USA flag logo/image

An Official Website of the United States Government

Scalable System Approaches to Unmanned Aerial Vehicle Upset Prevention and…

Award Information

Agency:
Department of Defense
Branch:
Navy
Award ID:
75277
Program Year/Program:
2005 / SBIR
Agency Tracking Number:
N052-097-0384
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Barron Associates, Inc.
1410 Sachem Place Suite 202 Charlottesville, VA -
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2005
Title: Scalable System Approaches to Unmanned Aerial Vehicle Upset Prevention and Recovery
Agency / Branch: DOD / NAVY
Contract: N68335-06-C-0036
Award Amount: $79,048.00
 

Abstract:

Wind shear, icing, wake vortices from ship superstructures or other aircraft, actuator malfunctions, or component failures can all contribute to upset conditions. For piloted aircraft, prevention of or recovery from these events is challenging because of the nonlinear dynamics encountered at angles of attack and sideslip outside of the normal flight envelope. The problems are magnified for unmanned aircraft given typical vehicle sizes, actuator bandwidth, and the absence of a pilot. The goal of our research is to develop the control technologies that enable unmanned aerial vehicles to perform flight-envelope protection and upset recovery autonomously. Reinforcement learning control is a core technology to design outer-loop controllers that affect situation-appropriate recovery within the problem constraints (structural loads, for example). Manual recovery practices and NATOPS procedures are also encoded in the design. Novel control devices can make air vehicles more resistant to departure by postponing the onset of flow separation at high angles of attack. Thus, we address the role of add-on actuators in the recovery control-system framework. These range from a flip-tail for a flight-test UAV to arrays of synthetic jets for a tailless UCAV. High-fidelity simulations of two dynamically dissimilar models are used to develop the technology in Phase I.

Principal Investigator:

Jeffrey F. Monaco
Research Scientist
4349731215
monaco@bainet.com

Business Contact:

David G. Ward
President
4349731215
barron@bainet.com
Small Business Information at Submission:

BARRON ASSOC., INC.
1410 Sachem Place, Suite 202 Charlottesville, VA 22901

EIN/Tax ID: 541243694
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No