F
Frank Allgöwer
Researcher at University of Stuttgart
Publications - 878
Citations - 24902
Frank Allgöwer is an academic researcher from University of Stuttgart. The author has contributed to research in topics: Model predictive control & Nonlinear system. The author has an hindex of 68, co-authored 832 publications receiving 21180 citations. Previous affiliations of Frank Allgöwer include École Polytechnique Fédérale de Lausanne & ETH Zurich.
Papers
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Journal ArticleDOI
A Quasi-Infinite Horizon Nonlinear Model Predictive Control Scheme with Guaranteed Stability
Hong Chen,Frank Allgöwer +1 more
TL;DR: It is proved that feasibility of the open-loop optimal control problem at time t = 0 implies asymptotic stability of the closed-loop system.
Journal ArticleDOI
Brief paper: An internal model principle is necessary and sufficient for linear output synchronization
TL;DR: An internal model requirement is necessary and sufficient for synchronizability of the network to polynomially bounded trajectories and the resulting dynamic feedback couplings can be interpreted as a generalization of existing methods for identical linear systems.
Journal ArticleDOI
Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations
Moritz Diehl,H. Georg Bock,Johannes P. Schlöder,Rolf Findeisen,Zoltan K. Nagy,Frank Allgöwer +5 more
TL;DR: In this paper, the authors present a model predictive control (NMPC) for a high-purity distillation column subject to parameter disturbances, which is based on the direct multiple-shooting (DMS) method.
Book ChapterDOI
Nonlinear Predictive Control and Moving Horizon Estimation — An Introductory Overview
TL;DR: This work states that nonlinear model predictive control, i.e. MPC based on a nonlinear plant description, has only emerged in the past decade and the number of reported industrial applications is still fairly low.
Journal ArticleDOI
Robust output feedback model predictive control of constrained linear systems
TL;DR: This paper provides a solution to the problem of robust output feedback model predictive control of constrained, linear, discrete-time systems in the presence of bounded state and output disturbances by combining a simple, stable Luenberger state estimator and a recently developed, robustly stabilizing, tube-based, model predictive controller.