43 citations · 62 across the 9 of their papers we have counts for
9 papers
Interpretation of High-Dimensional Regression Coefficients by Comparison with Linearized Compressing Features
Joachim Schaeffer, Jinwook Rhyu, Robin Droop +2
Linear regression is often deemed inherently interpretable; however, challenges arise for high-dimensional data. We focus on further understanding how linear regression approximate…
Stability-informed Bayesian Optimization for MPC Cost Function Learning
Sebastian Hirt, Maik Pfefferkorn, Ali Mesbah +1
Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predicti…
Interpretation of High-Dimensional Linear Regression: Effects of Nullspace and Regularization Demonstrated on Battery Data
Joachim Schaeffer, Eric Lenz, William C. Chueh +3
High-dimensional linear regression is important in many scientific fields. This article considers discrete measured data of underlying smooth latent processes, as is often obtained…
LMI-based Data-Driven Robust Model Predictive Control
Hoang Hai Nguyen, Maurice Friedel, Rolf Findeisen
Predictive control, which is based on a model of the system to compute the applied input optimizing the future system behavior, is by now widely used. If the nominal models are not…
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles
Johanna Bethge, Maik Pfefferkorn, Alexander Rose +2
We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. The proposed approach employs…
Safe Machine-Learning-supported Model Predictive Force and Motion Control in Robotics
Janine Matschek, Johanna Bethge, Rolf Findeisen
Many robotic tasks, such as human-robot interactions or the handling of fragile objects, require tight control and limitation of appearing forces and moments alongside sensible mot…