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Volume 12 Issue 6
Jun.  2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  >  12(6): 1209-1220
C. Ma and D. Dong, “Non-singular practical fixed-time prescribed performance adaptive fuzzy consensus control for multi-agent systems based on an observer,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 6, pp. 1209–1220, Jun. 2025. doi: 10.1109/JAS.2024.124428
Citation: C. Ma and D. Dong, “Non-singular practical fixed-time prescribed performance adaptive fuzzy consensus control for multi-agent systems based on an observer,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 6, pp. 1209–1220, Jun. 2025. doi: 10.1109/JAS.2024.124428
shu

Non-Singular Practical Fixed-time Prescribed Performance Adaptive Fuzzy Consensus Control for Multi-Agent Systems Based on an Observer

doi: 10.1109/JAS.2024.124428
Funds:  This work was supported by the National Natural Science Foundation of China (62203356)
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    Abstract
  • In this paper, the problem of non-singular fixed-time control with prescribed performance is studied for multi-agent systems characterized by uncertain states, nonlinearities, and non-strict feedback. To mitigate the nonlinearity, a fuzzy logic algorithm is applied to approximate the intrinsic dynamics of the system. Furthermore, a fuzzy logic system state observer based on leader state information is designed to address the partial unobservability of followers. Subsequently, the power integral method is incorporated into the backstepping approach to avoid singularities in the fixed-time controller. A command filter method is introduced into the standard backstepping approach to reduce the computational complexity of controller design. Then, a non-singular fixed-time adaptive control strategy with prescribed performance is proposed by constraining the tracking error within a prescribed range. Rigorous theoretical analysis ensures the convergence of consensus error in the multi-agent system to the prescribed performance region within a fixed time. Finally, the practicality of the algorithm is validated through numerical simulations.

     

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