Aircraft Control Classifier for Unforeseen Scenario Autonomy

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Solution Overview

Problem

Developing aircraft control systems that can automatically operate in a wide range of scenarios without requiring direct pilot control is challenging due to the complexity and variability of aircraft systems, leading to increased development time and burden on developers.

Innovation Solution

An aircraft control system utilizing a classifier with a processing engine and input/output interfaces, which generates control outputs based on input data and a control policy represented by training data, allowing for automatic operation in unforeseen scenarios by selecting optimal operating procedures through a graph model and machine learning system.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional rule-based control systems are used to operate aircraft systems, then the control logic can be explicitly defined for known scenarios, but the system cannot handle scenarios beyond those envisaged during development and requires considerable development time for each new scenario

Engineering Contradiction:

Improveability to operate in unforeseen scenariosVSAvoidcomplexity of control system design

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical rule-based control systems with a machine learning classifier that learns control policies from training data. The classifier substitutes explicit programming with a trained model that can generalize to unseen scenarios, resolving the contradiction between adaptability and complexity by using data-driven learning instead of manual rule construction for each scenario

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent applies preliminary action by training the classifier on comprehensive training data that represents control policies for multiple scenarios before actual operation. This pre-training enables the system to handle unforeseen scenarios during deployment without requiring real-time rule development, thus improving adaptability while maintaining manageable complexity through offline preparation

Inventive Principle:

Principle #10Preliminary action

2Reliability

If comprehensive rules for all possible scenarios are coded into the control system, then the system can handle various operating conditions, but the development burden on developers increases significantly

Engineering Contradiction:

Improvecontrol system operation across scenariosVSAvoiddevelopment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent substitutes manual rule coding with automated machine learning training. Instead of developers manually coding rules for each scenario, the system automatically learns control policies from training data, significantly reducing development time while maintaining reliable operation across diverse scenarios through the classifier's generalization capability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent uses training data that copies or represents control policies for multiple scenarios without requiring explicit implementation of each rule. The classifier learns from these training examples and internalizes the control logic, enabling the system to replicate appropriate control behavior for seen and unseen scenarios without manual rule construction

Inventive Principle:

Principle #26Copying

3Extent of automation

If a classifier with learned parameters is used to control the aircraft, then automatic operation in unforeseen scenarios becomes possible, but the system requires training data and machine learning infrastructure

Engineering Contradiction:

Improveautomatic control operationVSAvoidcomplexity of training and deployment process

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent replaces manual control operations with an automated classifier that processes sensor inputs and generates control outputs based on learned parameters. This substitution enables automatic operation in unforeseen scenarios while the training infrastructure complexity is managed through systematic data collection and model training processes that occur during system setup rather than operation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12187418B2Scenario-based control system

Publication Date: 2025.01.07 AIRBUS OPERATIONS LTD

AI summary

An aircraft control system (100) including an input interface (102), an output interface (114) and a processing engine (108) having a classifier (110) that applies input data (104) generated by the input interface (102) to generate output control data (112). The classifier (110) has a plurality of parameters which represent a control policy for operating the aircraft (800). The output interface (114) generates control outputs to control the aircraft (800) based on the output control data (112). A machine learning system (900) for training the classifier (110) including an environment (902), a pathway evaluation engine (904), storage (906), and a training engine (908). The machine learning system (900) generates training data (912) by selecting a pathway representing an operating procedure using the pathway evaluation engine (904). The training engine (908) trains the classifier (110) using the training data (912).