Aiming at the problem of low tension control accuracy existing in the warp yarn tension control of carbon fiber corner link loom, a warp yarn tension control method considering the effect of beating-up was proposed. Firstly, the elongation of the warp yarn during beating-up was analyzed, and then a new tension control model of the warp feeding system was established by combining Hooke's law with the existing tension model. Secondly, a command filter backstepping sliding mode controller was proposed, which estimated the first-order differentiation of the virtual control law through the filter to avoid the problem of "differential expansion", and adopted the radial basis function(RBF) neural network to adaptively estimate the unmodeled part of the system, and at the same time, the sliding mode control was introduced to enhance the robustness of the system. Finally, MATLAB/SIMULINK software was used to carry out simulation experiments on the tension system. The results show that the filtered backstepping sliding mode control considering the effect of beating-up in tension control compared with the traditional backstepping sliding mode control in the case of similar response time, the stabilization time is shortened by nearly 16.3%, the amount of overshooting is reduced by 24.6%. Compared with the fuzzy proportional integral derivative(PID) control, the stabilization time is shortened by 51.7%, the amount of overshooting is reduced by 49.2%.