The speed control model of ultrasonic motor is the basis of its motion control research. In order to study the ultrasonic motor speed control model with drive frequency as the regulating variable, a second-order linear time-invariant model for ultrasonic motor speed control is established based on the measured data between ultrasonic motor drive frequency and speed. Iterative learning identification is used to recognize the parameters of the motor speed control model. The parameter learning law for iterative learning identification is designed by double-parametric optimality theory for the case of weakened parameter convergence caused by different amounts of data in each set of real measurement data. The results obtained from the iterative learning identification are compared with the Hammerstein model. Simulation and experimental results show that the iterative learning identification can effectively identify the model parameters of the ultrasonic motor. The parameters converge quickly and well, the accuracy of the constructed model is high and the modeling method is effective.