This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …

In the eyes of many control scientists, the theory of the scenario approach is a tool for
determining the sample size in certain randomized control-design methods, where an …

This paper proposes a stochastic model predictive control (MPC) approach to optimize the
fuel consumption in a vehicle following context. The practical solution of that problem …

The scenario approach is a general methodology for data-driven optimization that has
attracted a great deal of attention in the past few years. It prescribes that one collects a …

We consider convex optimization problems with uncertain, probabilistically described,
constraints. In this context, scenario optimization is a well recognized methodology where a …

This article takes an integrated view of optimized capacity design and operation of islanded
energy hubs. We consider energy hubs that incorporate emerging distributed energy …

Volt/var control (VVC) is one of the primary functions of the distribution management system
aiming at optimum operation of power distribution networks while respecting all of their …

We provide a data-driven approach equipped with a formal guarantee for verifying the safety
of stochastic systems with unknown dynamics. First, using a notion of barrier certificates, the …

In this work, we propose a data-driven approach to synthesize safety controllers for
continuous-time nonlinear polynomial-type systems with unknown dynamics. The proposed …

This paper studies formal synthesis of controllers for continuous-space systems with
unknown dynamics to satisfy requirements expressed as linear temporal logic formulas …