no code implementations • 21 Dec 2023 • Simon Muntwiler, Johannes Köhler, Melanie N. Zeilinger
In this paper, we study state estimation for general nonlinear systems with unknown parameters and persistent process and measurement noise.
no code implementations • 21 Dec 2023 • Simon Muntwiler, Johannes Köhler, Melanie N. Zeilinger
Together, we present a unified framework to study functional estimation with a detectability condition, which is necessary and sufficient for the existence of a stable functional estimator, and a corresponding functional estimator design.
no code implementations • 16 Oct 2023 • Simon Muntwiler, Ognjen Stanojev, Andrea Zanelli, Gabriela Hug, Melanie N. Zeilinger
The fast modes are then truncated in the rotated coordinate system to obtain a lower-order model with reduced stiffness.
no code implementations • 1 Dec 2022 • Simon Muntwiler, Kim P. Wabersich, Robert Miklos, Melanie N. Zeilinger
We present an output feedback stochastic model predictive control (SMPC) approach for linear systems subject to Gaussian disturbances and measurement noise and probabilistic constraints on system states and inputs.
no code implementations • 25 Feb 2022 • Julian D. Schiller, Simon Muntwiler, Johannes Köhler, Melanie N. Zeilinger, Matthias A. Müller
We provide a novel robust stability analysis for moving horizon estimation (MHE) using a Lyapunov function.
1 code implementation • 8 Sep 2021 • Simon Muntwiler, Kim P. Wabersich, Melanie N. Zeilinger
In a numerical example of estimating temperatures of a group of manufacturing machines, we show the performance of tuning the unknown system parameters and the benefits of integrating physical state constraints in the MHE formulation.
no code implementations • 6 Apr 2020 • Simon Muntwiler, Kim P. Wabersich, Lukas Hewing, Melanie N. Zeilinger
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust formulations that are amenable to distributed design and optimization methods.
no code implementations • 5 Nov 2019 • Simon Muntwiler, Kim P. Wabersich, Andrea Carron, Melanie N. Zeilinger
While distributed algorithms provide advantages for the control of complex large-scale systems by requiring a lower local computational load and less local memory, it is a challenging task to design high-performance distributed control policies.