In autonomous systems, the ability to make forecasts and cope with uncertain predictions is
synonymous with intelligence. Model predictive control (MPC) is an established control …

Today's world is changing rapidly due to advancements in information technology,
computation and communication. Actuation, communication, sensing, and control are …

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 …

Methods based on polynomial chaos expansion allow to approximate the behavior of
systems with uncertain parameters by deterministic dynamics. These methods are used in a …

The present contribution demonstrates the applicability of polynomial chaos expansion to
stochastic (optimal) AC power flow problems that arise in the operation of power grids. For …

Polynomial chaos theory can be used to approximate a stochastic linear dynamical system
by a deterministic linear system of larger dimension. This approximation enables the use of …

The advent of new communication standards for the Internet of Things (IoT), such as Sigfox
or NB-IoT, opens a new set of possibilities that enable low-power communication between a …

This article is devoted to providing a review of mathematical formulations in which
Polynomial Chaos Theory (PCT) has been incorporated into stochastic model predictive …

Generalized Polynomial Chaos (gPC) theory has been widely used for representing
parametric uncertainty in a system, thanks to its ability to propagate uncertainty evolution. In …

摘要近几十年来, 不确定系统模型预测控制的理论和应用得到了飞速发展. 本文简要地回顾了不
确定系统中鲁棒模型预测控制和随机模型预测控制的发展历史, 总结了它们的相关应用 …