6 citations · 41 across the 34 of their papers we have counts for
7 papers · 1 filter
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…
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…
Teaching MPC: Which Way to the Promised Land?
Timm Faulwasser, Sergio Lucia, Moritz Schulze Darup +1
Since the earliest conceptualizations by Lee and Markus, and Propoi in the 1960s, Model Predictive Control (MPC) has become a major success story of systems and control with respec…
On the computation of lambda-contractive sets for linear constrained systems
Moritz Schulze Darup, Mark Cannon
We present two theoretical results on the computation of lambda-contractive sets for linear systems with state and input constraints. First, we show that it is possible to a priori…
Rigorous constraint satisfaction for sampled linear systems
Moritz Schulze Darup
We address a specific but recurring problem related to sampled linear systems. In particular, we provide a numerical method for the rigorous verification of constraint satisfaction…
Improved automatic computation of Hessian matrix spectral bounds
Moritz Schulze Darup, Martin Mönnigmann
This paper presents a fast and powerful method for the computation of eigenvalue bounds for Hessian matrices of nonlinear functions $φ: U \subseteq R^n\rightarrow…