Lukas Vogel

Lukas Vogel, Andrea Carron, Eleftherios E. Vlahakis et al.

We propose a stochastic model predictive control (MPC) framework for linear systems subject to joint-in-time chance constraints under unknown disturbance distributions. Unlike existing approaches that rely on parametric or Gaussian assumptions, or require expensive offline computation, the method uses conformal prediction to construct finite-sample confidence regions for the system's error trajectories with minimal computational effort. These probabilistic sets enable relaxation of the joint-in-time chance constraints into a deterministic closed-loop formulation based on indirect feedback, ensuring recursive feasibility and chance constraint satisfaction. Further, we extend to the output feedback setting and establish analogous guarantees from output measurements alone, given access to noise samples. Numerical examples demonstrate the effectiveness and advantages compared to existing approaches.

Dimitar Asenov, Peter Müller, Lukas Vogel

Software engineering is extremely information-intensive. Every day developers work with source code, version repositories, issue trackers, documentation, web-based and other information resources. However, three key aspects of information work lack good support: (i) combining information from different sources; (ii) flexibly presenting collected information to enable easier comprehension; and (iii) automatically acting on collected information, for example to perform a refactoring. Poor support for these activities makes many common development tasks time-consuming and error-prone. We propose an approach that directly addresses these three issues by integrating a flexible query mechanism into the development environment. Our approach enables diverse ways to process and visualize information and can be extended via scripts. We demonstrate how an implementation of the approach can be used to rapidly write queries that meet a wide range of information needs.