State Observation and Parameter Identification for Autonomous Heavy Haul Train

Abstract

Heavy haul train is large inertial and non-linear systems. Many real-time disturbances have a significant impact on autonomous driving control. In order to improve the effect of autonomous control, a new state observation and parameter identification method is proposed. The longitudinal multi-mass dynamics model is established for describing the train performance. The acceleration is calculated by Kalman filter of sampled speed. Resistance force and air braking response are identified by train dynamic model. The state observation method can significantly improve autonomous driving control effects. This method is used in control of heavy train autonomous driving.