A new tractable tube-based robust data-driven MPC for unknown discrete-time LTI systems using one noisy trajectory, simplex-constrained Hankel coefficients, and certified RPI sets to ensure recursive feasibility and practical ISS via a convex QP.
Willems’ fundamental lemma for state-space systems and its extension to multiple datasets,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
eess.SY 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A data-driven method designs probabilistic finite L2-gain stabilizers for stochastic linear systems from noisy trajectories via LMIs.
Adversaries can poison data-driven observability analysis by applying invertible linear transformations to data matrices to embed malicious states and destroy informativity certificates.
citing papers explorer
-
Tube-Based Robust Data-Driven Predictive Control
A new tractable tube-based robust data-driven MPC for unknown discrete-time LTI systems using one noisy trajectory, simplex-constrained Hankel coefficients, and certified RPI sets to ensure recursive feasibility and practical ISS via a convex QP.
-
Data-Driven Probabilistic Finite $\mathcal{L}_2$-Gain Stabilization of Stochastic Linear Systems
A data-driven method designs probabilistic finite L2-gain stabilizers for stochastic linear systems from noisy trajectories via LMIs.
-
Data Poisoning Attacks on Informativity for Observability: Invariance-Based Synthesis
Adversaries can poison data-driven observability analysis by applying invertible linear transformations to data matrices to embed malicious states and destroy informativity certificates.