In the servo press mounting machine for automobile turbine housing bush,the bush often vibrates during the pressing process,which seriously affects the precision and quality of the press fitting.In order to reveal the mechanism of chattering,the extrusion between the bushing and the hole was equivalent to a nonlinear friction process,and the transmission chain between the motor and the end piece was regarded as a link with both elastic and damping characteristics.The dynamic model and the state space expression of the servo press fit process were established.The established model effectively reproduced the actual shaking process.On this basis,the iterative learning algorithm was designed,which did not strictly rely on the accurate mathematical model,and had strong anti-interference.In order to verify the effectiveness of the proposed control algorithm,a numerical simulation model of the press mounting process control system including nonlinear dynamic model and iterative learning controller was constructed.The simulation results show that compared with the traditional PID algorithm,the iterative learning controller has higher positioning accuracy,faster response speed and stronger anti-interference ability.The relevant conclusions provide a theoretical basis for the application of iterative learning control in servo press fit.