8 papers
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…
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…
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…
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…
Min-Max Grassmannian Optimization for Online Subspace Tracking
Shreyas Bharadwaj, Bamdev Mishra, Cyrus Mostajeran +3
This paper discusses robustness guarantees for online tracking of time-varying subspaces from noisy data. Building on recent work in optimization over a Grassmannian manifold, we i…
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…