Abstract: In order to solve the problem of pipeline bending caused by slippage of trenchless pipe laying machine, a navigation control system of trenchless pipe laying machine was designed based on Real Time Kinematic-BeiDou Navigation Satellite System (RTK-BDS). According to the idea of multi-mode control algorithm, the multi-mode adaptive PID control algorithm of trenchless pipe laying machine was proposed. The analyses of the kinematic model and slip yaw model of tracked vehicles showed that the turning radius of vehicles will increase when slipping occurs; Through the analysis of the deviation model of vehicles, the calculation models of the heading deviation and lateral deviation of vehicles were given; Based on the analysis of the structure and related structural equations of the hydraulic motor of vehicles, the control transmission function of the hydraulic motor of trenchless pipe laying machine was obtained. The navigation control system of trenchless pipe laying machine mainly consists of BP (Back Propagation) neural network classifier, modal selector, knowledge base, adaptive comparator and walking controller. BP neural network classifier uses sensors to detect the running speeds of the left and right tracks, the actual average vehicle speed, the engine power and the oil pump pressure of the hydraulic motors on both sides of the trenchless pipe laying machine, so as to obtain the running state of the relevant modes of the vehicle, and input the sampled data to the computer. According to the model trained by BP neural network, the current state classification of vehicles is predicted. The modal selector can obtain the relevant parameters of the current vehicle state from the knowledge base through the classification results of BP neural network, and send them to the adaptive comparator and walking controller. The knowledge base contains the adaptive functions of the vehicle control system and the control parameters of positions. The adaptive comparator can calculate the weights of the two errors according to the current heading error and lateral error of the vehicle and the reference line, and compare the two weights. If the heading error weight is larger than the lateral error weight, the heading PID controller is selected to control the vehicle navigation, otherwise the lateral PID (Proportion-Integral-Differential) controller is selected to control the vehicle navigation. The vehicle modal control parameters and BP neural network training samples were obtained through the field test. The test results showed that the lateral overshoot was within 4.58 cm, the heading error reduced to ±3.7° and the lateral error was stable within ±2.6 cm. The linear control experiment results showed that the heading error of the control algorithm was within ±5.5°, which was within ±3° under 96.2% cases, and the lateral error was within ±2.6 cm, which was within ±1 cm under 89.6% cases. The engineering application test showed that the heading error could be kept within ±7° and the average heading error was within ±3.5° and the transverse error could be kept within ±4 cm. The control system can meet the construction requirements of trenchless pipe laying machine.