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 …

As the smart grid evolves, it requires increasing distributed intelligence, optimization and
control. Model predictive control (MPC) facilitates these functionalities for smart grid …

There has been an increasing interest in using model predictive control (MPC) for power
electronic applications. However, the exponential increase in computational complexity and …

This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …

Modern industrial processes often operate under different modes, which brings challenges
to model predictive control (MPC). Recently, most MPC related methods would establish …

Electric means of transportation have seen a rapid expansion partly due to flexibility to
commuters and carbon emission reduction. However, transportation electrification is …

In this article, a novel robust data-driven model-free predictive control framework based on
the I/O data of the controlled plants, which is performed by incorporating the neural predictor …

Model Predictive Control (MPC) is an optimal control algorithm with strong stability and
robustness guarantees. Despite its popularity in robotics and industrial applications, the …

In this work, we consider the problem of Koopman modeling and data-driven predictive
control for a class of uncertain nonlinear systems subject to time delays. A robust deep …

We present differentiable predictive control (DPC) as a deep learning-based alternative to
the explicit model predictive control (MPC) for unknown nonlinear systems. In the DPC …