Post on 26-Jan-2021
transcript
Technische Universität München
Lehrstuhl für
Flugsystemdynamik
Modellbasierte Entwicklung und Test
von Regelungssytemen Prof. Dr.-Ing. Florian Holzapfel
Rx2 active
[Rx1 healthy] [Rx1 fail]
RSw second lane active
CSw first input active CSw second input active
RSw second lane active
Rx2 active
[CSw fail]
Rx2 active
[Rx2 healthy]
[Rx2 fail]
CSw first input active
[RSw fail] [RSw healthy]
Manual
Control
Loss of
aircraftLoss of
servo
[CSw healthy]
[RSw fail] [RSw healthy]
[CSw healthy]
[CSw fail]
[CSw healthy]
[Rx2 healthy]
[Rx2 fail]
[CSw fail]
[CSw healthy]
[CSw fail] CSw second input active
[Rx2 healthy]
[Rx2 fail]
Loss of
servoAutopilot
Control
Manual
Control
Loss of
servoLoss of
aircraft
Loss of
aircraftAutopilot
Control
[Rx2 healthy]
RSw first lane active
[Rx2 fail]
Rx1 active
CSw first input active [CSw fail]
[CSw healthy]
Loss of
servo
Autopilot
ControlLoss of
servo
Rx2 active
Assisted and Auto Mode
Loss of Aircraft
Bat1 fail Bat2 fail RSw failPSw1 fail Rx1 fail Rx2 failCSw 1-N failPSw2 fail
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Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
What will this presentation be about?
• Introducing TUM Institute of Flight System Dynamics
• Model Based Development in Aerospace Applications
• Model Based Requirements Assessment
• Model Based Control Design
• Embedded System Design
• Model Based Testing
2
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
Introducing TUM Institute of Flight System Dynamics
Research 4
Modeling, Simulation &
Parameter Estimation
Flight Control &
Flight Guidance
Sensors,
Data Fusion & Navigation Trajectory Optimization
Main
Research
Areas
Avionics &
Safety Critical Systems
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
Introducing TUM Institute of Flight System Dynamics
DA42M-NG – Flying test bed for the Free State of Bavaria 5
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
Introducing TUM Institute of Flight System Dynamics
Industry Partners involved in Projects 6
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
Model Based Development in Aerospace Applications
Development in Accordance with Aerospace Standards
• Requirements based, process driven
development
• All development done in accordance to
safety process
• Extensive testing in real as well as
simulated environments
7
DO-
178B/C
ARP 4754
DO-254
ARP 4761
DO-160E
Quality
Assurance
Process
Project Management Process
Prerequisites &
Infrastructure
Project Planning &
Organization
Configuration
Management
Process
Certification
Liaison
Process
Verification
Process
EASA Cert Memos
SWCEH-001 / -002
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
9 Model Based Requirement Assessment
Requirements for Flight Controllers of manned aircraft
• Manned aircraft:
Many requirement catalogs – MIL-F-8785C, MIL-STD-1797, AS94900…
• Unmanned Aircraft:
Top Level Requirements given by mission
• Many people take the same requirements as for manned a/c
But what about requirements to inner control loops for
unmanned systems?
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
10
Zn
Zn
0q
0q
Model Based Requirement Assessment
Example: CAP Control Anticipation Parameter
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
11 Model Based Requirement Assessment
How to get Requirements that make Sense?
• Propagate them from outer loop / mission to inner loops
• Example of top level requirement:
The probability that the deviation from the commanded altitude is more than
xx [m] must be smaller than 10^-yy
• Fulfillment depends on:
sensor quality, disturbance probabilities & amplitudes, control error
SO WHAT ARE SPECIFICATIONS THAT MAKE SENSE?!?
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
12 Model Based Requirement Assessment
Motivation
System requirements
Sensor requirements • Accuracy
• Latency
• etc.
System dynamic requirements • overshoot
• damping
• etc.
Top level
Requirements Specify
Stakeholders
Customer
Project proposal
…
Functional model
Usage model
…
• Top level requirements are inherited
from different sources (Stakeholders,
Customer, Project proposal)
• They are formulated very general
e.g.: „The aircraft must land
automatically on RWY …“
• How can more specific requirements
for the system be derived?
• How can the knowledge of the
system’s physical properties be
utilized?
Deriva
tion
?
L0
L1
…
L(n-1)
Ln
Ph
ysic
s
Eq
ua
tio
n o
f m
otio
n
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
14 Model Based Requirements Assessment
General Procedure for Stochastic Variables
• In case of stochastic variables “Monte Carlo” Methods (can) be utilized for
evaluating the probability of an event 𝐹
• One can then apply the same methods as in deterministic case to find the
system Parameter configuration that barely satisfies a certain probability level
𝑃(𝐹)
• Desired / minimum Probability level 𝑃𝑑𝑒𝑠 𝐹 can be inherited from certification documents (CS-AWO, STANAG, …)
“Monte Carlo” Methods with sample size 𝑁 and 𝑛 = 1…𝑁
Suitable model
𝑓 𝚯, 𝒚
Requirements Derivation
𝒚𝑛 Constraints for event 𝐹
𝑔𝐹 𝒚𝑛 = 1, 𝑖𝑓 𝑠𝑎𝑡𝑖𝑠𝑓𝑖𝑒𝑑0, 𝑖𝑓 𝑣𝑖𝑜𝑙𝑎𝑡𝑒𝑑
System parameters to
be constrained
𝚯
𝚯𝑛
Physics
Probability
for event 𝐹 𝑃 𝐹
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
15 Model Based Requirements Assessment
ATOL Example: L1 Requirements
Position requirements Attitude requirements Aerodynamic & Structure
• In a first step simple geometric models are applied for L1-level
requirements
Geometric inequality constraint
Δ𝑦 <𝑏𝑅𝑊𝑌
2
Δ𝑥 <𝑙𝑅𝑊𝑌 − 𝑙𝑑𝑒𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛
2
2 ⋅ Δ𝑥
2⋅Δ
y
Geometric inequality constraint
0 < Θ < Θ𝑚𝑎𝑥
ΔΨ < ΔΨ𝑚𝑎𝑥
ΔΦ < ΔΦ𝑚𝑎𝑥
Aerodyn. Const.
𝛼𝐴 < 𝛼𝑆𝑡𝑎𝑙
𝑉𝐴 > 𝑉𝐴𝑚𝑖𝑛
Structural inequality
constraints
VK < 𝑉𝐾𝑚𝑎𝑥
𝑛𝑧 < 𝑛𝑧𝑆𝑡𝑟𝑢𝑐𝑡
• Quantitative constraints for system outputs have been found applying
simple (geometric) models
• These constraints are now utilized for deriving subordinate system
Requirements
Aircraft wing strike1
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
16 Model Based Requirements Assessment
ATOL L1 Example: Probabilistic Methodology
• Aircraft states (position/velocity)
afflicted with total error
o 5 control errors:
constant
o 7 navigation errors:
normal (Gaussian) distribution
• Integration into Simulink model as additional inputs Control_error, Navigation_error
• Application of statistical methods:
“Reliability methods“
o Generation of n samples
of navigation errors
according to statistical distributions
o n Simulink model executions
0 0.5 1 1.5 2 2.5 3 3.5 40
0.1
0.2
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0.8
0 0.5 1 1.5 2 2.5 3 3.5 40
0.2
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1.2
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n times ( n samples)
0 0.5 1 1.5 2 2.5 3 3.5 40
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1.2
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1.8
2
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0.5
0.6
0.7
0.8
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
17 Model Based Requirements Assessment
ATOL L1 Example: Failure Probabilities
• n simulated landing maneuvers with different touchdown parameters
• Criteria for successful landings o Multiple touchdown parameters are limited upwards and downwards:
o Violation of at least 1 parameter limit damage or loss of the aircraft
• Computation of failure probability o Probability that a landing maneuver results in a missed approach
o Touchdown parameters of n simulations evaluated by statistical methods
xTD,min
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
18 Model Based Requirements Assessment
ATOL L1 Example: Subset Simulation
• Adaptive simulation method
o Iterative and strategic placement of samples for computation of Pr(F)
significant reduction of number of samples nS required for a good level of accuracy
o Particularly useful to small probabilities
• Original failure domain
o Generation of samples (e.g. 200)
according to their probability distribution (e.g. Gaussian)
o Too few samples within failure domain
inaccurate computation of corresponding failure probability
• Solution: Creation of sub-events
o Enlargement of failure domain by creating intermediate failure events Ei
o Principle:
The higher the failure probability, the more accurate its calculation
with a given/unchanged number of samples.
decrease of probability: 1% 0,01%
number of samples: x 100 with Monte Carlo Simulation, x 2 with Subset Simulation
Failure domain F
-5 0 5-5
0
5Step #0
u1
u2
Non-failure
domain
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
19 Model Based Requirements Assessment
ATOL L1 Example: Subset Simulation Example
-5 0 5-5
0
5Step #1
u1
u2
-5 0 5-5
0
5Step #1
u1
u2
Placement of samples:
Monte Carlo Simulation
Definition of sub-event E0
so that probability Pr(E0) 0,1
Samples (200 per step) Samples within failure domain Samples for next step
Placement of samples: Special technique (“Markov Chain Monte Carlo“)
All samples for step 1 within failure domain E0!
Definition of sub-event E1
so that conditional probability Pr(E1|E0) 0,1
E0 E0 E1
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
20 Model Based Requirements Assessment
ATOL L1 Example: Subset Simulation Step
-5 0 5-5
0
5Step #4
u1
u2
-5 0 5-5
0
5Step #4
u1
u2
-5 0 5-5
0
5Step #3
u1
u2
-5 0 5-5
0
5Step #3
u1
u2
-5 0 5-5
0
5Step #2
u1
u2
-5 0 5-5
0
5Step #2
u1
u2
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
21 Model Based Requirements Assessment
ATOL L1 Example: Subset Simulation Final
-5 0 5-5
0
5Final
u1
u2
Step #0
Step #1
Step #2
Step #3
Step #4
Step #5
-5 0 5-5
0
5Step #5
u1
u2
-5 0 5-5
0
5Step #5
u1
u2
Sub-event E5 = original failure domain F
Estimation of conditional probability
Pr(E5|E4) = Pr(F|E4) (e.g. by counting the samples)
Pr(F) = Pr(F|E4) · Pr(E4|E3) · Pr(E3|E2) · Pr(E2|E1) · Pr(E1|E0) · Pr(E0)
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
22 Model Based Requirements Assessment
Requirements Definition: Example 1
• Derivation of requirements o Definition of a targeted level of safety (e.g. Pr(F) = 1%)
o Variation of:
– Performance of controllers control errors
– Performance of sensors /measurement accuracy probability distributions (standard deviation)
– Wind conditions etc.
• Example: Impact of sensory equipment on failure probability o Different sensors different measurement errors (computation of Pr(F) for each of them)
o E.g. measurement errors in vertical speed and horizontal position (x, y)
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
23 Model Based Requirements Assessment
Requirements Definition: Example 2
• Derivation of requirements o Definition of a targeted level of safety (e.g. Pr(F) = 1%)
o Variation of:
o Performance of controllers control errors
o Performance of sensors /measurement accuracy probability distributions (standard deviation)
o Wind conditions etc.
• Example: Impact aircraft mass and wind on failure probability o 3 different landing masses: 92,25 kg / 106,5 kg / 125 kg (computation of Pr(F) for each of them)
o Wind conditions: no wind / maximum wind (15 kts tail, 10 kts cross)
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
26 Model Based Control Design
Introduction
• Flight Control Design is not a
single point issue but spans a
large operation envelope /
domain
– Plant analysis
– Controller Design
– Gain Design
• Controller assessment needs
to be automated
– Non-compliance needs to be
detected automatically
– Reports ands evaluations need to
be organized automatically
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Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
27 Model Based Control Design
Process Steps
High Fidelity Model
Reduced Control Design Model
System Analysis
Control Structure Design
Validation
Gain Design
Automated Assessment on reduced Model
(clsassical: Linear)
Automated Assessment on High Fidelity Model
Configuration & Target Integration
Virtual Flight Test: HIL C/L clearance
Flight Test
Failure ModelsUncertainty
Models
Systems: Sensors/Actuation
Disturbance Models
Validation & Verification
• MathWorks tools are used during the
whole Controller Design and Verification
Process
• The usage of an integrated toolchain
throughout the process generates
benefits at the interfaces between the
particular process steps.
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
28 Model Based Control Design
Trim and Linearize
Trim applications:
Computation of performance data:
= p(1)
= x S (1)
= x S (2)
= Haddad
= Haddad
= Haddad
= Haddad
= Haddad
egal
egal
egal
= p(2)
= x S (3)
= x S (4)
= x S (5)
= x S (6)
x
u
V a
b
p
q
r
F
Q
Y l m
h
x h
z
T d
pxfr ,S
r
V
a b
p q r
S x a
b x h
z
T d
p
V
h g
Y
Haddad Constraint
[ ]
Y Q F
, , , ,
, , , ,
K K K HD V f
r q p
g b a
a b
g Y V
0pxfr!
, S
Nonlinear Equation solver
Numerical Linearization
= r(1)
= r(2)
= r(3)
= r(4)
= r(5)
= r(6)
= 0 ( auto )
= 0 ( auto )
= p(4) ( auto )
egal
egal
x
V
a b
p q r
F
Q
Y
l m
g sin × V h
0 B Y F
yuxh
xuxf
),(
),(
yyuuxxh
xuuxxf
ddd
ddd
000
00
),(
),(
u
x
u
x
d
d
0
0
y
x
d
d
Numerical Linearization:
linsyslinsyslinsys uAxx
linsyslinsyslinsys DuCxy
- Flight envelope parameter configurable
(Altitude, Velocity, Center of gravity, flap
configuration,…).
- Automated trim point calculation for
each flight condition.
- Output of all important trim result data
(trim success, used trim strategy,
number of iterations, …) for evaluation
of the trim results.
- Free configuration of the desired model
parameters (States, Inputs, outputs),
which shall be extracted from the
nonlinear model.
- For each trim point, a linearized model
of the nonlinear equation system is
generated
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
29 Model Based Control Design
Example: DA42 Longitudinal Controller Design with MATLAB
10-1
100
101
102
-30
-20
-10
0
10
Mag
nitude (d
B)
Frequency (rad/s)
Config 1 Flaps = 0 Gear = 0
10-1
100
101
102
-30
-20
-10
0
10
Phase (deg)
Frequency (rad/s)
Config 2 Flaps = 27 Gear = 0
10-1
100
101
102
-30
-20
-10
0
10
Mag
nitude (d
B)
Frequency (rad/s)
Config 3 Flaps = 42 Gear = 0
10-1
100
101
102
-30
-20
-10
0
10
Phase (deg)
Frequency (rad/s)
Config 4 Flaps = 0 Gear = 1
10-1
100
101
102
-30
-20
-10
0
10
Mag
nitude (d
B)
Frequency (rad/s)
Config 5 Flaps = 27 Gear = 1
10-1
100
101
102
-30
-20
-10
0
10
Phase (deg)
Frequency (rad/s)
Config 6 Flaps = 42 Gear =1
10-1
100
101
102
-30
-20
-10
0
10
Frequency (rad/s)
Ma
gn
itu
de
(d
B)
Bode - All borders combined
Config 1 Flaps = 0 Gear = 0
Config 2 Flaps = 27 Gear = 0
Config 3 Flaps = 42 Gear = 0
Config 4 Flaps = 0 Gear = 1
Config 5 Flaps = 27 Gear = 1
Config 6 Flaps = 42 Gear = 1
Trim Linearization
„qcom-CSAS“
Controller
G (s)dS
Stick-to-Command
Shape
V0/g cosg sinF tanFqturn,c
Aircraft qhcmd, el
FP
GA(s)-
H
FKds,q
qcmd Eq
qMEAS
hcmd hmecqcmd,s
Prefilter Delayafter FCC
Delayto FCC
Sensor
Turn compensation
Actuator
Plant
Extended Plant Dynamics
Controller Design
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.450
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (seconds)
Am
plitu
de
Pe
ak re
sp
on
se
Ris
etim
e
Pla
nt
an
aly
sis
H
Q
req
uir
em
en
ts
10-1
100
101
102
-50
-40
-30
-20
-10
0
10
Actuator
assumption
Delay
assumption
CAP C* BW etc.
HQ Requirements
Automatic trim and
linearization for the whole
flight envelope (h – Ma)
as well as all configur-
ations (gear, flaps)
Automatic HQ analysis
(Dropback, CAP, C*,
Neal Smith, Band-
width, ...) based on w0 and T (z = 0.71) for desired short period
dynamics
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
30 Model Based Control Design
Example: DA42 Longitudinal Controller Design with MATLAB
„qcom-CSAS“
Controller
G (s)dS
Stick-to-Command
Shape
V0/g cosg sinF tanFqturn,c
Aircraft qhcmd, el
FP
GA(s)-
H
FKds,q
qcmd Eq
qMEAS
hcmd hmecqcmd,s
Prefilter Delayafter FCC
Delayto FCC
Sensor
Turn compensation
Actuator
Plant
Extended Plant Dynamics
Controller implementation
-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80
0.5
1
1.5
2
2.5
3
3.5
4
db/qss
qm
ax/q
ss
Level 1
Level 2/3
Level 1/2
0 0.5 1 1.5 2 2.50
0.5
1
1.5
2
2.5
3
3.5
4Gibson´s dropback criterion
peak
/qss
qpeak/
qss
Excessive (PIO possible)Acceptable
-350 -300 -250 -200 -150 -100 -50 0-30
-25
-20
-15
-10
-5
0
5
10
15
20
25
-20dB
-14dB
-10dB
-8dB
-6dB
-4dB
-3dB
-2dB
-1dB0dB1dB
2dB
3dB
6dB
-10°
-20°
-30°
-40°
-50°
-60°
-70°
-80°
-90°
-100°
-110°
-120°
-130°
-140°
-150°
-160°
-170°
-180°
-190°
-200°
-210°
-220°
-230°
-240°
-250°
-260°
-270°
-280°
-290°
-300°
-310°
-320°
-330°
-340°
-350°
Phase des ORK [°]
Fre
quenzgangbetr
ag d
es O
RK
[dB
]
-40 -20 0 20 40 60 80-2
0
2
4
6
8
10
12
14
16
Level 1
Level 2
Level 3
Lag Lead
Pilot Phase Compensation [deg] at Omega = 3.5 rad/s
Clo
sed L
oop R
esonance [
dB
]
100
101
102
10-1
100
101
102
LEVEL 2
LEVEL 1
LEVEL 1
LEVEL 2
LEVEL 3
Cla
s. I,II-C
,IV
Cla
s. II-L
,III
LE
VE
L 2
. C
las. I,II-C
,IV
LE
VE
L 2
. C
las. II-L
,III
0.0960.15
3.610.0
Category C flight phases CAP boundaries
CAPNote: The boundaries for values n/a
outside the range show n are defined
by straight-line extensions
n/a (g/rad)
wn
SP
(rad/sec)
10-1
100
10-2
10-1
100
101
Category A & C flight phases (CAP - damping ratio boundaries)
zSP
CAP
LEVEL 1
LEVEL 2
LEVEL 3
-2
-1
0
1
2
3
q [
°/s
]
Closed loop pull & release Open loop pull & release
0 2 4 6 8 10-4
-2
0
2
4
Time [s]
q [
°/s
]
Closed loop doublet
0 2 4 6 8 10
Time [s]
Open loop doublet
-20
-10
0
10
20
Magnitude [
dB
]
Mismatch - Amplitude
10-1
100
101
102
-150
-100
-50
0
50
100
150
Frequency [rad/s]
Phase [
°]
Mismatch - Phase
• Automatic controller
stability and Handling
Qualities analysis
featuring LOES (low
order system generation)
for all trim conditions and
configurations (~12000
points)
• Automatic Gain design
for the controller to
comply the requirements
for all 12000 points.
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
31 Model Based Control Design
Example: DA42 Longitudinal Controller Design with MATLAB
0 20 40 60 80 100 120 140 160 180-10
-5
0
5
10
Time (s)
g-T
rackin
g / g (
°)
Tracking - Josef-Niederl-Run-09-2013-05-22-19-19-Con-act-1-K-stick-11.74-MC-grad-1200.mat
Error-RMS: 1.5042 Error-rate-RMS: 164.8987 Finetracking-RMS: 0.29065
10-1
100
-30
-20
-10
0
10
20
Frequency (rad/s)
Magnitu
de (
dB
)
YC / Y
CL / w
co,AC = 0.89 rad/s
YC
YCL
10-1
100
0.2
0.4
0.6
0.8
1
Frequency (rad/s)
Kohäre
nz
Kohärenz
10-1
100
-20
-10
0
10
20
Frequency (rad/s)
Magnitu
de (
dB
)
YP x Y
C
wCO
= 0.69193 wCO-A
= 0.70578
Comp.
Pursuit
10-1
100
0
0.2
0.4
0.6
0.8
1
Frequency (rad/s)
Kohäre
nz
Kohärenz
10-1
100
-10
0
10
20
30
Frequency (rad/s)
Magnitu
de (
dB
)
YP - Pilotgain@co: 0.79
YP
YP-pursuit
10-1
100
0
0.2
0.4
0.6
0.8
1
Frequency (rad/s)
Kohäre
nz
Kohärenz
0 20 40 60 80 100 120 140 160 180-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Deflection [
-]
Time (s)
Stick - : 0.097974 RMS: 0.097972 Mittelw.: -4.7533e-005 Force-RMS: 8.9456 Speed-RMS: 0.30615
0 20 40 60 80 100 120 140 160 180-50
-25
0
25
50
Forc
e [
N]
dS
dKraft
-Stick
• Simulator flight testing with 10 pilots
• Automatic data analysis of the simulation results with MATLAB
• Export of the analysis results to Excel
Microsoft
Excel
Automatic simulation
result analysis
Export:
• Analysis results.
• Automatic report
generator.
• Automatic
compliance matrix.
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
Flight Control Computer & FCC Algorithms
Controller
Algorithms
Simulink Coder
Embedded
Coder
Navigation &
Data Fusion
MATLAB
Embedded
Simulink Coder
Embedded
Coder
Startup, Runtime, Robustness and Interface Framework
ANSI C
Real Time Operating System
COTS
Electronic Hardware
COTS and Special Built
Sensors Actuators
Embedded System Design
Architecture of a Flight Control System 32
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
Embedded System Design
Tool Chain Structure and Workflow for Power PC 33
Model Development and Verification
• Embedded MATLAB, Simulink, Stateflow
• Simulink Model Advisor, Report Generator and Model Coverage
Source Code Development and Verification
• Simulink Embedded Coder
• Simulink Code Inspector
• PolySpace
• VerOCode and VeroSource (structural code coverage)
Debugging and Target Testing
• Lauterbach TRACE 32 Debugger (debugging and PIL)
• Bernecker + Rainer (B&R) (hardware in the loop test bed)
Startup, Runtime, Robustness and
Interface Framework
ANSI C
Embedded Coder
Source Code
Object Code
Target Deployment
In-Circuit
Debugging
Integrated
Debugging
Hardware &
Processor In the Loop
(HIL / PIL)
Model Link
Integrated
Build
Code
Reference
+
Structural Coverage
on Target
Simulink
Code
Inspector
• Generation and deployment of
source and object code to target
environment
• Strategy on qualifiable
verification tools
• All tools in use in aerospace
applications
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
Model Based Testing
Testing in Final Target Environment
34
DA42 Total System Simulation HIL
Flight Control
Computers (FCCs)
© B&R Automation
Actuator Test Bench Diamond DSIM
Flight Training Device
Aircraft Flight Test
Installation
High Fidelity
Aircraft Model
on Automation PC
Processor In The Loop Test Bench
Traceability between
Simulink and Trace32
Debugger Integration
in Simulink
• Target testing is required for generated and handwritten code (make it efficient!)
• PIL is a proven method for testing equivalence to Simulink (reuse of test cases)
• HIL testing is important to develop good and robust control algorithms (e.g. latencies)
• Requirements based testing of the final product is a fundamental idea of DO-178 B/C
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
• MBD helps engineers to keep focus on technical task
• Simulation / analysis based derivation of requirements:
basis for consistent requirements and
powerful method for early validation through simulation
• Continuity of development process
from requirements capturing to verification on target system
• Main challenge:
Improvement of automated design und analyses of algorithms over large
envelope
• Continuity and automation opens opportunities for SMEs to enter avionics
market
35
Summary
Institute of
Flight System Dynamics Modellbasierte Entwicklung und Test von Regelungssystemen
Prof. Dr.-Ing. Florian Holzapfel
Thank you for your attention