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Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning.

Linghuan Kong, Wei He, Chenguang Yang

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    Summary
    This summary is machine-generated.

    This study introduces fuzzy neural network (FNN) control with impedance learning for multiple robots handling an object. The method ensures robots track trajectories while respecting position and velocity constraints in unknown environments.

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    Area of Science:

    • Robotics
    • Control Systems
    • Artificial Intelligence

    Background:

    • Coordinated control of multiple robots is challenging due to unknown dynamics and environments.
    • Ensuring robots adhere to constraints is crucial for safe and effective operation.

    Purpose of the Study:

    • To develop a fuzzy neural network (FNN) control strategy for coordinated multiple robots carrying a common object.
    • To address unknown robotic dynamics and environmental interactions.
    • To implement impedance learning for improved trajectory tracking and constraint satisfaction.

    Main Methods:

    • Fuzzy neural network (FNN) learning algorithm to identify unknown robotic dynamics.
    • Impedance learning to regulate control inputs and enhance robot-environment interaction.
    • Integral barrier Lyapunov functions to enforce position and velocity constraints.

    Main Results:

    • The proposed FNN control effectively identifies unknown plant models.
    • Impedance learning enables robots to track desired trajectories accurately.
    • Integral barrier Lyapunov functions successfully guarantee position and velocity constraints.
    • Lyapunov stability theory confirms uniform boundedness of tracking errors.

    Conclusions:

    • The developed FNN control with impedance learning provides a robust solution for coordinated multi-robot systems.
    • The method ensures precise trajectory tracking and constraint satisfaction even with unknown dynamics and environments.
    • Simulation results validate the effectiveness and reliability of the proposed control strategy.