Graph-Based Aircraft Control Classifier Tuning for Reliable Outputs
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Solution Overview
Problem
Developing aircraft control systems using trained classifiers is time-consuming and requires significant trust in artificial intelligence techniques before they can be considered dependable, as existing methods do not adequately address the reliability and consistency of these systems, particularly in unforeseen scenarios.
Innovation Solution
A test system is employed to modify graph-based trained classifiers using a custom loss function, which calculates a loss score based on differences between validation data and control data, allowing for refinement and penalization of incorrect outputs, thereby enhancing the reliability and consistency of the aircraft control system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional rule-based control systems are used, then reliability is maintained through explicit rules, but development time and complexity increase significantly
Solution Approach 1:
The patent replaces traditional mechanical rule-based control systems with an AI-trained classifier system. The classifier is trained on historical flight data and scenario information to automatically generate control decisions, eliminating the need for manual rule coding while maintaining reliability through systematic training and validation processes.
Solution Approach 2:
The patent implements preliminary training of the classifier using extensive flight data and scenario simulations before deployment. This pre-training phase allows the system to learn optimal control strategies in advance, reducing development time for new scenarios while ensuring reliable performance through thorough pre-validation.
2Productivity
If AI techniques are used to reduce development time, then productivity increases, but reliability and trustworthiness decrease
Solution Approach 1:
The patent implements a comprehensive feedback mechanism where the classifier's outputs are continuously validated against expected control outcomes. The system compares generated control decisions with ground truth data and adjusts training accordingly, ensuring high reliability while maintaining rapid development through automated feedback loops.
Solution Approach 2:
The patent creates a dynamic training and validation process where the classifier can be continuously refined using new flight data and scenarios. This dynamic approach allows the system to adapt and improve over time, maintaining both high productivity through automated processes and high reliability through ongoing validation and retraining.
3Measurement precision
If extensive training data is used to improve classifier reliability, then measurement precision increases, but loss of time and computational resources increase
Solution Approach 1:
The patent segments the training process into multiple phases: initial training on comprehensive flight data, validation on separate test scenarios, and fine-tuning on specific edge cases. This segmentation allows the system to achieve high measurement precision through targeted training while reducing total training time by focusing computational resources on the most critical scenarios.
Solution Approach 2:
The patent applies partial training actions by focusing on the most critical flight scenarios and control decisions rather than uniformly training on all possible scenarios. This approach achieves sufficient measurement precision for safety-critical operations while significantly reducing training time and computational resources by concentrating on high-impact areas.
Data Source
AI summary
A test system for modifying a graph-based trained classifier, configured to output control data for controlling the aircraft system according to a graph model representing the aircraft system. The test system is configured to obtain scenario data, control data, and validation data. The test system generates a custom loss score based on differences between the validation data and the control data and modifies the graph-based trained classifier based on the custom loss score, the scenario data, and the control data. A computer-implemented method for modifying the graph-based trained classifier and a storage medium comprising instructions to perform the method are also provided.