Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …

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 …

Trajectory planning and trajectory tracking constitute two important functions of an
autonomous overtaking system and a variety of strategies have been proposed in the …

In this article, we present a nonlinear robust model predictive control (MPC) framework for
general (state and input dependent) disturbances. This approach uses an online …

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 …

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

This paper presents a brief review of state‐of‐the‐art operation and control strategies of
distributed energy resources, energy storage systems, and electric vehicles in the microgrid …

Driving requires forecasts. Forecasted movements of objects in the driving scene are
uncertain. Inevitably, decision and control algorithms for autonomous driving need to cope …