Fractional-Order Reference Model-Based Adaptive Feedback Linearization for Delta Manipulator

Key Points

Precise trajectory tracking is achieved through an adaptive control mechanism that adjusts parameters in real time, enhancing control stability.
The proposed fractional-order model improves the representation of the robot's dynamics, capturing phenomena often ignored by integer-order models.
By leveraging fractional calculus, the model addresses memory effects and viscoelastic behavior, leading to better performance under uncertainties.
This method may enable enhancements in robotic applications where traditional control approaches struggle with nonlinearity and variability.

Abstract

This paper presents a novel Fractional-Order Model Reference Adaptive Control (FO-MRAC) strategy for the adaptive feedback linearization of a nonlinear Delta parallel robot. By leveraging fractional calculus, the proposed approach enhances both robustness and adaptability in the presence of model uncertainties. The fractional-order framework enables a more accurate representation of the robot’s dynamics by capturing memory effects and viscoelastic behavior phenomena typically neglected in integer-order models. The adaptive control mechanism continuously adjusts the controller parameters in real time, ensuring precise trajectory tracking without requiring exact knowledge of the system model.

Fractional-Order Reference Model-Based Adaptive Feedback Linearization for Delta Manipulator

Key Points

Abstract

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