160 citations · 226 across the 11 of their papers we have counts for
13 papers
On stability and non-averaged performance of economic MPC with terminal conditions for optimal periodic operation
Jonas Mair, Lukas Schwenkel, Matthias A. Müller +1
Operation at steady state is often not optimal when optimizing over an economic cost objective. In many cases, periodic operation yields better performance. Therefore, we derive as…
The Cesàro Value Iteration
Jonas Mair, Lukas Schwenkel, Matthias A. Müller +1
In this paper, we consider undiscouted infinite-horizon optimal control for deterministic systems with an uncountable state and input space. We specifically address the case when t…
Output-feedback model predictive control under dynamic uncertainties using integral quadratic constraints
Lukas Schwenkel, Johannes Köhler, Matthias A. Müller +1
In this work, we propose an output-feedback tube-based model predictive control (MPC) scheme for linear systems under dynamic uncertainties that are described via integral quadrati…
Multi-objective robust controller synthesis with integral quadratic constraints in discrete-time
Lukas Schwenkel, Johannes Köhler, Matthias A. Müller +2
This article presents a novel framework for the robust controller synthesis problem in discrete-time systems using dynamic Integral Quadratic Constraints (IQCs). We present an algo…
On discount functions for economic model predictive control without terminal conditions
Lukas Schwenkel, Daniel Briem, Matthias A. Müller +1
In this paper, we investigate discounted economic model predictive control (E-MPC) schemes without terminal conditions in scenarios where the optimal operating behavior is a period…
Stability Mechanisms for Predictive Safety Filters
Elias Milios, Kim Peter Wabersich, Felix Berkel +1
Predictive safety filters enable the integration of potentially unsafe learning-based control approaches and humans into safety-critical systems. In addition to simple constraint s…
Robust peak-to-peak gain analysis using integral quadratic constraints
Lukas Schwenkel, Johannes Köhler, Matthias A. Müller +1
This work provides a framework to compute an upper bound on the robust peak-to-peak gain of discrete-time uncertain linear systems using integral quadratic constraints (IQCs). Such…
Linearly discounted economic MPC without terminal conditions for periodic optimal operation
Lukas Schwenkel, Alexander Hadorn, Matthias A. Müller +1
In this work, we study economic model predictive control (MPC) in situations where the optimal operating behavior is periodic. In such a setting, the performance of a standard econ…
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…
Transient Performance of Tube-based Robust Economic Model Predictive Control
Christian Klöppelt, Lukas Schwenkel, Frank Allgöwer +1
In this paper, we provide non-averaged and transient performance guarantees for recently developed, tube-based robust economic model predictive control (MPC) schemes. In particular…
Robust and optimal predictive control of the COVID-19 outbreak
Johannes Köhler, Lukas Schwenkel, Anne Koch +3
We investigate adaptive strategies to robustly and optimally control the COVID-19 pandemic via social distancing measures based on the example of Germany. Our goal is to minimize t…
Robust Economic Model Predictive Control without Terminal Conditions
Lukas Schwenkel, Johannes Köhler, Matthias A. Müller +1
In this paper, a novel tube-based economic Model Predictive Control (MPC) scheme for uncertain systems that uses neither terminal costs nor terminal constraints is investigated. We…
Online learning with stability guarantees: A memory-based real-time model predictive controller
Lukas Schwenkel, Meriem Gharbi, Sebastian Trimpe +1
We propose and analyze a real-time model predictive control (MPC) scheme that utilizes stored data to improve its performance by learning the value function online with stability g…