Niklas Schmid- Master of Science
- PhD Student at ETH Zurich
Niklas Schmid
- Master of Science
- PhD Student at ETH Zurich
About
10
Publications
367
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29
Citations
Introduction
Niklas Schmid is a Ph.D. student at the Automatic Control Laboratory at ETH Zürich under the supervision of Professor John Lygeros and Professor Tobias Sutter. His research focuses on the optimal control of stochastic systems under safety constraints, which includes the analysis of reachability, invariance, and chance-constrained optimal control problems.
Skills and Expertise
Current institution
Education
October 2019 - September 2021
Universität zu Lübeck
Field of study
- Medical Engineering Science
October 2016 - August 2019
Universität zu Lübeck
Field of study
- Medical Engineering Science
Publications
Publications (10)
Distributionally robust optimization (DRO) is a principled approach to incorporate robustness against ambiguity in the specified probabilistic models. This paper considers data-driven DRO problems with Wasserstein ambiguity sets, where the uncertain distribution splits into i.i.d. components. By exploiting the latter decomposition, we construct a t...
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 the joint chance constraints, which calls for non-Markovian, and possibly stochastic, policies. Hence, despite t...
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 treatment planning. Approach. From Prompt-Gamma-Timing measurements, we reconstruct the spatiotemporal distributi...
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 specification that the trajectory must satisfy with a predefined probability. For finite state and action spaces, the solu...
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 these quantities using dynamic and linear programming.
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 of inference and learning on classical GPs scales cubically, which is intractable for real-time applications....
