TY - GEN
T1 - Application of software enabled control technologies to a full-scale unmanned helicopter
AU - Drozeski, Graham
AU - Yavrucuk, Ilkay
AU - Johnson, Eric
AU - Prasad, J. V.R.
AU - Schrage, Daniel
AU - Vachtsevanos, George
PY - 2005
Y1 - 2005
N2 - This paper presents a control architecture designed to accommodate a selection of modern control algorithms on a full-scale rotary-wing, unmanned aerial vehicle. The architecture integrates a visual landing system, two path planners, a flight envelope protection algorithm, and two low-level flight controllers that were developed independently by six agencies in academia and industry. A newly developed vehicle model and an exportable simulation environment were assembled in an open control infrastructure to expedite the algorithm development The collaboration resulted in successful flight testing of the architecture and multiple control algorithms on Boeing's Renegade Unmanned Aerial Vehicle, a derivative of the Robinson R22. The aircraft successfully switched from a conventional flight controller to an adaptive neural network flight controller on four occasions making it the largest helicopter to operate under adaptive neural network Eight control.
AB - This paper presents a control architecture designed to accommodate a selection of modern control algorithms on a full-scale rotary-wing, unmanned aerial vehicle. The architecture integrates a visual landing system, two path planners, a flight envelope protection algorithm, and two low-level flight controllers that were developed independently by six agencies in academia and industry. A newly developed vehicle model and an exportable simulation environment were assembled in an open control infrastructure to expedite the algorithm development The collaboration resulted in successful flight testing of the architecture and multiple control algorithms on Boeing's Renegade Unmanned Aerial Vehicle, a derivative of the Robinson R22. The aircraft successfully switched from a conventional flight controller to an adaptive neural network flight controller on four occasions making it the largest helicopter to operate under adaptive neural network Eight control.
UR - http://www.scopus.com/inward/record.url?scp=29344465160&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:29344465160
SN - 156347736X
SN - 9781563477362
T3 - Collection of Technical Papers - AIAA Atmospheric Flight Mechanics Conference
SP - 1271
EP - 1277
BT - Collection of Technical Papers - AIAA Atmospheric Flight Mechanics Conference 2005
T2 - AIAA Atmospheric Flight Mechanics Conference 2005
Y2 - 15 August 2005 through 18 August 2005
ER -