An online identification neural network model and an adaptive controller were designed and verified by simulations to inhibit the influence of hysteresis, creep and dynamic characteristics of a piezo-stage on the positioning accuracy. First, the double Sigmoid activation function was adopted to improve the activation functions of neural networks, and the similarities and differences between improved neural work model and PI hysteresis model were analyzed. Then, a BP neural network with three layers based on the improved activation function was designed as the online identification model of piezo-stage, and the correction formulas for the network weights, thresholds as well as the activation function thresholds were derived. Finally, the adaptive control scheme of the piezo-stage was proposed based on the online identification neural network model, which made use of another neural networks to complete the parameter adjustment of an adaptive PID controller. Experimental results show that the average error and the maximum error are 0.095 μm and 0.32 μm for the online identification neural network model, 0.070 μm and 0.100 μm for the adaptive control scheme on tracking triangle waves, and 0.080 μm and 0.105 μm for the tracking multiple frequency wave, respectively. Obtained data prove that positioning accuracy of the piezo-stage is improved effectively.