To overcome the complicated nonlinearity such as friction, clearance and coupling and the uncertainties such as time-varying of parameter existed in the servo simulated loading system for Gun Control Systems (GCS), an adaptive sliding mode control strategy based on RBF neural network is proposed.To the uncertainties such as parameter time-varying in the system, RBF neural network is used to approach uncertain parts adaptively.Then, a dynamic adjustment approach of the switch gain RBF neural network is used to dynamically adjust the switching gain of the switching function, which enhances the dynamic performance of the system.An adaptive law is derived by using Lyapunov theory to estimate neural network weights and unknown functions online and ensure the stability of the system.Simulation results show that this control strategy can not only effectively suppress the external disturbances, but also has a rapid responding speed, which ensures the control precision and robustness when the system is loading in static or dynamic state.