6 citations · 41 across the 34 of their papers we have counts for
27 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Tailored neural networks for learning optimal value functions in MPC
Dieter Teichrib, Moritz Schulze Darup
Learning-based predictive control is a promising alternative to optimization-based MPC. However, efficiently learning the optimal control policy, the optimal value function, or the…
Secure learning-based MPC via garbled circuit
K. Tjell, N. Schlüter, P. Binfet +1
Encrypted control seeks confidential controller evaluation in cloud-based or networked systems. Many existing approaches build on homomorphic encryption (HE) that allow simple math…