Control System for Active Safety and Smooth Operation of High-Speed Trains

Abstract

The train operation control system demands extremely high positioning accuracy and real-time control. A single sensor struggles to provide continuous, high-precision positioning in complex environments like tunnels and mountains, while traditional mechanical sensors suffer from limited reliability. This paper systematically investigates three dimensions: precise positioning via multi-source information fusion, fault prediction and health management of critical components, and control strategies for stable maneuvering based on multi-source information fusion. At the positioning level, the principles of integrated navigation are elucidated and optimization pathways for filtering and fusion algorithms in complex environments are analyzed. At the health management level, an on-board PHM architecture is established and pattern-mining-based methods for advanced fault warning are studied. At the control level, sensorless position/velocity control and precise station-stop technologies are investigated, exploring the deep integration of positioning information into control decisions. Results indicate that multi-source information fusion effectively compensates for single-sensor limitations; PHM technology enables a shift from reactive to proactive maintenance; and sensorless control provides a redundant speed feedback channel for traction systems. These findings offer theoretical support for developing a train operation control system that balances safety and comfort.