Jonathan Becedas
Papers
11
Total Citations
292
H-Index
8
About
Jonathan Becedas is a robotics and control systems researcher whose work spans flexible manipulator control, adaptive identification methods, and tactile sensing for robotic grasping. His most influential contributions lie at the intersection of intelligent control theory and practical robotic systems, with a particular focus on handling real-world uncertainties in mechanical systems. Becedas is perhaps best known for pioneering fast online algebraic identification methods combined with Generalized Proportional Integral (GPI) control for flexible robotic arms — work that has accumulated over 70 citations and addresses the notoriously difficult challenge of controlling lightweight manipulators with unknown payloads and friction. Complementing this, his sliding mode control research (48 citations) demonstrated robust tracking performance under significant parameter uncertainty, advancing the field of lightweight robot arm design. A second major research thread concerns robotic grasping intelligence. His work on two-finger flexible grippers with force feedback (60 citations) and micro-vibration-based slip detection in tactile sensors (60 citations) has meaningfully advanced how robots perceive and respond to contact forces during manipulation tasks. Together, these contributions have garnered over 280 citations, establishing Becedas as a notable voice in adaptive robotics control and sensor-driven manipulation — research of growing relevance as collaborative and dexterous robots become increasingly prominent across industry and research alike.
Research Focus
Key Achievements
Top Papers
- 1
- 2Micro-Vibration-Based Slip Detection in Tactile Force Sensors60 citations · 2014
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- 7Slip Detection in Robotic Hands with Flexible Parts9 citations · 2013
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- 10Slip Detection in a Novel Tactile Force Sensor5 citations · 2016