Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Craig R. Carnigan

dblp:231/3160 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 1990
—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 · 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 › adaptive control
adaptive tracking control
0.011990
Adaptive tracking for complex systems using reduced-order models · ICRA 1990
Robotics › Motion planning and robot control › dynamic modeling
reduced-order model
0.011990
Adaptive tracking for complex systems using reduced-order models · ICRA 1990
Robotics › Motion planning and robot control
robot control
0.011990
Adaptive tracking for complex systems using reduced-order models · ICRA 1990

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

parameter adaptive control · 0.0
YearPublicationVenuePosition
1990 Adaptive tracking for complex systems using reduced-order models
abstract
Reduced-order models are considered in the context of parameter adaptive controllers for tracking workspace trajectories. A dual-arm manipulation task is used to illustrate the methodology and provide simulation results. A parameter adaptive controller is designed to track a payload trajectory using a four-parameter model instead of the full-order, nine-parameter model. Several simulations with different payload-to-arm mass ratios are used to illustrate the capabilities of the reduced-order model in tracking the desired trajectory. The adaptive controller was able to reduce tracking errors substantially over the constant gain approach for cases where the payload began to dominate the system dynamics. This meant that even when only slight perturbations to the four-parameter model used by the controller were present, the adaptive controller could reduce the tracking errors significantly, as if a full-order model were being used.>
Craig R. Carnigan
ICRA1