6 citations · 41 across the 34 of their papers we have counts for
38 papers
Towards learning-enhanced solvers for multi-parametric optimization
Dieter Teichrib, Fabian König, Moritz Schulze Darup
Neural networks (NNs) have been employed in various ways to accelerate the solution of multi-parametric optimization problems (OPs), which prominently arise in model predictive con…
On the (non-)resilience of encrypted controllers to covert attacks
Philipp Binfet, Janis Adamek, Moritz Schulze Darup
The security of networked control systems (NCS) is receiving increasing attention from both cyber-security and system-theoretic perspectives. The former focuses on classical IT sec…
On the Effect of Quadratic Regularization in Direct Data-Driven LQR
Manuel Klädtke, Feiran Zhao, Florian Dörfler +1
This paper proposes an explainability concept for direct data-driven linear quadratic regulation (LQR) with quadratic regularization. Our perspective follows the parametric effect…
MPC for Aquifer Thermal Energy Storage Systems Using ARX Models
Johannes van Randenborgh, Moritz Schulze Darup
An aquifer thermal energy storage (ATES) can mitigate CO2 emissions of heating, ventilation, and air conditioning (HVAC) systems for buildings. In application, an ATES keeps large…
A lightweight numerical model for predictive control of borehole thermal energy storages
Johannes van Randenborgh, Steffen Daniel, Moritz Schulze Darup
Borehole thermal energy storage (BTES) can reduce the operation of fossil fuel-based heating, ventilation, and air conditioning systems for buildings. With BTES, thermal energy is…
On data usage and predictive behavior of data-driven predictive control with 1-norm regularization
Manuel Klädtke, Moritz Schulze Darup
We investigate the data usage and predictive behavior of data-driven predictive control (DPC) with 1-norm regularization. Our analysis enables the offline removal of unused data an…
A mixed-integer framework for analyzing neural network-based controllers for piecewise affine systems with bounded disturbances
Dieter Teichrib, Moritz Schulze Darup
We present a method for representing the closed-loop dynamics of piecewise affine (PWA) systems with bounded additive disturbances and neural network-based controllers as mixed-int…
A moving horizon estimator for aquifer thermal energy storages
Johannes van Randenborgh, Moritz Schulze Darup
Aquifer thermal energy storages (ATES) represent groundwater saturated aquifers that store thermal energy in the form of heated or cooled groundwater. Combining two ATES, one can h…
Towards explainable data-driven predictive control with regularizations
Manuel Klädtke, Moritz Schulze Darup
Data-driven predictive control (DPC), using linear combinations of recorded trajectory data, has recently emerged as a popular alternative to traditional model predictive control (…
Reachability analysis for piecewise affine systems with neural network-based controllers
Dieter Teichrib, Moritz Schulze Darup
Neural networks (NN) have been successfully applied to approximate various types of complex control laws, resulting in low-complexity NN-based controllers that are fast to evaluate…
Encrypted system identification as-a-service via reliable encrypted matrix inversion
Janis Adamek, Philipp Binfet, Nils Schlüter +1
Encrypted computation opens up promising avenues across a plethora of application domains, including machine learning, health-care, finance, and control. Arithmetic homomorphic enc…
On the design of stabilizing FIR controllers
Janis Adamek, Nils Schlüter, Moritz Schulze Darup
Recently, it has been observed that finite impulse response controllers are an excellent basis for encrypted control, where privacy-preserving controller evaluations via special cr…
Privacy-preserving gradient-based fair federated learning
Janis Adamek, Moritz Schulze Darup
Federated learning (FL) schemes allow multiple participants to collaboratively train neural networks without the need to directly share the underlying data.However, in early scheme…
Piecewise regression via mixed-integer programming for MPC
Dieter Teichrib, Moritz Schulze Darup
Piecewise regression is a versatile approach used in various disciplines to approximate complex functions from limited, potentially noisy data points. In control, piecewise regress…
A code-driven tutorial on encrypted control: From pioneering realizations to modern implementations
Nils Schlüter, Junsoo Kim, Moritz Schulze Darup
The growing interconnectivity in control systems due to robust wireless communication and cloud usage paves the way for exciting new opportunities such as data-driven control and s…
Towards a unifying framework for data-driven predictive control with quadratic regularization
Manuel Klädtke, Moritz Schulze Darup
Data-driven predictive control (DPC) has recently gained popularity as an alternative to model predictive control (MPC). Amidst the surge in proposed DPC frameworks, upon closer in…
MPC using mixed-integer programming for aquifer thermal energy storages
Johannes van Randenborgh, Moritz Schulze Darup
Aquifer thermal energy storages (ATES) are used to temporally store thermal energy in groundwater saturated aquifers. Typically, two storages are combined, one for heat and one for…
Extending direct data-driven predictive control towards systems with finite control sets
Manuel Klädtke, Moritz Schulze Darup, Daniel E. Quevedo
Although classical model predictive control with finite control sets (FCS-MPC) is quite a popular control method, particularly in the realm of power electronics systems, its direct…
Privacy Analysis of Affine Transformations in Cloud-based MPC: Vulnerability to Side-knowledge
Teimour Hosseinalizadeh, Nils Schlüter, Moritz Schulze Darup +1
Search for the optimizer in computationally demanding model predictive control (MPC) setups can be facilitated by Cloud as a service provider in cyber-physical systems. This advant…
On the security of randomly transformed quadratic programs for privacy-preserving cloud-based control
Philipp Binfet, Nils Schlüter, Moritz Schulze Darup
Control related data, such as system states and inputs or controller specifications, is often sensitive. Meanwhile, the increasing connectivity of cloud-based or networked control…
Efficient computation of Lipschitz constants for MPC with symmetries
Dieter Teichrib, Moritz Schulze Darup
Lipschitz constants for linear MPC are useful for certifying inherent robustness against unmodeled disturbances or robustness for neural network-based approximations of the control…
Error bounds for maxout neural network approximations of model predictive control
Dieter Teichrib, Moritz Schulze Darup
Neural network (NN) approximations of model predictive control (MPC) are a versatile approach if the online solution of the underlying optimal control problem (OCP) is too demandin…
Cryptanalysis of Random Affine Transformations for Encrypted Control
Nils Schlüter, Philipp Binfet, Moritz Schulze Darup
Cloud-based and distributed computations are of growing interest in modern control systems. However, these technologies require performing computations on not necessarily trustwort…
Convex NMPC reformulations for a special class of nonlinear multi-input systems with application to rank-one bilinear networks
Manuel Klädtke, Moritz Schulze Darup
We show that a special class of (nonconvex) NMPC problems admits an exact solution by reformulating them as a finite number of convex subproblems, extending previous results to the…