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Se-Hee Ryu

dblp:71/1158 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

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 · 87% Robot manipulation · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control › sliding mode control
chattering elimination
0.012001
Fuzzy Logic Based Tuning of Sliding Mode Controller for Robot Trajectory Control · ICRA 2001
Robotics › Motion planning and robot control › robot control
sliding mode control
0.012001
Fuzzy Logic Based Tuning of Sliding Mode Controller for Robot Trajectory Control · ICRA 2001

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

sliding mode control · 0.0fuzzy logic · 0.0
YearPublicationVenuePosition
2001 Fuzzy Logic Based Tuning of Sliding Mode Controller for Robot Trajectory Control
abstract
Sliding control guarantees system performance in the presence of modeling uncertainties and parameter inaccuracies. However this is obtained at the price of high control activity that may exacerbate chattering. As one way to alleviate this problem a continuation method using a boundary layer around switching is usually used. In this case selecting the control bandwidth and boundary layer thickness is a crucial problem for the trade-off between sliding performance and chattering. The parameter tuning is usually done by trial-and-errors in practice causing significant effort and time. An auto-tuning method based on fuzzy rules is suggested. To this end, the tracking error index and chattering index are defined first and then the fuzzy rules are designed to compromise the error and chattering indices. To demonstrate the efficiency of the proposed method a robotic control system is simulated and tested. It is shown that the proposed algorithm is effective to facilitate the parameter tuning for sliding mode controllers.
Se-Hee Ryu, Jahng-Hyon Park
ICRA1