Optimal Path Planning and Power Allocation for a Long Endurance Solar-Powered UAV
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Optimal Path Planning and Power Allocation for a Long Endurance Solar-Powered UAV Saghar Hosseini, Ran Dai, and Mehran Mesbahi 1 Saghar Hosseini, ACC 2013, Washington, DC Robotics, Aerospace, and Information Networks Lab
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Optimal Path Planning and Power Allocation for a Long Endurance Solar-Powered UAV. Saghar Hosseini, Ran Dai , and Mehran Mesbahi. Robotics, Aerospace, and Information Networks Lab. Motivation and Applications. Development of Solar-Powered UAVs - PowerPoint PPT Presentation
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Optimal Path Planning and Power Allocation for a Long Endurance Solar-Powered
UAVSaghar Hosseini, Ran Dai, and Mehran Mesbahi
1
Saghar Hosseini, ACC 2013, Washington, DC
Robotics, Aerospace, and Information Networks Lab
Motivation and Applications• Development of Solar-Powered UAVs
• The first solar-powered flying models was built in 1974
• NASA pathfinder was launched in 1998 with altitude of 80,201ft
• Boeing Solar Eagle is expected to stay aloft for five years• Applications :
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Atmospheric SatelliteSurveillance and Reconnaissance
Forest Fire Fighting
Saghar Hosseini, ACC 2013, Washington, DC
Outline• Previous work• Optimal control problem formulation• Nonlinear programming results• Reduced hybrid model• Examples• Future research Th
e 20
13 A
mer
ican
Con
trol
Co
nfer
ence
, Jun
e 17
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, W
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n, D
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Saghar Hosseini, ACC 2013, Washington, DC
Previous Work• Level flight
• Klesh and Kabamba (2007, 2009)
• Cylinder• Spangelo et al. (2009)
• 3D trajectory• Sachs et al. (2009)
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Saghar Hosseini, ACC 2013, Washington, DC
Solar UAV Power Resources
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Saghar Hosseini, ACC 2013, Washington, DC
Solar UAV Kinematics
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Saghar Hosseini, ACC 2013, Washington, DC
Optimal Control Problem
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Saghar Hosseini, ACC 2013, Washington, DC
Nonlinear Programming Results
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-0.5 0 0.5 1 1.5 2-1
-0.5
0
0.5
1
1.5
2
y (1
00km
)
x (100km)
0 5 10 15 20 250
1
2
3
4
5
6
7
8
z (k
m)
time (hr)
0 5 10 15 20 25-0.5
0
0.5
(d
eg)
time (hr)
0 5 10 15 20 25-3
-2
-1
0
1
2
3
4
(de
g)
time (hr)
Saghar Hosseini, ACC 2013, Washington, DC
Level flight
Climb Glide
Level flight
Climb Glide
Nonlinear Programming Results
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0 5 10 15 20 25-40
-20
0
20
40
60
80
100
time (hr)
Pow
er (w
att)
PBattPSunPEng
0 5 10 15 20 25
0.4
0.5
0.6
0.7
0.8
0.9
1
SO
C
time (hr)
Saghar Hosseini, ACC 2013, Washington, DC
Level flight
Climb Glide
Level flight
Climb Glide
0 5 10 15 20 250
5
10
15
V (m
/s)
time (hr)
Nonlinear Programing Results
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Saghar Hosseini, ACC 2013, Washington, DC
Reduced Hybrid Model
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Saghar Hosseini, ACC 2013, Washington, DC
Mode 1 : Low Altitude Level Flight
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Saghar Hosseini, ACC 2013, Washington, DC
Mode 2 : Steady climb
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Saghar Hosseini, ACC 2013, Washington, DC
Examples
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-2 -1 0 1 2-1
-0.5
0
0.5
1
1.5
2
y (1
00km
)
x (100km)
Reduced ModelOriginal model
0 5 10 15 20 250
1
2
3
4
5
6
7
8
z (k
m)
time (hr)
Reduced ModelOriginal model
0 5 10 15 20 25-3
-2
-1
0
1
2
3
4Flight path angle (deg)
(de
g)
time (hr)
Reduced ModelOriginal model
0 5 10 15 20 25-0.5
0
0.5
(d
eg)
time (hr)
Reduced ModelOriginal model
Saghar Hosseini, ACC 2013, Washington, DC
Examples
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Original Model
Reduced Hybrid Model
393.417 sec 1.758 sec
0 5 10 15 20 25
0.4
0.5
0.6
0.7
0.8
0.9
1
SO
C
time (hr)
Reduced ModelOriginal model
0 5 10 15 20 25-40
-20
0
20
40
60
80
100
time (hr)
Pow
er (w
att)
PBatt
PSun
PEngPBatt Original model
PSun Original model
PEng Original model
Saghar Hosseini, ACC 2013, Washington, DC
0 5 10 15 20 250
5
10
15
V (m
/s)
time (hr)
Reduced ModelOriginal model
Computation time
Reduced Hybrid Model Result
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Saghar Hosseini, ACC 2013, Washington, DC
Future Research
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• Coverage Problem• Thermal fields/Wind gust• Coordinated flight• Mission planning
Saghar Hosseini, ACC 2013, Washington, DC
