Demo Data from SIGIR 2026 / reviewers in the wild / expert
Se-Hee Ryu
dblp:71/1158
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot control › sliding mode control
chattering elimination |
0.0 | 1 | 2001 | 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 |
0.0 | 1 | 2001 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Fuzzy Logic Based Tuning of Sliding Mode Controller for Robot Trajectory ControlabstractSliding 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 |
ICRA | 1 |