Robotic arms are becoming increasingly popular in industrial applications. However, improving the response and accuracy of robotic arms while reducing their cost has become challenging. The Kalman Filter (KF) has attracted a significant amount of research as it improves the control quality by filtering the feedback signal. On the other hand, KF solution becomes very challenging when the system under study is nonlinear. This work proposes a new online state estimation algorithm that combines the Smooth Variable Structure Filter (SVSF) with the Unscented Kalman Filter (UKF). The proposed method overcomes the limitations of SVSF and UKF in terms of stability and sensitivity to noise. A simulation study is conducted in this paper to demonstrate the results of the proposed method when applied to estimate the states of a PRRR industrial robotic arm.

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