The adaptive sliding mode control algorithm is proposed based on Radial Basis Function (RBF) neural network and LuGre friction mode for the electrohydraulic position servo system (valve-controlled symmetrical cylinder), which is model complex, time-varying of parameters and the friction influence significantly in characteristics. The advantages in this algorithm:(1) The mathematical model of it could be simplified to the second order and be reduced the model parameters via RBF neural network approximation the relationship between control current and system output pressure.(2) LuGre model could accurately describe the complex of dynamic and static characteristics in the friction process. Designing the friction compensation based on this model could improve the control precision. (3) The design of adaptive sliding mode controller enhanced the robustness of the system. The stability of closed-loop system was proved by constructing Lyapunov function. Simulation experiments show that the proposed algorithm has high control precision, fast response, and strong robustness.