Cited by 27Distributed Control Multi-Agent SystemsAdaptive Control of Nonlinear SystemsStability and Control of Uncertain Systems

An Adaptive Fault-Tolerant Control Scheme for Heterogeneous Multiagent Systems

Gong, Jianye, Ma, Yajie, Jiang, Bin, Zhang, Youmin, Guo, Li·2025·IEEE Transactions on Systems, Man, and Cybernetics: Systems

Abstract

This article proposes an adaptive fault-tolerant control (FTC) scheme for heterogeneous multiagent systems with time-varying communication link faults and actuator faults. First, the communication link faults, including the channel signal fading and cyber bias attack, is considered, and the communication link fault compensation controller is designed through introducing the adaptive signals with the estimate of the norms of the faulty matrix. Then, by using the minimum eigenvalue of the control gain matrix, the minimum-eigenvalue-based adaptive fault-tolerant controller is proposed to compensate for the time-varying actuator loss of effectiveness and bias faults. Moreover, the convergence performance analysis of the developed FTC algorithm is given based on the Lyapunov theory. The simulation results carried out on the quadrotors-unmanned ground vehicles formation systems validate the effectiveness of the theoretical results.

Integrity signals

Registry

Crossref · 10.1109/tsmc.2024.3496717

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Bioinspired techniques enhance adaptability in swarms and refine precision in manipulators, while supporting uptime in agriculture.

    “Bioinspired techniques, detailed in Section Key Technical Methods in Hybrid FTC, enhance adaptability in swarms [6] and refine precision in manipulators [18], while supporting uptime in agriculture [8].”
  • Hybrid AI methods integrating fuzzy logic, neural networks, and reinforcement learning with a three-layer fusion model improve efficiency in multi-agent human-robot collaboration with moderate overhead.

    “Hybrid Artificial Intelligence (AI) methods integrate fuzzy logic, neural networks, and RL with a three-layer fusion model, improving efficiency in multi-agent HRC [8,41], with moderate overhead [42].”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Gong, J., Ma, Y., Jiang, B., Zhang, Y., & Guo, L.
Affiliations
  • Nanjing University of Aeronautics and Astronautics (CN)
  • Concordia University (CA)
  • Anhui Polytechnic University (CN)
Venue
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Published
2025-02
Volume
55
Issue
2
Pages
1264-1276
ISSN
  • 2168-2216 (ISSN-L)
  • 2168-2232
OpenAlex
W4404809874
Cited by
25
References
36
Citations by year
2026: 15 · 2025: 10
FWCI
4.16
Type
journal article
Language
en
Open access
closed
Locations
IEEE Transactions on Systems Man and Cybernetics Systems (publishedVersion)
Funders
  • National Natural Science Foundation of China (62020106003, 62273177, 62233009)
  • Natural Science Foundation of Jiangsu Province of China (BK20222012, BK20211566)
  • Fundamental Research Funds for the Central Universities (NI2024001)
  • Scientific Start up Fund for Introducing Talents of Anhui Polytechnic University (2022YQQ052)
  • Natural Science Research Project of Anhui Province Universities (2023AH040121)
  • Open Fund of the National Key Laboratory of Helicopter Aeromechanics (2024-ZSJ-LB-02-04)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
  • Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
  • Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
Keywords
adaptive fault-tolerant control, heterogeneous multi-agent systems, actuator faults, fault compensation, unmanned ground vehicles

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review