In view of the nonlinearity and parameter timevarying of the hydraulic looper system, the dynamic model of the hydraulic looper system was established and the reversibility of the model was analyzed by using the Interactor algorithm. The neural network was trained by using input and output data of the looper system. The inverse system of α-order neural network and the looper system were combined in series to form a pseudo linear system, and the PSO-PID controller was used to form a closedloop control loop. The results show that the method based on neural network inverse control can achieve good decoupling effect, and avoid the influence of unmodeled of system model and time-varying parameters on decoupling effect. By using the method, it can effectively avoid the shortcomings that the inverse decoupling control depends on the system model and is sensitive to the system model’ s variable parameters, and has strong robustness.