Abstract: In hot strip rolling mills, loopers are installed between finishing stands to ensure the balance of metal mass flow and the stability of strip tension during the rolling process. Therefore, the control accuracy and dynamic response characteristics of the looper system directly determine the quality of the final strip product. To address the control challenges caused by the multivariable strong coupling, nonlinear and time-varying characteristics of the hydraulic looper system, this paper designs a composite intelligent control strategy that combines an Improved Particle Swarm Optimization(IPSO) algorithm with Back Propagation Neural Networks(BPNN) Proportional-Integral-Derivative(PID). A feedforward compensation decoupling control loop is constructed to weaken the coupling effects in the system. An improved PSO algorithm with dynamic adjustment mechanisms for inertia weights and learning factors is developed to solve problems in traditional BP neural network PID, such as strong randomness in weight initialization, tendency to fall into local optima, and slow convergence speed. Simulation experiments based on the MATLAB/Simulink platform demonstrate that the proposed IPSO-BP-PID algorithm achieves better control performance compared to both BP neural network PID and traditional PID under unit step input signals, verifying the effectiveness of the IPSO-BP-PID algorithm.