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Toshiya Maki

dblp:272/5418 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · unresolved

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 · 83% Legged, aerial and field robots · 17%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
adaptive control
0.412020
Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During Flight · ICRA 2020
Robotics › Motion planning and robot control › robot control › adaptive control
model reference adaptive control
0.412020
Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During Flight · ICRA 2020
Robotics › Motion planning and robot control
robot control
0.412020
Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During Flight · ICRA 2020
Robotics › Legged, aerial and field robots
aerial robots
0.112020
Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During Flight · ICRA 2020
Robotics › Legged, aerial and field robots › aerial robots › rotorcraft
multirotor
0.112020
Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During Flight · ICRA 2020

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

model reference adaptive control · 0.4MIMO control · 0.4
YearPublicationVenuePosition
2020 Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During Flight
abstract
Carrying payloads in air is a major mission for multirotor aerial robot. However, the presence of payloads on multirotor aerial robot has a risk of degrading the performance of the flight controller. This concern becomes obvious especially when carrying objects not securely attached to the body or performing aerial manipulation. Therefore, controller with the ability to adapt itself to the effects of payloads on flight stability is needed. This paper proposes a novel nonlinear multiple-input and multiple-output (MIMO) model reference adaptive control (MRAC) system for attitude control of multirotor aerial robots which can dynamically compensate change in the position of center of gravity and inertia caused by payloads. Stability and robustness of the controller are experimentally confirmed in quadrotor and transformable multirotor, and experiments modeling practical applications are conducted for each aerial robot system, proving the utility of the controller.
Toshiya Maki, Moju Zhao, Fan Shi 0002, Kei Okada, Masayuki Inaba
ICRA1