Eckhard Arnold
Papers
1
Total Citations
18
H-Index
1
About
Eckhard Arnold is a leading figure in the field of model-predictive control (MPC) and its real-time application to large-scale robotic systems, particularly rotary cranes. His work focuses on the critical intersection of feedforward and feedback control—known as two-degree-of-freedom (2DOF) control—where the generation of optimal reference trajectories is paramount. Arnold’s major contribution lies in developing constrained, real-time MPC strategies that dynamically plan these trajectories, ensuring both safety and high tracking performance under physical system limits. His seminal 2013 paper on this topic, which has garnered 18 citations, provides a foundational framework for integrating trajectory planning directly into the control loop, a challenge that is central to modern automation. By addressing the computational demands of real-time optimization, Arnold has enabled more precise and efficient operation of heavy machinery, reducing sway and improving cycle times in industrial cranes. His work is essential reading for researchers and students in robotics and control engineering, offering a practical bridge between theoretical MPC and real-world deployment.
Research Focus
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Top Papers
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