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    Area of Science:

    • Robotics
    • Control Systems
    • Artificial Intelligence

    Background:

    • Redundant manipulators require accurate models for trajectory tracking.
    • Model-based control fails when manipulator models are unknown.
    • Data-driven approaches are needed for model-unknown systems.

    Purpose of the Study:

    • To develop a data-driven control algorithm for model-unknown redundant manipulators.
    • To enable trajectory tracking without prior kinematic model knowledge.
    • To demonstrate the effectiveness of the proposed algorithm.

    Main Methods:

    • A novel neural dynamics-based model predictive control (NDMPC) algorithm is proposed.
    • The algorithm integrates a model predictive control (MPC) scheme, a neural dynamics (ND) solver, and a discrete-time Jacobian matrix (DTJM) updating law.
    • The DTJM updating law predicts future manipulator outputs for the MPC scheme.

    Main Results:

    • The proposed NDMPC algorithm enables trajectory tracking for model-unknown redundant manipulators.
    • Theoretical analyses confirm the algorithm's convergence.
    • Simulations and experiments validate its feasibility and superiority over existing methods.

    Conclusions:

    • The data-driven NDMPC algorithm successfully achieves trajectory tracking without requiring a kinematics model.
    • This approach offers a robust solution for controlling redundant manipulators with unknown dynamics.
    • The findings advance the field of robotics by enabling model-free control strategies.