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Yonghao Xie

dblp:182/6399 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0002-0215-6303ORCID · corroborated

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

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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
1 paper
Multi-agent systems · 100%
Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems › consensus
distributed consensus
0.912025
Embedding prescribed-time adaptive control protocol unveiling distributed consensus in multirobot systems via directed topology · Sci. China Inf. Sci. 2025
Knowledge, reasoning and agents › Multi-agent systems
multi-robot systems
0.912025
Embedding prescribed-time adaptive control protocol unveiling distributed consensus in multirobot systems via directed topology · Sci. China Inf. Sci. 2025
Mathematical optimization
distributed optimization
0.312025
Embedding prescribed-time adaptive control protocol unveiling distributed consensus in multirobot systems via directed topology · Sci. China Inf. Sci. 2025

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

prescribed-time adaptive control · 1.7
YearPublicationVenuePosition
2025 Embedding prescribed-time adaptive control protocol unveiling distributed consensus in multirobot systems via directed topology
Yonghao Xie, Xinru Ma, Shaorong Xie
Sci. China Inf. Sci.1
2025 Adaptive Dynamics-Based Prescribed-Time Control for Robots Formation Tracking in Task Space
abstract
This article investigates the prescribed-time formation control in the task space of multirobot systems (MRSs), which is subject to the uncertain nonlinear dynamics and the position requirements. The strategy constructs a cascade system consisting of control and reference layers by connections of coupled prescribed-time control units, which separately guarantee the convergence of coordination errors, accuracy of states, and synchronization between layers. Meanwhile, the transformation from task space to joint space is built based on the pseudo-inverse of the Jacobi matrix, which avoids the singular value problem brought by computing the inverse kinematics. The adaptive control method utilizes parameter estimation to eliminate the inaccuracy problem brought by the pseudo-inverse of Jacobi matrix transformation. Then, this article provides solutions to the formation control and the formation along the trajectory control. Correspondingly, the Lyapunov analysis process proves the system’s stability and the parameter estimation’s boundedness, which confirms the sufficient conditions for realizing the prescribed-time formation control of the MRSs. Finally, this article presents examples of time-varying formation and along-trajectories formation, thereby demonstrating the effect of the controller.
Xinru Ma, Yonghao Xie, Jun Liu 0007, Yan Peng 0001, Shaorong Xie, Jun Luo 0006
IEEE Trans. Syst. Man Cybern. Syst.3