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 …

Model predictive control (MPC) has demonstrated exceptional success for the high-
performance control of complex systems. The conceptual simplicity of MPC as well as its …

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 …

Uncertainties from deepening penetration of renewable energy resources have posed
critical challenges to the secure and reliable operations of future electric grids. Among …

A two-layer control scheme based on model predictive control (MPC) operating at two
different timescales is proposed for the energy management of a grid-connected microgrid …

Motivated by requirements in the process industries, the largest user of model predictive
control, we re-examine some features of recent research on this topic. We suggest that some …

In this paper, we discuss the model predictive control algorithms that are tailored for
uncertain systems. Robustness notions with respect to both deterministic (or set based) and …

In autonomous systems, the ability to make forecasts and cope with uncertain predictions is
synonymous with intelligence. Model predictive control (MPC) is an established control …

This paper presents a new approach to efficiently integrate long prediction horizons subject
to uncertainty into a stochastic model predictive control (MPC) framework for the energy …