Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 12 min ago
Peer-reviewedCC BY 4.0Mathematics

A Fuzzy Gain-Scheduled Time Base Generator for Fixed-Time Leader-Following Tracking Consensus of Feedforward Nonlinear Multi-Agent Systems

  • Fengyi Liu1
  • Mingquan Du1
  • Ming Zhao1
  • Wei Li2
  1. 1Wuxi Key Laboratory of Artificial Intelligence and Security, School of Cyber Science and Engineering, Wuxi University, Wuxi 214105, China
  2. 2School of Artificial Intelligence and Computer Science & Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education, Jiangnan University, Wuxi 214122, China
Open at sourceDOICite

Abstract

This paper proposes a fuzzy gain-scheduled time base generator (FGS-TBG) framework for prescribed fixed-time leader-following consensus of Lipschitz feedforward nonlinear multi-agent systems (FNMASs) with a dynamic leader under distributed output feedback. Existing TBG-based fixed-time consensus methods mostly rely on static leader assumptions and constant tuning parameters, limiting their adaptability to dynamic reference trajectories and causing redundant control energy consumption; meanwhile, most are designed for integrator-type or linear systems, leaving a notable research gap for FNMASs. To address the static-leader limitation, we first develop a smooth TBG-based distributed observer and a distributed output-feedback consensus protocol for FNMASs with a dynamic leader. Our scheme accommodates a user-defined nonzero leader control input and guarantees prescribed fixed-time leader-following tracking consensus independent of the initial conditions. To boost the adaptability of fixed-parameter TBG schemes, we further develop an FGS-TBG strategy for online intelligent tuning of TBG gains. Instead of approximating unknown nonlinear dynamics as in traditional adaptive fuzzy control, the fuzzy system only schedules TBG gains, retaining all inherent theoretical characteristics of TBG while enhancing adaptability and cutting redundant control energy. We establish the fixed-time convergence of the closed-loop system through Lyapunov analysis. Comparative simulations verify that the proposed framework achieves superior adaptability and lower control cost while maintaining the prescribed fixed-time performance.

Related

  1. Practical Predefined-Time Safe Resilient Consensus of Heterogeneous Nonlinear Fractional-Order Multi-Agent Systems Under Hybrid Cyber Attacks
    Laixin Gao, Lingchun Li, Guangming Zhang
    Fractal and FractionalPeer-reviewedCC BY 4.0Analysis & PDEsCrossref
  2. Accelerating Nonlinear MPC by Behavior Cloning with Separated Timescales: Application to Nuclear Reactor Load-Following
    Perceval Beja-Battais, Alain Grossetête, Nicolas Vayatis
    HALPreprintCC BY-NC-ND 4.0Full textOptimisation & controlHALarXiv
  3. Periodically kicked feedforward chains of simple excitable FitzHugh–Nagumo neurons
    Benjamin Ambrosio, Stanislav Mintchev
    Nonlinear DynamicsPeer-reviewedCC BY 4.0Full text10 citedGeneral mathematicsStatistical & nonlinear physicsHAL
  4. Gain-Scheduled H∞ Attitude Control of a 6U CubeSat Under Inertia Uncertainty and Actuator Constraints
    Aslı Durmuşoğlu
    MathematicsPeer-reviewedCC BY 4.0General mathematicsCrossref
  5. A concentration result for multilayer feedforward neural networks
    Vera Koponen
    arXivPreprintarXiv licenceFull textLogic & foundationsProbabilityarXiv

Cite

BibTeXRIS
BibTeX
@article{liu2026fuzzy,
  title = {{A Fuzzy Gain-Scheduled Time Base Generator for Fixed-Time Leader-Following Tracking Consensus of Feedforward Nonlinear Multi-Agent Systems}},
  author = {Fengyi Liu and Mingquan Du and Ming Zhao and Wei Li},
  journal = {Mathematics},
  year = {2026},
  month = oct,
  doi = {10.3390/math14203668},
  url = {https://www.mdpi.com/2227-7390/14/20/3668},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A Fuzzy Gain-Scheduled Time Base Generator for Fixed-Time Leader-Following Tracking Consensus of Feedforward Nonlinear Multi-Agent Systems
AU  - Fengyi Liu
AU  - Mingquan Du
AU  - Ming Zhao
AU  - Wei Li
PY  - 2026
DA  - 2026/10/10
JO  - Mathematics
DO  - 10.3390/math14203668
UR  - https://www.mdpi.com/2227-7390/14/20/3668
AB  - This paper proposes a fuzzy gain-scheduled time base generator (FGS-TBG) framework for prescribed fixed-time leader-following consensus of Lipschitz feedforward nonlinear multi-agent systems (FNMASs) with a dynamic leader under distributed output feedback. Existing TBG-based fixed-time consensus methods mostly rely on static leader assumptions and constant tuning parameters, limiting their adaptability to dynamic reference trajectories and causing redundant control energy consumption; meanwhile, most are designed for integrator-type or linear systems, leaving a notable research gap for FNMASs. To address the static-leader limitation, we first develop a smooth TBG-based distributed observer and a distributed output-feedback consensus protocol for FNMASs with a dynamic leader. Our scheme accommodates a user-defined nonzero leader control input and guarantees prescribed fixed-time leader-following tracking consensus independent of the initial conditions. To boost the adaptability of fixed-parameter TBG schemes, we further develop an FGS-TBG strategy for online intelligent tuning of TBG gains. Instead of approximating unknown nonlinear dynamics as in traditional adaptive fuzzy control, the fuzzy system only schedules TBG gains, retaining all inherent theoretical characteristics of TBG while enhancing adaptability and cutting redundant control energy. We establish the fixed-time convergence of the closed-loop system through Lyapunov analysis. Comparative simulations verify that the proposed framework achieves superior adaptability and lower control cost while maintaining the prescribed fixed-time performance.
SN  - 2227-7390
ER  -