Dual-Assurance Aircraft Control for Certifiable Autonomous Flight
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Solution Overview
Problem
Current aviation technologies face challenges in certifying autonomous aircraft control systems due to the complexity of certifying advanced processing systems and unstructured sensor inputs, which can lead to increased cognitive load on pilots and limitations in remote operation capabilities.
Innovation Solution
A dual-assurance system architecture is introduced, comprising a lower assurance system for autonomous processing and a higher assurance system for validation, where the lower assurance system generates flight commands using uncertified components and the higher assurance system validates and executes these commands in a deterministic 'human-in-the-loop' manner, ensuring certifiable and fault-tolerant aircraft control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous processing systems with uncertified components are used, then automation extent and productivity are improved, but reliability and ease of certification deteriorate
Solution Approach 1:
The system is divided into two distinct segments: a lower assurance system that performs autonomous processing with uncertified components, and a higher assurance system that validates outputs using certified components. This segmentation allows each part to operate within its appropriate certification level, enabling automation while maintaining overall system reliability and certifiability.
Solution Approach 2:
The higher assurance system acts as an intermediary between the autonomous lower assurance system and the final control actions. It validates the outputs of the autonomous system, providing a bridge that enables uncertified components to be used while still ensuring certified reliability through the validating layer.
2Extent of automation
If advanced processing systems are used, then automation extent is improved, but device complexity increases
Solution Approach 1:
By segmenting the system into lower and higher assurance layers with distinct responsibilities, the complexity is distributed and organized. The lower assurance system handles complex autonomous processing while the higher assurance system focuses on validation, making the overall complexity more manageable and structured.
Solution Approach 2:
The higher assurance system serves multiple functions: validating outputs from the lower assurance system, ensuring certification compliance, and maintaining system safety. This multi-functionality reduces the need for separate dedicated systems, managing complexity while maintaining robust autonomous capabilities.
3Productivity
If autonomous flight command generation is implemented, then productivity is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The higher assurance system serves as an intermediary validation layer that makes the autonomous processing measurable and detectable. It provides a structured approach to validate flight commands generated by the lower assurance system, transforming the black-box autonomous operation into a verifiable process that maintains productivity while enabling measurement and detection.
Solution Approach 2:
The system implements feedback loops where the higher assurance system validates outputs from the lower assurance system and can provide corrections or alerts. This feedback mechanism makes the autonomous process detectable and measurable, allowing productivity improvements while maintaining oversight and validation capability.
Data Source
AI summary
The method can include: determining sensor information with an aircraft sensor suite; based on the sensor information, determining a flight command using a set of models; validating the flight command S130; and facilitating execution of a validated flight command. The method can optionally include generating a trained model. However, the method S100 can additionally or alternatively include any other suitable elements. The method can function to facilitate aircraft control based on autonomously generated flight commands. The method can additionally or alternatively function to achieve human-in-the-loop autonomous aircraft control, and/or can function to generate a trained neural network based on validation of autonomously generated aircraft flight commands.