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.
Ihadadene et al. (Wed,) studied this question.