To address the degradation of endeffector accuracy in supernumerary robotic arms (SRAs) caused by human motion disturbances during humanrobot collaboration, we propose a compensation control method based on modeling and prediction of these disturbances. First, we design a sensory scheme using T265 visualinertial odometry, which integrates an inertial measurement unit (IMU) with visual estimation to accurately measure humaninduced disturbances. Next, we develop a kinematic model of the humanSRA system, expressing the endeffector pose as a function of both human motion disturbances and SRA joint movements. The control objective is to maintain a stable endeffector pose, and for this, we develop a disturbance compensation strategy using feedforward proportionalintegralderivative (PID) control. To further enhance the compensation control response speed, we propose a predictive approach utilizing a Kalman filter to estimate human motion disturbances. The Kalman filter algorithm is used to accurately predict human motion trajectories by formulating a statespace equation for human motion. Finally, we conduct experiments on both human motion disturbance prediction and disturbance compensation control. Experimental results show that the absolute error between predicted and actual disturbances is 048±032 mm. A comparison of compensation performance with and without prediction shows that the proposed method reduces the absolute error of the SRA′s endeffector on the working plane from 318±217 mm to 123±091 mm. These findings confirm that the proposed compensation control strategy effectively improves endeffector accuracy, with the Kalman filterbased prediction method significantly reducing control delay.