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20152026
most citedError bounds for maxout neural network approximations of model predictive control

6 citations · 41 across the 34 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

eess.SY2022★ 1 cited

Encrypted distributed state estimation via affine averaging

N. Schlüter, P. Binfet, J. Kim +1

Distributed state estimation arises in many applications such as position estimation in robot swarms, clock synchronization for processor networks, and data fusion. One characteris…

eess.SY2022★ 1 cited

Encrypted extremum seeking for privacy-preserving PID tuning as-a-Service

Nils Schlüter, Matthias Neuhaus, Moritz Schulze Darup

Wireless communication offers many benefits for control such as substantially reduced deployment costs, higher flexibility, as well as easier data access. It is thus not surprising…

eess.SY2022★ 2 cited

Tailored max-out networks for learning convex PWQ functions

Dieter Teichrib, Moritz Schulze Darup

Convex piecewise quadratic (PWQ) functions frequently appear in control and elsewhere. For instance, it is well-known that the optimal value function (OVF) as well as Q-functions f…

eess.SY2022

Convex reformulations for a special class of nonlinear MPC problems

Manuel Klädtke, Moritz Schulze Darup

We show how the solution to NMPC problems for a special type of input-affine discrete-time systems can be obtained by reformulating the underlying non-convex optimal control proble…

eess.SY2022★ 1 cited

A deterministic view on explicit data-driven (M)PC

Manuel Klädtke, Dieter Teichrib, Nils Schlüter +1

We show that the explicit realization of data-driven predictive control (DPC) for linear deterministic systems is more tractable than previously thought. To this end, we compare th…