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20162026
most citedOn a Stochastic Fundamental Lemma and Its Use for Data-Driven Optimal Control

58 citations · 217 across the 54 of their papers we have counts for

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Showing 2024Show all

9 papers · 1 filter

eess.SY2024

Tutorial Problems for Nonsmooth Dynamics and Optimal Control: Ski Jumping and Accelerating a Bike Without Pedaling

Julian Golembiewski, Timm Faulwasser

Nonsmooth phenomena, such as abrupt changes, impacts, and switching behaviors, frequently arise in real-world systems and present significant challenges for traditional optimal con…

math.OC2024

Large problems are not necessarily hard: A case study on distributed NMPC paying off

Gösta Stomberg, Maurice Raetsch, Alexander Engelmann +1

A key motivation in the development of Distributed Model Predictive Control (DMPC) is to accelerate centralized Model Predictive Control (MPC) for large-scale systems. DMPC has the…

eess.SY2024

Towards Event-Triggered NMPC for Efficient 6G Communications: Experimental Results and Open Problems

Jens Püttschneider, Julian Golembiewski, Niklas A. Wagner +2

Networked control systems enable real-time control and coordination of distributed systems, leveraging the low latency, high reliability, and massive connectivity offered by 5G and…

cs.RO2024★ 1 cited

Cooperative distributed model predictive control for embedded systems: Experiments with hovercraft formations

Gösta Stomberg, Roland Schwan, Andrea Grillo +2

This paper presents experiments for embedded cooperative distributed model predictive control applied to a team of hovercraft floating on an air hockey table. The hovercraft collec…

math.OC2024

A continuous-time fundamental lemma and its application in data-driven optimal control

Philipp Schmitz, Timm Faulwasser, Paolo Rapisarda +1

Data-driven control of discrete-time and continuous-time systems is of tremendous research interest. In this paper, we explore data-driven optimal control of continuous-time linear…

cs.LG2024★ 1 cited

On Dissipativity of Cross-Entropy Loss in Training ResNets

Jens Püttschneider, Timm Faulwasser

The training of ResNets and neural ODEs can be formulated and analyzed from the perspective of optimal control. This paper proposes a dissipative formulation of the training of Res…