It has been proven that advanced building control, like model predictive control (MPC), can
notably reduce the energy use and mitigate greenhouse gas emissions. However, despite …

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 …

In the past ten years many Stochastic Model Predictive Control (SMPC) algorithms have
been developed for systems subject to stochastic disturbances and model uncertainties …

Existing approaches to constrained dynamic programming are limited to formulations where
the constraints share the same additive structure of the objective function (that is, they can …

A two-stage robust optimization (RO) method for buildings' space heating loads (SHLs) in an
integrated community energy system (ICES) is proposed. At the first stage, a bi-level …

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 …

Stochastic model predictive control (SMPC) provides a probabilistic framework for MPC of
systems with stochastic uncertainty. A key feature of SMPC is the inclusion of chance …

Constraint tightening to non-conservatively guarantee recursive feasibility and stability in
Stochastic Model Predictive Control is addressed. Stability and feasibility requirements are …

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 …