While model predictive control (MPC) is the industrially preferred method for advanced
control in the process industries, it has not found much use in consumer products and safety …

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

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 …

Model predictive control (MPC) is an optimization-based strategy for high-performance
control that is attracting increasing interest. While MPC requires the online solution of an …

A stochastic receding-horizon control approach for constrained Linear Parameter Varying
discrete-time systems is proposed in this paper. It is assumed that the time-varying …

In this paper, we present modifications to the sphere decoder initially introduced in the work
of Geyer and Quevedo and modified in the work of Karamanakos et al. that significantly …

We study the computational complexity certification of inexact gradient augmented
Lagrangian methods for solving convex optimization problems with complicated constraints …

In this paper, a multiple model predictive controller (MPC) is proposed for the management
of passenger car start up through dry clutch in automated manual transmission. Based on a …

I am indebted to my supervisor Prof. Dr.-Ing. Rolf Findeisen for leaving large scientific
freedom, the opportunity to learn about the administrative aspects of academia, and many …

Abstract Model‐based optimization approaches for monitoring and control, such as model
predictive control and optimal state and parameter estimation, have been used successfully …