Smart Control of 2 Degree of Freedom HelicoptersGlenn Janiak, Ken Vonckx, Advisor: Dr. Suruz Miah
Department of Electrical and Computer Engineering, Bradley University, Peoria IL
Objective and ContributionObjective• Develop a platform allowing mobile devices to control
the motion of a group of helicoptersContribution• Determine trade-offs between traditional control
techniques and machine learning• Multi-Helicopter ApplicationApplications• Teleoperation approach to search and rescue• Aerial turbulence resistance
Problem Setup
Helicopter 1
Helicopter 2
Helicopter N
Proposed
smart control
algorithm of
helicopters
Mobile
device 1
Mobile
device 2
Mobile
device N
Wireless
network
Figure 1:High level architecture of the proposed system.
Figure 2:2-DOF helicopter (Quanser Aero).
• State-space representation of 2-DOF helicopter
θ
ψ
θ
ψ
=
0 0 1 00 0 0 10 −Ksp/Jp −Dp/Jp 00 0 1 −Dy/Jy
θψ
θ
ψ
+
0 00 0
Kpp/Jp Kpy/JpKyp/Jy Kyy/Jy
VpVy
Motion (Trajectory) Control Algorithm
Motion
controllerHelicopter
Encoder
Mobile
device
Error
signal
2-DOF
motors
θ
θd
d
Actual
configuration
sent back to
user
VpVy
Figure 3:A desired orientation is given by a user. The difference betweenthis input and the actual position is calculated. The controller the calcu-lates the proper amount of voltage to apply to the DC motors.
1 Employ state-space representation of 2-DOF helicopter:x = Ax + Bu
2 Use state feedback lawu = −Kx
to minimize the quadratic cost function:J(u) = ∫∞
0 (xTQx + uTRu + 2xTNu)dt3 Find the solution S to the Riccati equation
ATS + SA− (SB + N)R−1(BTS + NT ) + Q = 04 Calculate gain, K
K = R−1(BTS + NT )
Optimal Noise Resistant ControlAlgorithm
• Utilizes gain calculated in LQR• Added Kalman filter to reduce external disturbances to the
system
Figure 4:Noise resistant 2-DOF helicopter model.
Reinforcement Learning Algorithm• Uses neural network based on difference between desired
and actual orientation to determine optimal gain
θd− θ
d −
_θd − _θ
_ d − _
V
w1
w2
w3
w4
w5
w6
w7
w8
w9
w10
w11
w12
w13
w14
e1
e2
e3
e4
P
e1
e2
e3
e4
e21
e1e2
e22
e1e3
e1e4
e2e3
e2e4
e3e4
e23
e24
Input
layer
Hidden
layer
Output
layer
Figure 5:ADP Neural Network
Simulation Results
0 1 2 3 4 5 6 7 8 9 10
Time [s]
0
5
10
15
20
25
30
35
40
45
50
Pitch
[d
eg
]
(a)
0 1 2 3 4 5 6 7 8 9 10
Time [s]
0
20
40
60
80
100
120
140
160
180
200
Yaw
[deg]
(b)
0 1 2 3 4 5 6 7 8 9 10
Time [s]
-20
-15
-10
-5
0
5
10
15
20
Vo
ltag
e [V
]
(c)
0 1 2 3 4 5 6 7 8 9 10
Time [s]
-20
-15
-10
-5
0
5
10
15
20
Vo
ltag
e [V
]
(d)Figure 6:A comparison between LQG and LQR control for a step input isshown for (a) the main rotor and (b) the tail rotor and the correspondingvoltages in (c) and (d)
Experimental ResultsHelicopter #2
(Quanser Aero #2)
Helicopter #1
(Quanser Aero #1)
Mobile device
(Samsung
Galaxy S9+)
Single-board
computer #1
(Raspberry Pi 3
Model B #1)
Single-board
computer #2
(Raspberry Pi 3
Model B #2)
Figure 7:Experimental Setup
0 5 10 15 20 25 30
time(s)
0
5
10
15
Pitch
(de
g)
(a)
0 5 10 15 20 25 30
time(s)
0
10
20
30
40
50
60
Ya
w(d
eg
)
(b)Figure 8:ADP experimental results for (a) the main rotor and (b) the tail ro-tor given a step input
0 5 10 15 20 25 30
time(s)
-10
-5
0
5
10
15
20
Pitch
(de
g)
(a)
0 5 10 15 20 25 30
time(s)
-40
-20
0
20
40
60
Ya
w(d
eg
)
(b)Figure 9:Comparison between P and PI control for a step input is shownfor (a) the main rotor and (b) the tail rotor
(a) (b)Figure 10:(a) Time = 0 and (b) Time = 10
Conclusion and Future Work• Model-based reinforcement learning technique (ADP) is
useful when system model is unknown
• Implement PI controller for ADP algorithm• Use digital compass to increase accuracy of orientation
and help identify initial position
