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

Wuichung Cheng

dblp:381/6310 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
2since 2021 · last 2024
0009-0008-3943-4603ORCID · corroborated

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

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
2 papers
Motion planning and robot control · 56% Robot manipulation · 44%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › parallel manipulator
cable-driven parallel robot
1.522024
Deformable Open-Frame Cable-Driven Parallel Robots: Modeling, Analysis, and Control · IEEE Trans. Robotics 2024
Iterative Learning Control for Deformable Open-Frame Cable-Driven Parallel Robots · ICRA 2024
Robotics › Motion planning and robot control › robot control › learning control
iterative learning control
0.812024
Iterative Learning Control for Deformable Open-Frame Cable-Driven Parallel Robots · ICRA 2024
Robotics › Motion planning and robot control
robot control
0.812024
Iterative Learning Control for Deformable Open-Frame Cable-Driven Parallel Robots · ICRA 2024
Robotics › Motion planning and robot control › robot control
trajectory tracking
0.212024
Iterative Learning Control for Deformable Open-Frame Cable-Driven Parallel Robots · ICRA 2024
Robotics › Motion planning and robot control
workspace analysis
0.212024
Deformable Open-Frame Cable-Driven Parallel Robots: Modeling, Analysis, and Control · IEEE Trans. Robotics 2024

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

model-based feedforward control · 0.8iterative learning control · 0.8inverse kinematics · 0.8euler-bernoulli beam equations · 0.8
YearPublicationVenuePosition
2024 Iterative Learning Control for Deformable Open-Frame Cable-Driven Parallel Robots
abstract
This paper proposed an iterative learning control (ILC) scheme for deformable open-frame cable-driven parallel robots (D-CDPRs). In contrast to the straightforward inverse kinematics of the rigid frame cable-driven parallel robots (CDPRs), accurate modeling of the deformable frame poses challenges due to errors and uncertainties. To address these issues, the authors propose the use of ILC, a control strategy that modifies the control input over iterations based on previous results. ILC has been successfully applied to traditional cable robots, particularly in handling model uncertainty. The paper presents a novel ILC control scheme specifically designed for D-CDPRs, with a focus on reducing tracking errors over repetitive operations. Additionally, hardware experiments are conducted to validate the effectiveness and reliability of the proposed ILC approach. The results demonstrate the efficacy of ILC in mitigating tracking errors, even in scenarios where the dynamic model of the D-CDPRs is unknown.
Wuichung Cheng, Arthur Ngo Foon Chan, Darwin Lau
ICRA1
2024 Deformable Open-Frame Cable-Driven Parallel Robots: Modeling, Analysis, and Control
abstract
This article proposes a generalized type of cable-driven parallel robot with deformable frames (D-CDPRs). The class of D-CDPRs allows: first, inevitable deformation of traditional rigid frame CDPRs to be considered; and second, new possibilities to develop CDPRs with lightweight frames that would deform. Comparatively, such lightweight CDPRs are easier to set up and largely reduce the cost of material and construction. However, the analysis and control of D-CDPRs are challenging as existing works usually assume the CDPR frame is rigid, such that the cable exit points on the frame are known and fixed. If the modeling errors induced by the deformable frame are not addressed appropriately, the control performance of D-CDPRs will be inaccurate and even unstable. To tackle this problem, novel modeling, analysis, and control approaches are proposed accordingly for D-CDPRs. Using the Euler–Bernoulli beam equations to develop a D-CDPR model, the workspace analysis is proposed and explored. Furthermore, the model-based feedforward length (MBFL) controller is proposed, where it is shown that cable length can be used to execute the tension control for D-CDPRs. Finally, the proposed work is validated in both simulation and hardware experiments.
Arthur Ngo Foon Chan, Wuichung Cheng, Darwin Lau
IEEE Trans. Robotics2