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    This study introduces a new adaptive tracking control for nonlinear multiagent systems with state constraints. The integral barrier Lyapunov functional method ensures follower outputs match the leader while respecting state bounds.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear Dynamics
    • Networked Systems

    Background:

    • Multiagent systems often face challenges with state constraints and communication errors.
    • Existing barrier Lyapunov functions can be overly conservative, limiting their applicability.
    • Adaptive control is crucial for handling uncertainties in nonlinear systems.

    Purpose of the Study:

    • To develop an adaptive tracking control scheme for nonlinear multiagent systems with state constraints.
    • To introduce integral barrier Lyapunov functionals (iBLFs) to relax conservatism and handle state constraints.
    • To ensure follower systems track the leader's trajectory while maintaining state variable bounds.

    Main Methods:

    • Utilized integral barrier Lyapunov functionals (iBLFs) to address state constraints and coupling errors.
    • Designed an adaptive distributed controller using the backstepping method and iBLF differentiation via the integral mean value theorem.
    • Employed neural networks to approximate unknown system terms and Lyapunov stability theory for system analysis.

    Main Results:

    • The proposed control scheme successfully ensures that all follower outputs track the leader's output trajectory.
    • State variables of the agents remain within the predefined constraint bounds.
    • All closed-loop signals within the multiagent system are proven to be bounded.

    Conclusions:

    • The novel adaptive tracking control scheme effectively manages nonlinear multiagent systems with state constraints.
    • The integral barrier Lyapunov functional approach offers a less conservative and more feasible solution compared to traditional methods.
    • The controller's efficiency is validated, demonstrating its practical applicability in complex networked systems.