6 citations · 16 across the 8 of their papers we have counts for
9 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…
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…
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…
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…
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…
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…
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…