7 papers · 1 filter
Informativity for Data-driven Prediction
Joel Stevens, Jeremy Coulson
In this work we examine the problem of data-driven prediction. That is, given a LTI system with unknown dynamics, we wish to use data collected from the system to predict the syste…
On the sensitivity of the subspace predictor to behavioral perturbations
Dian Jin, Jeremy Coulson
Behavioral systems define discrete-time LTI systems in terms of a set of trajectories, which forms a linear subspace. This subspace underlies the subspace predictor used in data-dr…
From time series to dissipativity of linear systems with dynamic supply rates
Henk J. van Waarde, Jeremy Coulson, Alberto Padoan
This paper studies the problem of verifying dissipativity of linear time-invariant (LTI) systems using input-output data. We leverage behavioral systems theory to express dissipati…
Geometrically robust least squares through manifold optimization
Jeremy Coulson, Alberto Padoan, Cyrus Mostajeran
This paper presents a methodology for solving a geometrically robust least squares problem, which arises in various applications where the model is subject to geometric constraints…
Robust Least-Squares Optimization for Data-Driven Predictive Control: A Geometric Approach
Shreyas Bharadwaj, Bamdev Mishra, Cyrus Mostajeran +3
The paper studies a geometrically robust least-squares problem that extends classical and norm-based robust formulations. Rather than minimizing residual error for fixed or perturb…
Online Subspace Learning on Flag Manifolds for System Identification
Dian Jin, Jeremy Coulson
Data-driven control methods based on subspace representations are powerful but are often limited to linear time-invariant systems where the model order is known. A key challenge is…
Distances between finite-horizon linear behaviors
Alberto Padoan, Jeremy Coulson
The paper introduces a class of distances for linear behaviors over finite time horizons. These distances allow for comparisons between finite-horizon linear behaviors represented…