This paper presents a review on the development and application of model predictive
control (MPC) for autonomous intelligent mechatronic systems (AIMS). Starting from the …

Next-generation vehicle control and future autonomous driving require further advances in
vehicle dynamic state estimation. This article provides a concise review, along with the …

We show that artificial neural networks with rectifier units as activation functions can exactly
represent the piecewise affine function that results from the formulation of model predictive …

Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

In this paper, we propose a robust self-triggered model predictive control (MPC) algorithm
for constrained discrete-time nonlinear systems subject to parametric uncertainties and …

Abstract Model predictive control allows to provide high performance and safety guarantees
in the form of constraint satisfaction. These properties, however, can be satisfied only if the …

Dealing with uncertainties is one of the most challenging issues that prevent nonlinear
model predictive control (NMPC) from being a widespread reality. Many different robust …

The microgrid contains many distributed power sources which have great randomness and
volatility, so it is difficult to maintain the stable operation. As a mobile energy storage …

Cyber-physical Systems (CPS) in industrial manufacturing facilities demand a continuous
interaction with different and a large amount of distributed and networked computing nodes …

We suggest that using deep learning networks to learn model predictive controllers is a
powerful alternative to online optimization, especially when the underlying problems are …