Arnim Kargl
Papers
1
Total Citations
4
H-Index
1
About
Arnim Kargl’s research centers on model-based control of robotic systems, with a particular focus on bridging the gap between theoretical control algorithms and real-world hardware performance. His most-cited work, “Identification of Friction Models for MPC-based Control of a PowerCube Serial Robot,” exemplifies this approach by systematically developing a model predictive control (MPC) scheme for a Schunk PowerCube robot. Kargl’s major contribution lies in his structured, step-by-step methodology for identifying and integrating accurate friction models—a critical but often overlooked factor—into the control loop, directly enhancing robustness and tracking precision. This work, which has garnered 4 citations, demonstrates his commitment to practical, implementable solutions for industrial serial robots. By leveraging the Neweul-M² multibody dynamics tool, Kargl shows how careful system identification can unlock the full potential of advanced controllers, making his research valuable for engineers seeking to deploy MPC in real-world automation. His efforts highlight the importance of model fidelity in achieving high-performance, reliable robotic motion.
Research Focus
Key Achievements
Top Papers
- 1