In order to improve the rapidity and accuracy of the response of the servo system of a certain obstacle breaking weapon,the neural network sliding mode control is studied.Combined with the model of servo system,the Ridgelet Recurrent Neural Network(RRNN) is introduced to dynamically and adaptively approximate the model,which can effectively improve the speed of response and robustness.Through the Ridgelet Recurrent Neural Network Sliding Mode Controller (RRNN-SMC),the influence of uncertain factors such as load disturbance and parameter change is effectively overcome.Finally,the Particle Swarm Optimization (PSO) algorithm is applied to optimize the ridgelet parameters and link weights,which can effectively reduce the influence of sliding mode chattering.The simulation results show that the method can ensure the stability of the servo system,accelerate dynamic real-time response and improve the precision of servo control.