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 …

Gaussian process (GP) regression has been widely used in supervised machine learning
due to its flexibility and inherent ability to describe uncertainty in function estimation. In the …

We consider the problem of generating motion plans for a robot that are guaranteed to
succeed despite uncertainty in the environment, parametric model uncertainty, and …

This paper presents an investigation of how Model Predictive Control (MPC) and weather
predictions can increase the energy efficiency in Integrated Room Automation (IRA) while …

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 …

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 …

This paper provides a review of model predictive control (MPC) methods with active
uncertainty learning. System uncertainty poses a key theoretical and practical challenge in …

One of the most critical challenges facing society today is climate change and thus the need
to realize massive energy savings. Since buildings account for about 40% of global final …

We study the constrained linear quadratic regulator with unknown dynamics, addressing the
tension between safety and exploration in data-driven control techniques. We present a …