Niklas Schmid
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View article: Distributionally Robust Optimization over Wasserstein Balls with i.i.d. Structure
Distributionally Robust Optimization over Wasserstein Balls with i.i.d. Structure Open
We consider distributionally robust optimization problems where the uncertainty is modeled via a structured Wasserstein ambiguity set. Specifically, the ambiguity is restricted to product measures $P^{\otimes N}$, where $P$ lies within a W…
Stopping power and range estimations in proton therapy based on prompt gamma timing: motion models and automated parameter optimization Open
Objective. Particle therapy treatments are currently limited by uncertainties of the delivered dose. Verification techniques like Prompt-Gamma-Timing-based Stopping Power Estimation (PGT-SPE) may allow for reduction of safety margins in tr…
Joint Chance Constrained Optimal Control via Linear Programming Open
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 represent an invariance, reachability or reach-avoid specificati…
Computing Optimal Joint Chance Constrained Control Policies Open
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 Programming is inapplicable due to the time correlation of t…
Parallel Model Predictive Control for Deterministic Systems Open
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 model predictive control (MPC) method that provides an app…
Probabilistic Reachability and Invariance Computation of Stochastic Systems using Linear Programming Open
We consider the safety evaluation of discrete time, stochastic systems over a finite horizon. Therefore, we discuss and link probabilistic invariance with reachability as well as reach-avoid problems. We show how to efficiently compute the…
Probabilistic Reachability and Invariance Computation of Stochastic Systems using Linear Programming Open
We consider the safety evaluation of discrete time, stochastic systems over a finite horizon. Therefore, we discuss and link probabilistic invariance with reachability as well as reach-avoid problems. We show how to efficiently compute the…
View article: A real-time GP based MPC for quadcopters with unknown disturbances
A real-time GP based MPC for quadcopters with unknown disturbances Open
Gaussian Process (GP) regressions have proven to be a valuable tool to predict disturbances and model mismatches and incorporate this information into a Model Predictive Control (MPC) prediction. Unfortunately, the computational complexity…
View article: A real-time GP based MPC for quadcopters with unknown disturbances
A real-time GP based MPC for quadcopters with unknown disturbances Open
Gaussian Process (GP) regressions have proven to be a valuable tool to predict disturbances and model mismatches and incorporate this information into a Model Predictive Control (MPC) prediction. Unfortunately, the computational complexity…