Train Deceleration Command Generation Using Travel History Data
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Solution Overview
Problem
Conventional automatic train operation devices require complex adjustments and extensive labor to achieve precise stopping accuracy, as the process of adjusting fuzzy control rules is cumbersome and time-consuming, increasing the number of labor-hours needed for modifications.
Innovation Solution
An automatic train operation device that includes a determining unit for identifying a step command start position before the target stop, a deceleration command generating unit that uses travel histories to control braking, and a travel history storage unit to store and modify deceleration commands, reducing the need for manual adjustments by leveraging past travel data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If fuzzy control rules are adjusted to improve stopping accuracy, then stopping accuracy is improved, but the number of labor-hours needed for adjustment increases
Solution Approach 1:
The control device automatically adjusts fuzzy control rules by evaluating past travel data and determining appropriate rule modifications without requiring manual intervention. The system stores travel data including stopping accuracy results, automatically analyzes this data to identify patterns, and modifies the fuzzy control rules accordingly, enabling the system to self-optimize stopping accuracy while eliminating the time-consuming manual adjustment process
Solution Approach 2:
The system implements a feedback mechanism where past travel data including stopping accuracy measurements are stored and continuously evaluated. This feedback loop allows the system to learn from previous operations and automatically adjust fuzzy control rules to improve stopping accuracy, replacing manual trial-and-error adjustment with an automated learning process that reduces labor-hours while maintaining or improving precision
2Manufacturing precision
If the ability to follow the target velocity pattern is enhanced to ensure stopping accuracy, then stopping accuracy is improved, but the number of switchings of the notch command increases, which deteriorates ride comfort
Solution Approach 1:
The system dynamically adjusts fuzzy control rules based on evaluated past travel data rather than using fixed, pre-programmed control parameters. This dynamic adaptation allows the control strategy to optimize both stopping accuracy and ride comfort by learning from actual operational outcomes, modifying control rules to reduce unnecessary notch command switchings while maintaining precise stopping performance
Solution Approach 2:
The system changes control parameters by automatically modifying fuzzy control rules based on evaluated travel history. Instead of enhancing the ability to follow target velocity patterns with fixed parameters that cause excessive switching, the system adapts the control parameters themselves through automated rule adjustment, finding optimal parameter settings that balance stopping accuracy with smooth operation and reduced notch switching
Data Source
AI summary
An automatic train operation device includes: a step command start position determining unit to determine whether a train passes through a step command start position that is a position a certain distance before a target stop position of the train; a deceleration command generating unit to generate a deceleration command to control braking force of a braking device in a section from the step command start position that the train passes through to the target stop position at which the train stops; and a travel history storage unit to store travel state information and the deceleration command for the section as a plurality of travel histories. When the step command start position is determined by the step command start position determining unit, the deceleration command generating unit generates the deceleration command by using the travel histories.