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    Area of Science:

    • Control Theory
    • Systems Science
    • Artificial Intelligence

    Background:

    • Investigates robust consensus tracking in uncertain fractional-order multiagent systems (FOMAS).
    • Addresses challenges posed by heterogeneous nonlinearities, external disturbances, and unknown leader inputs.
    • Considers second-order multiagent systems as a special case.

    Purpose of the Study:

    • To develop novel distributed robust adaptive algorithms for FOMAS.
    • To ensure exponential convergence of consensus tracking error to zero.
    • To extend results to switching topologies and mitigate controller chattering.

    Main Methods:

    • Application of the fractional Lyapunov direct method.
    • Design of discontinuous and continuous neural network-based (NN-based) distributed robust adaptive algorithms.
    • Utilizes multiple Lyapunov functions for switching topologies.

    Main Results:

    • Discontinuous NN-based algorithm guarantees exponential convergence of consensus tracking error under fixed topology.
    • Continuous NN-based algorithm achieves uniformly ultimately bounded error, reducible to desired levels, eliminating chattering.
    • All algorithms are fully distributed, requiring no global information.

    Conclusions:

    • The proposed NN-based algorithms effectively solve the robust consensus tracking problem for uncertain FOMAS.
    • The methods are validated through numerical simulations, demonstrating correctness and robustness.
    • The study contributes advanced control strategies for complex multiagent systems.