Abstract
The use of fractional-order methods has shown strong potential in enhancing the stability and performance of systems in recent years. In this article, a fractional-order Kalman filter is designed and applied to the integer-order model of a 2-DOF robotic manipulator to examine its feasibility and effectiveness under disturbances. In addition, the design and application of the proposed Fractional Kalman Filter for state estimation are presented in detail. To establish the robustness of the approach, a rigorous Lyapunov-based stability analysis is conducted, guaranteeing that the estimation error remains bounded under varying conditions and asymptotically converges to zero within a small neighbourhood. The study aims to demonstrate how fractional-order filtering can improve state estimation in the presence of uncertainties and disturbances when compared with the standard integer-order Kalman filter. MATLAB simulation results are provided to illustrate these improvements and to validate the claims when compared to the traditional Kalman Filter.