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G. R. Eisler

dblp:230/7038 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1991
—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 · 87% Robot manipulation · 13%

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 › optimal control
minimum-time trajectory control
0.011991
Minimum-time trajectory control of a two-link flexible robotic manipulator · ICRA 1991
Robotics › Motion planning and robot control › robot control › trajectory tracking
trajectory control
0.011991
Minimum-time trajectory control of a two-link flexible robotic manipulator · ICRA 1991
Robotics › Robot manipulation
flexible manipulator
0.011991
Minimum-time trajectory control of a two-link flexible robotic manipulator · ICRA 1991

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

quadratic cost criterion · 0.0finite element model · 0.0feedback control · 0.0
YearPublicationVenuePosition
1991 Minimum-time trajectory control of a two-link flexible robotic manipulator
abstract
An analysis is made of the experimental results of a minimum-time trajectory control scheme for a two-link flexible robot. An offline optimization routine determines a minimum-time, straight-line tip trajectory which stays within the torque constraints of the motors and ends with no vibrational transients. An efficient finite-element model is used in the optimization to approximate the flexible arm dynamics. The control strategy described is used to determine the feedback gains for the position, velocity, and strain gage signals from a quadratic cost criterion based on the finite-element model linearized about the straight-line tip trajectory. These feedback signals are added to the modeled torque values obtained from the optimization routine and used to control the robot arm actuators. The results indicate that this combination of model-based and error-driven control strategies, achieves a closer tracking of the desired trajectory and a better handling of modeling errors than either strategy alone.>
David A. Schoenwald, John T. Feddema, G. R. Eisler, Daniel J. Segalman
ICRA3