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 …

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 …

Many practical applications in control require that constraints on the inputs and states of the
system are respected, while some performance criterion is optimized. In the presence of …

Stochastic uncertainties are ubiquitous in complex dynamical systems and can lead to
undesired variability of system outputs and, therefore, a notable degradation of closed-loop …

To realize the synergy between plug-in electric vehicles (PEVs) and wind power, this paper
presents a hierarchical stochastic control scheme for the coordination of PEV charging and …

Heating, ventilation and air conditioning (HVAC) systems regulate comfort levels in
buildings, but also consume a large amount of energy, which makes them an attractive …

Model predictive control (MPC) using randomized optimization is expected to solve different
control problems. However, it still faces various challenges for real-world applications. This …

In this paper, a learning-based predictive control (LPC) scheme is proposed for adaptive
optimal control of discrete-time nonlinear systems under stochastic disturbances. The …

The limited dispatchability of wind energy poses a challenge to its increased penetration.
One technically feasible solution to this challenge is to integrate a battery energy storage …

Highlights•A stochastic forecast error model is identified using historical Dutch climate
data.•Quantification of controlled greenhouse system performance loss for forecast …