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Yong Sik Park

dblp:181/4110 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control › observer-based control
disturbance observer-based control
0.212016
Powered upper-limb control using passivity-based nonlinear disturbance observer for unknown payload carrying applications · ICRA 2016
Robotics › Motion planning and robot control › robot control
passivity-based control
0.212016
Powered upper-limb control using passivity-based nonlinear disturbance observer for unknown payload carrying applications · ICRA 2016
Robotics › Motion planning and robot control
robot control
0.212016
Powered upper-limb control using passivity-based nonlinear disturbance observer for unknown payload carrying applications · ICRA 2016

Methods — techniques the papers use, named apart from their topics

passivity · 0.2nonlinear disturbance observer · 0.2gain tuning · 0.2
YearPublicationVenuePosition
2016 Powered upper-limb control using passivity-based nonlinear disturbance observer for unknown payload carrying applications
abstract
This paper proposes a passivity-based nonlinear disturbance observer (DOB) design for a powered upper-limb robot control. The proposed DOB allows for the nonlinearities of the robot dynamics, whereas the typical DOB designs cannot. Moreover, by virtue of the passivity property, human operator and environmental interactions can be embedded in the control loop. As a DOB, the proposed approach has a disturbance observation property that makes the actual robot behave like a nominal model selected by the user. Performance analysis proposes a gain tuning rule. In experimental validation, actual powered upper-limb robot is used to perform unknown payload carrying applications.
Minjun Kim 0003, Woongyong Lee, Jae Yeon Choi, Yong Sik Park, Goobong Chung, Kyung-Lyong Han, Il Seop Choi, Il Hong Suh, Youngjin Choi, Wan Kyun Chung
ICRA4