Abstract: To enhance the operational safety and intelligent control level of underground trackless auxiliary transport rubber-tyred vehicles in coal mines, a motion simulation and human-computer interaction system integrating new-generation information technology with the concept of digital twin is proposed to solve the limitations of traditional monitoring and simulation modeling methods in intuitively representing operational status and constructing high-fidelity models. The system takes a trackless rubber-tyred vehicle with multi-sensor redundancy as the research object, and the system architecture and functional modules are designed using MATLAB/Simulink, and the CAN bus is used for standardized conversion and high-speed transmission of multi-source sensor data. Real-time data interaction between the digital twin model and the physical entity is achieved through the I/O interface of the vehicle control unit (VCU), forming a virtual-physical fusion environment that enables continuous monitoring of key safety parameters such as torque, speed, and brake pressure. A predictive simulation method based on physical modeling and real-time data is introduced to accurately simulate the dynamic response of vehicle under various working conditions, supporting VCU program optimization and control algorithm parameter tuning. Taking a low-speed four-wheel steering scenario in an underground coal mine as an example, the digital twin-based simulation platform reproduces real turning conditions. Combined with the dynamic characteristics of vehicle, multiple simulation experiments are conducted to optimize the proportional-integral-derivative (PID) control parameters. The optimized control strategy significantly improves yaw rate and reduces turning radius. The simulation results closely match the measured data, with a maximum relative error of 1.34% and an average error of 0.85%, verifying the accuracy and reliability of the model. The overall time delay of the control system is controlled within 30 ms, fully demonstrating the real-time performance of the system.