Publications by authors named "Daniel Trombetta"

This paper presents a novel, safe tracking control design method that learns the parameters of an uncertain Euler-Lagrange (EL) system online using adaptive learning laws. A barrier function (BF) is first used to transform the full-state constrained EL-dynamics into an equivalent unconstrained dynamics. An adaptive tracking controller is then developed along with the parameter update law in the transformed state space such that the states remain bounded for all time within a prescribed bound.

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Article Synopsis
  • - The article explores estimation and control challenges in cyberphysical human systems (CPHSs), particularly in human-robot collaboration (HRC) within manufacturing settings, where human safety and comfort are critical.
  • - It highlights the importance of considering human factors in robot control to enhance operation efficiency and discusses methods for inferring human intentions using skeletal tracking and gaze data.
  • - Two main challenges addressed are: 1) interpreting a human's intent from their movement data and 2) designing a controller that ensures the robot's motion remains within safe boundaries using control barrier functions.
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