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Michael Drossakis

dblp:151/9499 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—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 · 100%

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 › sensor-based control › visual servoing
image-based visual servoing
0.212014
Robustness analysis of model predictive control for constrained Image-Based Visual Servoing · ICRA 2014
Robotics › Motion planning and robot control › robot control
model predictive control
0.212014
Robustness analysis of model predictive control for constrained Image-Based Visual Servoing · ICRA 2014
Robotics › Motion planning and robot control › robot control › robust control
robustness analysis
0.212014
Robustness analysis of model predictive control for constrained Image-Based Visual Servoing · ICRA 2014
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing
0.212014
Robustness analysis of model predictive control for constrained Image-Based Visual Servoing · ICRA 2014

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

nonlinear model predictive control · 0.2input-to-state stability analysis · 0.2
YearPublicationVenuePosition
2014 Robustness analysis of model predictive control for constrained Image-Based Visual Servoing
abstract
In this paper, robustness analysis of constrained Image Based Visual Servoing based on Nonlinear Model Predictive Control (NMPC) is presented. It is known, that real applications such an aerial or a fast underwater robotic systems, suffer from the presence of external disturbances. These kinds of disturbances are inevitable in the physical systems, so it is of great interest to employ robust controllers. Therefore, a rigorous robustness analysis should be conducted. In this paper, the Image Based Visual Servoing system under the MPC framework is proven to be Input-to-State Stable (ISS) and a permissible upper bound of the disturbances is provided. Finally, the validity of the theoretic results is illustrated through a simulated example.
Shahab Heshmati-Alamdari, George K. Karavas, Alina Eqtami, Michael Drossakis, Kostas J. Kyriakopoulos
ICRA4