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A Precisely Predefined-Time Convergent Barrier RNN for Collaborative Position and Orientation Control of Dual-Arm

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    This study introduces a new control scheme for dual-arm robots, enabling precise control of end-effector positions. A novel neural network ensures rapid, accurate convergence even with noise, validating its effectiveness in real-world applications.

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    Area of Science:

    • Robotics
    • Control Systems
    • Artificial Intelligence

    Background:

    • Dual-arm robots require precise control for tasks like object manipulation.
    • Real-world robotic systems face challenges from noise and time constraints.
    • Existing control methods may not meet stringent performance requirements.

    Purpose of the Study:

    • To propose a novel collaborative position and orientation control scheme (CPOCS) for dual-arm robots.
    • To develop a control strategy that ensures high-precision position control while maintaining orientation.
    • To address real-time control challenges including unknown bounded noise and strict time responses.

    Main Methods:

    • Introduction of a precisely predefined-time convergent barrier recurrent neural network (PCB-RNN).
    • Development of a novel piecewise barrier evolution formula for the PCB-RNN.
    • Theoretical analysis to prove the convergence capabilities under various conditions.
    • Simulation and physical experiments on dual-arm robot platforms.

    Main Results:

    • The proposed CPOCS achieves high-precision end-effector positioning.
    • The PCB-RNN demonstrates precisely predefined-time convergence (PPTC) under unknown bounded noise.
    • Validation of the control scheme's effectiveness through extensive simulations and experiments.
    • The PCB-RNN outperforms existing recurrent neural networks in convergence speed and accuracy.

    Conclusions:

    • The novel CPOCS is effective for dual-arm robot control.
    • The PCB-RNN offers advanced PPTC capabilities, robust to noise.
    • The proposed methods provide a reliable solution for practical robotic control applications.