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    Area of Science:

    • Control Engineering
    • Artificial Intelligence
    • Robotics

    Background:

    • Multiagent systems (MASs) face challenges in achieving coordinated control, especially with unknown dynamics and communication delays.
    • Ensuring collision avoidance and maintaining network connectivity are critical for safe and effective MAS operation.

    Purpose of the Study:

    • To investigate adaptive fuzzy collision-free formation control for uncertain nonlinear MASs with communication delays.
    • To develop a control strategy that guarantees formation objectives, collision avoidance, and topology connectivity.

    Main Methods:

    • Fuzzy logic systems (FLSs) are used to model unknown agent dynamics.
    • A navigation function establishes collision avoidance and connectivity preservation criteria.
    • A distributed command governor estimates unknown leader states and derivatives.
    • Backstepping control design theory formulates the adaptive fuzzy formation control approach.

    Main Results:

    • The developed adaptive fuzzy formation control approach successfully achieves formation objectives.
    • Collision avoidance between agents is ensured.
    • Network topology connectivity is preserved throughout the operation.
    • Effectiveness is validated through simulations on multiple Euler-Lagrangian (EL) systems.

    Conclusions:

    • The proposed adaptive fuzzy control method effectively addresses collision-free formation control for uncertain nonlinear MASs under communication delays.
    • The integration of fuzzy logic, navigation functions, and command governors provides a robust solution for complex MAS coordination.
    • The method demonstrates practical applicability and effectiveness in real-world scenarios, as shown with EL systems.