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    This study introduces a new neuroadaptive control strategy for robotic manipulators that removes complex feasibility conditions. The developed method effectively handles position and velocity constraints, improving user-friendliness in control development.

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    Area of Science:

    • Robotics and Control Systems
    • Artificial Intelligence in Automation

    Background:

    • Existing adaptive constrained robotic control algorithms often require strict "feasibility conditions" and limitations on constraining functions.
    • These requirements make current control development methods complex and less user-friendly for robotic manipulators.

    Purpose of the Study:

    • To develop a novel neuroadaptive constrained control strategy for uncertain robotic manipulators.
    • To eliminate the need for "feasibility conditions" and simplify control development.
    • To accommodate various forms of position and velocity constraints without additional limitations.

    Main Methods:

    • Construction of a novel unified mapping function (UMF) to remove restrictions on constraining boundaries.
    • Integration of UMF-based coordinate transformation with neural network universal approximation properties.
    • Development of a neuroadaptive control that bypasses traditional "feasibility conditions".

    Main Results:

    • The proposed strategy successfully obviates complex and undesired "feasibility conditions".
    • Demonstrated that all closed-loop signals remain semiglobally bounded.
    • Verified that position and velocity constraints are consistently maintained.

    Conclusions:

    • The new neuroadaptive constrained control strategy offers a more user-friendly and effective approach for robotic manipulators.
    • The method's effectiveness was validated using a two-link rigid robotic manipulator, confirming its practical applicability.