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Preassigned Time Adaptive Neural Tracking Control for Stochastic Nonlinear Multiagent Systems With Deferred

Xiyue Guo, Huaguang Zhang, Jiayue Sun

    IEEE Transactions on Neural Networks and Learning Systems
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    Summary

    This study presents a novel control strategy for stochastic multiagent systems (MASs) to achieve adaptive tracking control under deferred constraints. The method ensures prescribed performance, enhancing system stability and reliability.

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    Area of Science:

    • Control Theory
    • Systems Engineering
    • Applied Mathematics

    Background:

    • Stochastic multiagent systems (MASs) present complex control challenges due to inherent randomness and interconnected dynamics.
    • Deferred full state constraints and prescribed performance requirements complicate the design of effective control strategies.

    Purpose of the Study:

    • To develop a preassigned time adaptive tracking control scheme for stochastic MASs.
    • To address challenges posed by deferred full state constraints and deferred prescribed performance.
    • To ensure that the system achieves tracking control within a specified time frame.

    Main Methods:

    • A modified nonlinear mapping with shift functions is employed to handle initial condition constraints.
    • A Lyapunov function is co-designed using shift and fixed-time prescribed performance functions.
    • Neural networks are utilized to approximate unknown nonlinear terms in the system.
    • A preassigned time adaptive tracking controller is constructed using local information.

    Main Results:

    • The proposed nonlinear mapping effectively circumvents feasibility conditions of full state constraints.
    • The developed controller achieves deferred prescribed performance for stochastic MASs.
    • The controller operates effectively using only locally available information from the agents.
    • Numerical simulations validate the effectiveness of the proposed control scheme.

    Conclusions:

    • The study successfully demonstrates a robust preassigned time adaptive tracking control for stochastic MASs.
    • The methodology provides a viable solution for systems with deferred constraints and performance requirements.
    • The approach enhances the practical applicability of control theory in complex multiagent systems.