808 citations · 2.6k across the 59 of their papers we have counts for
12 papers · 1 filter
Robust targeted exploration for systems with non-stochastic disturbances
Janani Venkatasubramanian, Johannes Köhler, Mark Cannon +1
We propose a novel targeted exploration strategy designed specifically for uncertain linear time-invariant systems with energy-bounded disturbances, i.e., without any assumptions o…
Parallelizable Parametric Nonlinear System Identification via tuning of a Moving Horizon State Estimator
Léo Simpson, Jonas Asprion, Simon Muntwiler +2
This paper introduces a novel optimization-based approach for parametric nonlinear system identification. Building upon the prediction error method framework, traditionally used fo…
Fast System Level Synthesis: Robust Model Predictive Control using Riccati Recursions
Antoine P. Leeman, Johannes Köhler, Florian Messerer +3
System level synthesis enables improved robust MPC formulations by allowing for joint optimization of the nominal trajectory and controller. This paper introduces a tailored algori…
Homothetic tube model predictive control with multi-step predictors
Danilo Saccani, Giancarlo Ferrari-Trecate, Melanie N. Zeilinger +1
We present a robust model predictive control (MPC) framework for linear systems facing bounded parametric uncertainty and bounded disturbances. Our approach deviates from standard…
Robust Nonlinear Optimal Control via System Level Synthesis
Antoine P. Leeman, Johannes Köhler, Andrea Zanelli +2
This paper addresses the problem of finite horizon constrained robust optimal control for nonlinear systems subject to norm-bounded disturbances. To this end, the underlying uncert…
State space models vs. multi-step predictors in predictive control: Are state space models complicating safe data-driven designs?
Johannes Köhler, Kim P. Wabersich, Julian Berberich +1
This paper contrasts recursive state space models and direct multi-step predictors for linear predictive control. We provide a tutorial exposition for both model structures to solv…
Stability and performance analysis of NMPC: Detectable stage costs and general terminal costs
Johannes Köhler, Melanie N. Zeilinger, Lars Grüne
We provide a stability and performance analysis for nonlinear model predictive control (NMPC) schemes subject to input constraints. Given an exponential stabilizability and detecta…
Linear tracking MPC for nonlinear systems Part II: The data-driven case
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
We present a novel data-driven model predictive control (MPC) approach to control unknown nonlinear systems using only measured input-output data with closed-loop stability guarant…
Linear tracking MPC for nonlinear systems Part I: The model-based case
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
We develop a tracking model predictive control (MPC) scheme for nonlinear systems using the linearized dynamics at the current state as a prediction model. Under reasonable assumpt…
Model predictive control for linear uncertain systems using integral quadratic constraints
Lukas Schwenkel, Johannes Köhler, Matthias A. Müller +1
In this work, we propose a tube-based MPC scheme for state- and input-constrained linear systems subject to dynamic uncertainties characterized by dynamic integral quadratic constr…
Robust stability analysis of a simple data-driven model predictive control approach
Joscha Bongard, Julian Berberich, Johannes Köhler +1
In this paper, we provide a theoretical analysis of closed-loop properties of a simple data-driven model predictive control (MPC) scheme. The formulation does not involve any termi…
On the design of terminal ingredients for data-driven MPC
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge. The scheme includes a term…