1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
Formation Path Following Control of Underactuated USVs -- With Proofs
Å. Eek, K. Y. Pettersen, E. -L. M. Ruud +1
This paper proposes a formation control method for two underactuated unmanned surface vessels (USVs) to follow curved paths in the presence of ocean currents. By uniting a line-of-…
Planning Safe Paths through Hazardous Environments
Chris Denniston, Thomas R. Krogstad, Stephanie Kemna +1
Autonomous underwater vehicles (AUVs) are robotic platforms that are commonly used to map the sea floor, for example for benthic surveys or for naval mine countermeasures (MCM) ope…