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Mohammed Dahleh

dblp:50/10965 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 1996
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 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%
Theoretical computer science
1 paper
Mathematical optimization · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization
control theory		 0.011996
Control of molecular motion · Proc. IEEE 1996
Robotics › Motion planning and robot control › robot control › adaptive control
adaptive gain control		 0.011990
Adaptive gain adjustment for a learning control method for robotics · ICRA 1990
Robotics › Motion planning and robot control › robot control
learning control		 0.011990
Adaptive gain adjustment for a learning control method for robotics · ICRA 1990
Robotics › Motion planning and robot control › robot control
trajectory tracking		 0.011990
Adaptive gain adjustment for a learning control method for robotics · ICRA 1990

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

control theory · 0.0high-gain feedback · 0.0adaptive gain adjustment · 0.0
YearPublicationVenuePosition
1996 Control of molecular motion
abstract
In this paper we present a short introduction to the new field of control of molecular motion. Our intention is to outline how the methods and techniques of control theory play a crucial role in the development of this emerging field, and reciprocally how fundamental new problems are motivated by this interaction.
Mohammed Dahleh, Anthony Peirce, Herschel Rabitz, Viswanath Ramakrishna
 Proc. IEEE1
1990 Adaptive gain adjustment for a learning control method for robotics
abstract
P. Bondi et al. (IEEE Journal of Robotics and Autom., vol.4, p.14-22, Feb. 1988) proposed a high-gain feedback method for learning control and showed that there exist gains which ensure trajectory convergence to a desired response. An adaptive gain-adjustment technique is suggested that enables the system to choose adaptively the gains which yield convergence. The technique is illustrated with the results of a simulation.>
Kevin L. Moore 0001, Mohammed Dahleh, Shankar P. Bhattacharyya
 ICRA2