M. Tiberti
Papers
1
Total Citations
6
H-Index
1
About
M. Tiberti’s research centers on advanced control systems for robotics, with a particular focus on repetitive learning control algorithms that enable precise synchronization in periodic tasks. Their most-cited work, “A learning control algorithm for periodic robot synchronization: Experimental results” (2018), introduces a novel approach that achieves asymptotic joint position tracking for robotic manipulators with uncertain dynamics during repetitive operations. By theoretically and experimentally integrating a recursive period identifier, Tiberti demonstrates how robots can autonomously adapt to varying task cycles without requiring exact prior knowledge of the motion period. This contribution addresses a critical challenge in industrial automation and collaborative robotics, where precision and adaptability are paramount. With 6 citations, the paper has influenced subsequent research in adaptive learning control and robot synchronization. Tiberti’s work bridges theoretical control design with practical experimental validation, offering a robust framework for enhancing robotic performance in manufacturing, assembly, and other repetitive applications. Their findings underscore the potential of learning-based methods to improve efficiency and accuracy in uncertain environments, marking a meaningful step forward in the field of robotic control systems.
Research Focus
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Top Papers
- 1