206 citations · 207 across the 8 of their papers we have counts for
5 papers · 1 filter
Gain-Scheduling Data-Enabled Predictive Control for Nonlinear Systems with Linearized Operating Regions
Sebastian Zieglmeier, Mathias Hudoba de Badyn, Narada D. Warakagoda +2
This paper presents a Gain-Scheduled Data-Enabled Predictive Control (GS-DeePC) framework for nonlinear systems based on multiple locally linear data representations. Instead of re…
Multi-Horizon Time Series Forecasting of non-parametric CDFs with Deep Lattice Networks
Niklas Erdmann, Lars Bentsen, Roy Stenbro +3
Probabilistic forecasting is not only a way to add more information to a prediction of the future, but it also builds on weaknesses in point prediction. Sudden changes in a time se…
Reinforcement Learning for Pollution Detection in a Randomized, Sparse and Nonstationary Environment with an Autonomous Underwater Vehicle
Sebastian Zieglmeier, Niklas Erdmann, Narada D. Warakagoda
Reinforcement learning (RL) algorithms are designed to optimize problem-solving by learning actions that maximize rewards, a task that becomes particularly challenging in random an…
Data-Enabled Predictive Control with Predictive Adaptive Line-of-Sight Guidance for 3-D Path Following of Autonomous Underwater Vehicles
Sebastian Zieglmeier, Mathias Hudoba de Badyn, Narada D. Warakagoda +2
This paper presents a fully data-driven 3-D path-following framework for autonomous underwater vehicles (AUVs), a representative class of underwater field robotics, based on Data-E…
Semi-Data-Driven Model Predictive Control: A Physics-Informed Data-Driven Control Approach
Sebastian Zieglmeier, Mathias Hudoba de Badyn, Narada D. Warakagoda +2
Data-enabled predictive control (DeePC) has emerged as a powerful technique to control complex systems without the need for extensive modeling efforts. However, relying solely on o…