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Rujing Zhou

dblp:28/2948 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—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 › model-based control
reduced-order model control		 0.012001
Robust Nonlinear Reduced-Order Dynamic Controller Design and its Application to a Single-link Manipulator · ICRA 2001
Robotics › Motion planning and robot control
robot control		 0.012001
Robust Nonlinear Reduced-Order Dynamic Controller Design and its Application to a Single-link Manipulator · ICRA 2001
Robotics › Motion planning and robot control › robot control › robust control
robust nonlinear control		 0.012001
Robust Nonlinear Reduced-Order Dynamic Controller Design and its Application to a Single-link Manipulator · ICRA 2001
Robotics › Motion planning and robot control
manipulator control		 0.012001
Robust Nonlinear Reduced-Order Dynamic Controller Design and its Application to a Single-link Manipulator · ICRA 2001

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

robust control · 0.0lyapunov stability · 0.0dynamic feedback control · 0.0
YearPublicationVenuePosition
2001 Robust Nonlinear Reduced-Order Dynamic Controller Design and its Application to a Single-link Manipulator
abstract
We focus on the design of robust reduced-order dynamic feedback control for a class of nonlinear systems with uncertainties. A robust nonlinear reduced-order dynamic controller (RNRDC) is proposed. The resulting closed-loop system can be robustly stabilized for all admissible uncertainties via the proposed controller. The application of the proposed RNRDC to a permanent magnet brush DC motor driving a single-link manipulator is also considered. The parameters uncertainties can be up to /spl plusmn/80%. The load can vary ranging from 50% to +100% of the nominal value. Our simulation results show that for the example system the proposed RNRDC can achieve good performance.
Jianying Zhou 0002, Rujing Zhou, Youyi Wang
 ICRA2