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A. Timchenko

dblp:144/5070 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1992
—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 · 91% Robot manipulation · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control › flexible manipulator control
flexible joint robot control
0.011992
Control of robots with elastic joints: deterministic observer and Kalman filter approach · ICRA 1992
Robotics › Motion planning and robot control › robot control
observer-based control
0.011992
Control of robots with elastic joints: deterministic observer and Kalman filter approach · ICRA 1992
Robotics › Motion planning and robot control
robot control
0.011992
Control of robots with elastic joints: deterministic observer and Kalman filter approach · ICRA 1992
Robotics › Robot manipulation
manipulator modeling
0.011992
Control of robots with elastic joints: deterministic observer and Kalman filter approach · ICRA 1992

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

kalman filter · 0.0deterministic observer · 0.0
YearPublicationVenuePosition
1992 Control of robots with elastic joints: deterministic observer and Kalman filter approach
abstract
The authors extend the idea of a fourth-order model of an elastic link and introduce two complementary ways of controlling such systems. In the case of measurable joint forces/torques, they propose a control law based on a deterministic observer whose job is to recover immeasurable components of the state vector. If the forces/torques are not measurable, they propose a Kalman filter technique which uses the manipulator's model to recover the unknown states of the system. Simulation results are presented. The example used is a 6-link manipulator, UMS-2. The results encourage implementation of these control structures on a real system.>
A. Timchenko, Nenad Kircanski
ICRA1