Aron Karakai, Jaap Eising, Andrea Martinelli +1
We develop a system-theoretic framework for the structured analysis of distributed optimization algorithms with decomposable cost functions. We model such algorithms as a network o…
Dissipativity-Based Data-Driven Decentralized Control of Interconnected Systems
Taiki Nakano, Ahmed Aboudonia, Jaap Eising +3
We propose data-driven decentralized control algorithms for stabilizing interconnected discrete-time linear time-invariant systems. We first derive a data-driven condition to synth…
Set-valued regression and cautious suboptimization: From noisy data to optimality
Jaap Eising, Jorge Cortes
This paper deals with the problem of finding suboptimal values of an unknown function on the basis of measured data corrupted by bounded noise. As a prior, we assume that the unkno…
Distributed Time-Varying Gaussian Regression via Kalman Filtering
Nicola Taddei, Riccardo Maggioni, Jaap Eising +2
We consider the problem of learning time-varying functions in a distributed fashion, where agents collect local information to collaboratively achieve a shared estimate. This task…
Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection
Joshua Näf, Keith Moffat, Jaap Eising +1
This paper proposes Select-Data-driven Predictive Control (Select-DPC), a new method for controlling nonlinear systems using output-feedback for which data are available but an exp…