Aircraft System Identification Using Artificial Neural ...
Transcript of Aircraft System Identification Using Artificial Neural ...
Aircraft System Identification
Using Artificial Neural Networks
Kenton Kirkpatrick
Jim May Jr.
John ValasekJohn Valasek
Aerospace Engineering Department
Texas A&M University
51st AIAA Aerospace Sciences Meeting
January 9, 2013
Compos Volatus
Overview
� Motivation
� System Identification
� Artificial Neural Networks
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� Artificial Neural Networks
� ANNSID
� Conclusions and Open Challenges
Motivating Questions
� Is it possible to use artificial neural networks to determine a linear model
for an aircraft based on experimental data?
� Would a linear model determined by an artificial neural network be able to
accurately model behaviors not present in the training data?
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accurately model behaviors not present in the training data?
� How would an artificial neural network-determined linear model compare
to other accepted methods of aircraft system identification?
Motivation
� System identification
o Some methods are only accurate under strict conditions
o Determining accurate solutions can be time consuming
o Currently accepted accurate solutions require learning parameters
independently or Kalman filtering
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� Artificial neural networks
o Require minimal user input
o Easily implemented
o Robust to noise
o Fast
System Identification
� Identification of linear model for aircraft systems
o Requires experimentally determined data, including state response to
control inputs and excitation of modes
o Separate linear models are generally determined for longitudinal and
lateral/directional modes
� Linear models are needed to analyze stability and determine control policies
� Identifying a linear model requires determining:
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� Identifying a linear model requires determining:
o State matrix, A
o Control matrix, B
o Output matrix, C
o Carry-through matrix, D
1k k k
k kk
x Ax Bu
y C x Du
+ = +
= +
Longitudinal Linear Model
� Longitudinal Motion
o Covers motion that occurs in the pitching plane
o Includes forward velocity, vertical velocity (or angle-of-attack), pitch
angle, and pitch rate
o Controls include elevator deflection and thrust
[ ]T
x u qα θ=
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Vα
u q
θ
[ ]x u qα θ=
[ ]T
e Tu δ δ=
Lat/D Linear Model
� Lateral/Directional Motion
o Covers motion that occurs in the rolling and yawing planes
o Includes side velocity (or side-slip angle), roll rate, yaw rate, roll angle,
and heading angle
o Controls include aileron and rudder deflections
[ ]T
x p rβ φ ψ=
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[ ]T
x p rβ φ ψ=
[ ]T
a ru δ δ=
V
βϕ
ψ
r
p
Observer/Kalman filter Identification
-100
0
100
v (
ft/s
ec)
Nonlinear
OKID
-100
0
100
p (
deg/s
ec)
Flight Condition
Altitude: 27,000 ft
Airspeed: 800 ft/sec
UCAV6 LINEAR SYSTEM IDENTIFICATION (LAT/D)
Valasek, John, and Chen, Wei, "Observer/Kalman Filter Identification for On-Line System Identification of Aircraft,"
Journal of Guidance, Control, and Dynamics, Volume 26, Number 2, pp. 347-353, March-April 2003.
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0 5 10 15 20-100
0 5 10 15 20-100
0 5 10 15 20-20
0
20
40
r (d
eg/s
ec)
0 5 10 15 200
50
100
150
phi (d
eg)
0 5 10 15 20-5
0
5
dr
(deg)
0 5 10 15 20-5
0
5
da (
deg)
OKID eigenvalues
-1.246 ± 4.2727i
-7.013
.02243
linearizer
eigenvalues
-1.4133 ± 4.5775i
-7.3635
.00835
Artificial Neural Networks
� Class of machine learning algorithms designed
to mimic the learning behavior of true neural
networks
� Actual neural networks are created by complex
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� Actual neural networks are created by complex
interactions between neurons that pass
electrochemical signals between each other
� Artificial neural networks are an attempt to
mimic this behavior by creating virtual units that
process and pass numerical information between
members of a network of units
Feedforward Neural Networks
� Most common artificial neural network
� External information enters the input layer
� Individual units process the inputs and pass the new information to the next
layer in the network
InputHidden
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.
.
. ...
.
.
.
Output
Feedforward Neural Networks
� Individual units are composed of inputs, internal processing, and outputs
� Inputs:
o Consist of weighted outputs from the previous layer
o Can include an extra weighted input of 1to offset learning bias
� Proccessing:
o Weighted Inputs are summed together
Sum is passed through a user-determined threshold function
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o Sum is passed through a user-determined threshold function
� Outputs:
o Threshold function result is output from the unit
o Outputs of current layer become inputs of next layer
Threshold Functions
� Threshold functions are used in neural network units to bound the summed
inputs of the unit based on the problem to be solved
� Common threshold functions include:
o Linear
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o Step (Perceptron)
o Sigmoid
( )t x x=
( )( ) sgnt x x=
1( )
1 xt x
e−
=+
Backpropagation
� Training a feedforward neural network requires an algorithm for updating
the weights of the network
� The most common training algorithm is the Backpropagation algorithm
o Uses gradient descent to update weights starting with the output layer
o Propagates errors between network outputs and desired outputs
backward through the network for weight updates
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backward through the network for weight updates
( )21
( ) ( )2
k k kE w s o w= −
i
i
Ew
wη
∂∆ = −
∂
ANNSID
� Artificial Neural Network System Identification (ANNSID) uses
backpropagation to determine A and B matrices
� ANN Requirements:
1. No hidden layers. Only input and output layers.
InputHidden
Input
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.
.
.
Input
.
.
.
Hidden
.
.
.
OutputOutput
ANNSID
� Artificial Neural Network System Identification (ANNSID) uses
backpropagation to determine A and B matrices
� ANN Requirements:
1. No hidden layers. Only input and output layers.
2. Must use linear threshold function (i.e., no threshold).
3. No bias inputs to nodes.
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3. No bias inputs to nodes.
OutputΣ
X t(X)
1w0
I1
I2...In
w1
w2
wn
OutputΣ
I1
I2...In
w1
w2
wn
ANNSID
� Artificial Neural Network System Identification (ANNSID) uses
backpropagation to determine A and B matrices
o Uses experimental data to learn state prediction
o A and B matrices are discrete
�
Inputs:1k k kx Ax Bu+ = + 1 1 2 1 3 1 4 1w w w w
w w w w
→ → → →
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o Inputs:
− xk
− uk
o Outputs:
− xk+1
1 2 2 2 3 2 4 2
1 3 2 3 3 3 4 3
1 4 2 4 3 4 4 4
w w w wA
w w w w
w w w w
→ → → →
→ → → →
→ → → →
=
5 1 6 1
5 2 6 2
5 3 6 3
5 4 6 4
w w
w wB
w w
w w
→ →
→ →
→ →
→ →
=
Longitudinal Example
� ANNSID for identifying longitudinal linear
model
o C700
All initial conditions are 0
Input
Outputuk
αk
q
uk+1
α
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o All initial conditions are 0
o Experimentally determined response for
training network
� OKID model simulated for comparison
qk
θk
δe,k
δT,k
αk+1
qk+1
θk+1
Longitudinal Example
0.1462 0.3697 0.1647 0.5904
0.0834 0.3808 0.7905 0.0177
0.0285 0.1274 2.1541 0.1341
0.0078 0.0010 0.8567 0.0027
ANNSID
longA
− − − − − − = − −
− −
0.2371 0.3715 0.0517 0.6304− − − −
0.6635 0.1235
0.6236 0.0051
7.8345 0.0006
0.4836 0.0007
ANNSID
longB
− − = − − − −
0.4012 0.1241−
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0.1394 1.0602 0.9127 0.0230
0.0918 0.2402 2.0719 0.1316
0.0129 0.0450 0.8722 0.0080
OKID
longA
− − − = − −
−
0.6219 0.0001
7.1121 0.0036
0.6369 0.0003
OKID
longB
− − = − − −
ANNSID OKID
λ1,2 = -0.2187 ± 0.1667j λ1,2 = -0.1384 ± 0.1364j
λ3 = -2.1564 λ3 = -2.2396
λ4 = -0.0901 λ4 = -0.8609
Lat/D Example
� ANNSID for identifying lateral/directional
linear model
o C700
All initial conditions are 0
Input
Outputβk
pk
r
βk+1
p
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o All initial conditions are 0
o Experimentally determined response for
training network
� OKID model simulated for comparison
rk
ϕk
δa,k
δr,k
pk+1
rk+1
ϕk+1
Lat/D Example
/
0.1688 0.0102 0.9895 0.1749
1.2807 2.3198 0.1820 0.0042
3.6614 0.5574 0.2284 0.0053
0.0992 0.8402 0.0288 0.0047
ANNSID
lat dA
− − − − − = − − −
−
0.1718 0.0185 0.9994 0.1919− − −
/
0.0037 0.0014
2.2508 0.2005
0.0022 0.6712
0.1341 0.0126
ANNSID
lat dB
− = − − −
0.0021 0.0282−
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/
1.0393 2.1342 0.1275 0.0018
3.4943 0.5350 0.2464 0.0019
0.0653 0.8935 0.0362 0.0045
OKID
lat dA
− = − − −
/
1.9976 0.2653
0.0794 0.6607
0.1886 0.0231
OKID
lat dB
− = −
−
ANNSID OKID
λ1,2 = -0.2760 ± 1.8554j λ1,2 = -0.2832 ± 1.8300j
λ3 = -2.1790 λ3 = -2.0162
λ4 = 0.0187 λ4 = 0.0311
Conclusions
� Accurate aircraft linear models for longitudinal and lateral/directional
motion can be determined using an artificial neural network
o Resulting matrices are comparable to OKID
o Works well on inputs not used in training
� The network must be restricted for network weights to be equivalent to A
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� The network must be restricted for network weights to be equivalent to A
and B matrices
o No hidden layers
o Linear threshold
o No bias input
o Inputs of current state and control, outputs of next state
� ANNSID is able to learn accurate models quickly (< 8 seconds CPU time
for scenarios tested)
Open Challenges
� Determine full linear model
o Use ANNSID formulation to determine linear models that include
longitudinal and lateral/directional coupling
o Will require flight conditions involving inputs from all controls
� Learn models for aircraft of different types
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� Learn models for aircraft of different types
o Investigate more aircraft similar to the C700
o Investigate modeling of high-performance aircraft
o Investigate modeling UAVs
� Investigate using ANNSID-determined models for control
o Develop feedback control laws using linear model
o Test control laws using the linear model on real aircraft