ATO Parking Control With Adaptive Offset Learning for Accurate Docking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The urban rail transit ATO system faces challenges in achieving high-accuracy parking due to mismatched braking systems and variations in train performance, leading to inaccurate docking during peak operations, which affects efficiency and reliability.
Innovation Solution
An adaptively adjusted parking control method that monitors speed tracking performance, updates stop results, calculates a parking point offset based on statistical features, and restarts the learning process when thresholds are exceeded, allowing for real-time adjustments to ensure accurate docking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed parameters are used in the ATO system, then the system structure is simple and easy to operate, but the system cannot adapt to changes in track environment and train performance, resulting in poor stopping accuracy
Solution Approach 1:
The patent implements dynamic parameter adjustment by introducing a learning mechanism that continuously adapts parking parameters based on historical stopping data. The system transitions from fixed parameters to dynamic parameters that automatically adjust according to actual train performance and track conditions, resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The patent establishes a feedback loop where stopping accuracy is continuously monitored and used to adjust parking parameters. The system collects historical stopping data, analyzes deviations from target positions, and automatically modifies parking parameters to improve accuracy, creating a closed-loop control system that adapts to changing conditions.
2Measurement precision
If pneumatic braking is used in the low-speed stage, then the braking system is simple to implement, but the braking force decreases and causes over-docking tendency
Solution Approach 1:
The patent applies preliminary action by extending the electric braking action time into the low-speed stage before transitioning to pneumatic braking. This preliminary extension of electric braking ensures sufficient braking force is maintained during the critical low-speed approach, preventing over-docking while managing the complexity of coordinated braking control.
Solution Approach 2:
The patent changes the braking control parameter by adjusting the speed threshold at which electric braking transitions to pneumatic braking. By modifying this transition point parameter, the system optimizes the braking force distribution between electric and pneumatic systems, improving docking accuracy without requiring complete redesign of the braking control architecture.
3Measurement precision
If the electric braking fades out early in the low-speed stage, then the pneumatic braking action time is reduced, but the braking force decreases and docking accuracy deteriorates
Solution Approach 1:
The patent uses preliminary action by extending electric braking into the low-speed stage before pneumatic braking takes over. This ensures that electric braking maintains sufficient action time to provide precise force control during the critical final approach, preventing accuracy deterioration while managing the transition timing between braking systems.
Data Source
AI summary
An adaptively adjusted and accurate parking control method for an ATO system includes performing statistical learning on the basis of historical stop information, and adaptively inferring a parking point offset. Moreover, two application preconditions of the method are provided: firstly, speed tracking performance is good in an electric braking stage, and secondly, a pneumatic braking process has random and statistical stationary characteristics. The method improves the average stop accuracy in a statistical sense, satisfies a high-accuracy stop requirement of the entire train formation, and can also evaluate train performances and track circumstances timely and duly, thereby satisfying complex and variable real-time operation task requirements.