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Evolving and Certifiable Autopilot for Unmanned Aerial Systems

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC19C0102
Agency Tracking Number: 183903
Amount: $749,994.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: A2
Solicitation Number: SBIR_18_P2
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-08-14
Award End Date (Contract End Date): 2021-08-13
Small Business Information
910 E. 29th, Lawrence, KS, 66046-0000
DUNS: 103592028
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Willem Anemaat
 (785) 832-0434
 anemaat@darcorp.com
Business Contact
 Libby Romaguera
Phone: (228) 813-6680
Email: libby.a.romaguera@nasa.gov
Research Institution
N/A
Abstract
An intelligent flight control system is developed with learning capabilities and a high degree of assurance that can be certified by the FAA and tested on a modular reconfigurable UAS.nbsp; Existing lack of intelligence, adaptability and high performance of current automatic flight controllers is addressed by taking advantage of high-performance computing platforms, 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 flight experience and develop intuition to adapt to a high level of uncertainties. To provide a high degree of assurance and make the learning autopilot system safe and certifiable, a conventional autopilot system is integrated based on a run-time assurance architecture. A monitor is developed to check aircraft states and envelope protection limits and handover aircraft control to a conventional autopilot system if needed. Provable guarantees of the monitor and the controllers is provided using formal analysis. The hybrid flight control system has adaptability and intelligence of skilled pilots and is capable of performing complex analysis and decision making in real-time. An artificial neural network model is built and trained to mimic the performance of classical robust optimal controllers, extending robustness, adaptability and curiosity of artificial neural network controllers and integrating a Real-Time Assurance system.Technology demonstration of the intelligent flight control system is achieved by flight testing of a Modular Air Vehicle, where the configuration can be customized to fit flight test needs and test adaptability of the proposed technology.nbsp; A Modular Air Vehicle is designed and prototyped.nbsp; Once the intelligent flight controllers are integrated with the airframe, ground and flight tests will be carried out to verify the performance and reliability of the proposed technology.

* Information listed above is at the time of submission. *

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