Evolving and Certifiable Autopilot for Unmanned Aerial Systems, Phase I

Metadata Updated: February 28, 2019

The project consist of the development of a new intelligent flight control system with learning capabilities and a high degree of assurance, that can be certified by the FAA

Machine learning and artificial intelligent research has led to many tangible results and recent developments in cognitive control and decision making. Although automatic flight controllers are widely used and they have become common in recent years, they often lack intelligence, adaptability, and high performance. Reliability of UASs in unforeseen conditions is a direct function of their intelligence and adaptability.

The proposed project aims to take advantage of high-performance computing platforms and the state-of-the art machine learning and verification algorithms to develop a new intelligent, adaptable, and certifiable flight control system with learning capabilities. The autopilot system will be able to learn from each flight experience and develop intuition to adapt to a high level of uncertainties. To provide a high degree of assurance and to make the learning autopilot system safe and certifiable, a secondary and conventional autopilot system will be integrated based on the run-time assurance architecture. A monitor will be developed to continuously check aircraft states and envelope protection limits, and handover aircraft control to the conventional autopilot system if needed. Provable guarantees of the monitor and the controllers will be provided using formal analysis. The propose a hybrid flight control system which has adaptability and intelligence of skilled pilots and at the same is cable of performing complex analysis and decision making algorithms in real-time.[HTML_REMOVED] We aim to build and train an artificial neural network model that can[HTML_REMOVED]mimic the performance of the classical robust optimal controllers, extend the robustness, adaptability, and curiosity of the artificial neural network controller and integrate a Real-Time Assurance (RTA) system.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date February 28, 2019
Metadata Updated Date February 28, 2019

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date February 28, 2019
Metadata Updated Date February 28, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_94578
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 52811cca-7214-4ad9-91b7-74297f3858e3
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2019-01-01
Homepage URL https://techport.nasa.gov/view/94578
License http://www.usa.gov/publicdomain/label/1.0/
Data Last Modified 2018-09-07
Program Code 026:027
Source Datajson Identifier True
Source Hash 13ad225f4f0bad197e3934b24b272d9e1c54b37f
Source Schema Version 1.1

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