As the positioning precision of piezo-actuators is always severely deteriorated by hysteresis nonlinear effect, this paper proposes a neural network control scheme with a hysteresis compensator based on sliding-mode technique to improve the performance of the piezo-actuators. A Radial Basic Function Neural Network (RBFNN) was developed as a equivalent control value in the sliding-mode control and the hysteresis compensator was used to estimate the lumped uncertainty caused by the varying parameters in the RBFNN, external disturbance and the approximate algorithm to compensate the output signal of the RBFNN. For the above steps, the dynamics of actuator was guaranteed on the sliding surface. The adaptive tuning laws of the network and the compensator were derived on the basis of Lyapunov stability theory, and the convergence and stability of the control system were proved theoretically. A low frequency triangle reference displacement with a variable amplitude was used to detect and analyze the effect of the proposed control method. Experimental results show that the mean and maximal positioning errors by the tradition neural network are 0.43 μm and 0.77 μm respectively, but these errors can be reduced to 0.27 μm and 0.49 μm under the sliding model controller. Finally, the positioning precision is approved evidently.