3 papers
eess.SY2026
Sinkhorn Ambiguity Sets for Distributionally Robust Control: Convexity, Weak Compactness, and Tractability
Riccardo Cescon, Andrea Martin, Giancarlo Ferrari-Trecate
Classical stochastic control assumes perfect knowledge of the uncertainty affecting the plant. In practice, however, such information is often incomplete. To address this limitatio…
eess.SY2025
On the Global Optimality of Linear Policies for Sinkhorn Distributionally Robust Linear Quadratic Control
Riccardo Cescon, Andrea Martin, Giancarlo Ferrari-Trecate
The Linear Quadratic Gaussian (LQG) regulator is a cornerstone of optimal control theory, yet its performance can degrade significantly when the noise distributions deviate from th…
eess.SY2025
Data-driven Distributionally Robust Control Based on Sinkhorn Ambiguity Sets
Riccardo Cescon, Andrea Martin, Giancarlo Ferrari-Trecate
As the complexity of modern control systems increases, it becomes challenging to derive an accurate model of the uncertainty that affects their dynamics. Wasserstein Distributional…