For the inherent hysteresis nonlinearity of magnetostrictive materials, this paper presents a composite control method based on CMAC (cerebellar model neural network) feedforward inverse compensation and PID. Firstly, CMAC neural network is used to learn and obtain the hysteresis inverse model of giant magnetostrictive actuator (GMA) for compensation, and then the CMAC model is used to learn and adapt online quickly, and PID controller is used to reduce the error and disturbance during tracking control, so as to realize the precision control of GMA. CMAC feedforward inverse compensation controller and CMAC-PID compound control model are established by MATLAB. Finally, the effectiveness of the proposed method is verified by simulation experiments. The results show that the proposed hysteresis model approximated by CMAC neural network has satisfactory accuracy. Under the action of CMAC-PID composite control scheme, the maximum relative error between the expected displacement and the actual displacement of the system is only 2.39%, and the average relative error is less than 0.5%. It shows that the control strategy can adapt to the nonlinear change of the control object and effectively improve the tracking accuracy of GMA.