1 citations · 1 across the 6 of their papers we have counts for
6 papers
Bounded Linear Programs for Data-Driven Optimal Control via Moment-Matching
Andrea Martinelli, Lucia Pezzetti, Niklas Schmid +2
Linear programming (LP) formulations offer a conceptually elegant approach to infinite-horizon, model-free nonlinear optimal control in continuous spaces. However, in addition to t…
Joint Chance Constrained Safe-Optimal Control
Niklas Schmid, Jared Miller, Tristan Zeller +3
We consider the finite-time optimal control of stochastic systems subject to a probabilistic constraint on the trajectories' safety. Such formulations are known as joint chance con…
Distributionally Robust Optimization over Wasserstein Balls with i.i.d. Structure
Andrey Kharitenko, Marta Fochesato, Anastasios Tsiamis +2
We consider distributionally robust optimization problems where the uncertainty is modeled via a structured Wasserstein ambiguity set. Specifically, the ambiguity is restricted to…
Joint Chance Constrained Optimal Control via Linear Programming
Niklas Schmid, Marta Fochesato, Tobias Sutter +1
We establish a linear programming formulation for the solution of joint chance constrained optimal control problems over finite time horizons. The joint chance constraint may repre…
Computing Optimal Joint Chance Constrained Control Policies
Niklas Schmid, Marta Fochesato, Sarah H. Q. Li +2
We consider the problem of optimally controlling stochastic, Markovian systems subject to joint chance constraints over a finite-time horizon. For such problems, standard Dynamic P…
Parallel Model Predictive Control for Deterministic Systems
Yuchao Li, Aren Karapetyan, Niklas Schmid +3
In this note, we consider infinite horizon optimal control problems with deterministic systems. Since exact solutions to these problems are often intractable, we propose a parallel…