Frequency (rad/sec)

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PENNSTATE
1 8 5 5
Project PS 5.2
Simulation and Control of Shipboard Launch and
Recovery Operations
PI: Asst. Prof. Joseph F. Horn
Tel: (814) 865 6434 Email: joehorn@psu.edu
Graduate Students:
Dooyong Lee, PhD Candidate
Derek Bridges, PhD Candidate
2005 RCOE Program Review
May 3, 2005
PENNSTATE
1 8 5 5
Background / Problem Statement
• The shipboard launch and recovery task is one of the most
challenging, training intensive, and dangerous of all
rotorcraft operations
• The helicopter / ship dynamic interface (DI) is difficult to
accurately model
• Industry and government could use better tools for
analyzing shipboard operations to reduce the flight test
time and cost to establish safe operating envelopes
• Workload requirements could be reduced using tasktailored control systems for shipboard operations
Technical Barriers
• Accurate models require the integration of the time-varying
ship airwake and the flight dynamics of the helicopter
• Currently pilot workload requirements and HQ analysis
must be assessed using expensive flight tests and piloted
simulation. Better engineering tools needed to reduce
costs for analyzing current and future ships / aircraft.
• A practical, fully autonomous or piloted assisted landing
AFCS has not yet been developed, need to assess
requirements and potential benefits
PENNSTATE
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Task Objectives:
• Develop advanced simulation model of the shipboard dynamic interface
• Validate the model using available test data
• Use the model to develop advanced flight control systems to address workload issues in the DI
Approaches:
• Develop a MATLAB/SIMULINK based simulation of the H-60 based on GenHel (will facilitate
model improvements and control law development)
• Develop a maneuver controller to simulate pilot control during launch and recovery operations
• Integrate simulation with ship airwake models, investigate relative effects of steady and timeaccurate CFD wakes, and stochastic wake models based on CFD and flight test data
• Simulate UH-60 operating off LHA and validate model with JSHIP flight test data
• Develop new concepts in AFCS design for shipboard operations
• Develop a real-time simulation facility of shipboard operations (using DURIP funds)
Expected Results:
• A simulation tool for analyzing handling qualities in the DI and predicting safe landing envelopes
• A methodology for designing a task-tailored AFCS for shipboard operations
• A conceptual design of an autonomous landing systems and assessment of the system
requirements for such a system (possible UAV applications)
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PSU DI Simulation Program
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• Developed “tunable” pilot model for different levels of tracking tolerance
• Integrated CFD solutions of ship airwake with non-linear flight dynamics model
• Demonstrated using UH-60A / LHA combination, same as JSHIP flight test program
• Validated model with flight test data from JSHIP program
• Evaluate task tailored control laws
Human pilot model
(Optimal control model)
Time-accurate
ship airwake from CFD
Real-time simulation
Matlab based
DI simulation program
(based on GENHEL)
Stochastic
airwake model
Task-tailored
control law design
(using CONDUIT)
Validation
with flight test data
(from JSHIP program)
PENNSTATE
Stochastic Ship Airwake Modeling
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• A method for extracting equivalent airwake disturbances from flight test
data (or high order simulation model) has been developed
 Method is similar to that used for turbulence models developed at NASA Ames (Ref.
Labows and Tischler et al, MacFarland - SORBET Model)
 Filters are derived to simulate the spectral properties of the airwake, can compare to
traditional turbulence models (e.g. von Karman, Dryden)
 Spectral filters are based on von Karman model, and modified to fit the desired forms
of spectral characteristics
• Stochastic airwake model can be readily used for flight control
optimization
Designed to fit the
spectral properties
of the airwake
Stochastic airwake model
White noise
Linear filter
+
Pilot stick inputs
+
+
Helicopter
Dynamics
Optimized to
reject disturbances
SAS
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Stochastic Ship Airwake Modeling
PSD of vertical gust component, (ft/sec)2/(rad/sec)
Sample Results for Vertical Component, 0° WOD
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“Best Fit” Spectral Filter
Lw =10.7156 ft, sw = 4.81 ft/sec
Extracted from simulation
with full time-varying airwake
2
Lw 
L
L  
1  1.5913 w s  0.9173 w s  
V 
V
 V  

H s  
2
3
L
L 
L 
1  2.5601 w s  3.3169  w s   1.8985  w s 
V
V 
V 
von Karman Turbulence Model
Lw = 37.8667 ft, sw = 2.8067 ft/sec
2
Lw 
Lw
 Lw  
4s w
1  2.7478 s  0.3398 s 
V 
V
 V  
H s  
2
3
L
L 
L 
1  2.9958 w s  1.9754 w s   0.1539 w s 
V
V 
V 
Frequency (rad/sec)
4s w
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Stochastic Ship Airwake Modeling
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• Comparison of response with stochastic airwake model, equivalent
disturbances and full time-varying airwake (spectral data averaged over
five runs)
Stochastic Airwake
Autospectra identified
Equivalent Airwake Disturbances
Full Time-Varying Airwake
- Input autospectrum(30 deg WOD), dB
PED
PED
COL
COL
LON
LON
LAT
LAT
- Input autospectrum(0 deg WOD), dB
by CIFER
Frequency (rad/sec)
Frequency (rad/sec)
PENNSTATE
Task-Tailored Control Design
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• Using CONDUIT to optimize SAS gains
• Include ADS-33 HQ specs as constraints in optimization
• Include longitudinal acceleration feedback and pitch attitude feedback
Airwake
Spectral
Filters
Longitudinal
acceleration
feedback to
improve gust
response
Pitch attitude
feedback to
provide
closed-loop
stability
Optimize for
minimal gust
response
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Stability Augmentation System
• Optimize gains using CONDUIT
 Based on phase-lag compensator
 Design parameters include the prefix “dpp_”
Roll SAS
Pitch SAS
Yaw SAS
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HQ specs
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• Selected design specs from CONDUIT as constraints
 Closed-loop eigenvalues(EigLcG1), Gain/Phase margin(StbMgG1),
Crossover frequency(CrsLnG1), Bandwidth for roll/pitch(BnwAtH1)
• New spec for disturbance rejection(DisRnL1)
 Based on psd of angular rate response to corresponding gust input
White
noise
Transfer function
q( s)
H ( s) 
qg ( s)
PSD
Magnitude [dB]
(Example) – Pitch rate
Level III
Level II
Level I
Frequency [rad/sec]
PENNSTATE
HQ Specification Window
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• Original SAS configurations - 30 degree WOD condition
(1)
roll
pitch (2)
yaw (3)
CrsLnG1 (1)
CrsLnG1 (2)
CrsLnG1 (3)
EigLcG1 (1)
EigLcG1 (2)
EigLcG1 (3)
StbMgG1 (1)
StbMgG1 (2)
StbMgG1 (3)
BnwAtH1 (1)
BnwAtH1 (2)
DisRnL1 (2)
DisRnL1 (3)
DisRnL2 (1)
H
H
S
J
H
J
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HQ Specification Window
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• Modified SAS configurations - 30 degree WOD condition
(1)
roll
pitch (2)
yaw (3)
CrsLnG1 (1)
CrsLnG1 (2)
CrsLnG1 (3)
EigLcG1 (1)
EigLcG1 (2)
EigLcG1 (3)
StbMgG1 (1)
StbMgG1 (2)
StbMgG1 (3)
BnwAtH1 (1)
BnwAtH1 (2)
DisRnL1 (2)
DisRnL1 (3)
DisRnL2 (1)
H
H
S
J
H
J
PENNSTATE
Simulation Results - Hovering Flight
1 8 5 5
• Angular rate responses (deg/sec)
Result with Original SAS configurations
Result with Optimized SAS configurations
- 30 degree WOD
R, deg/sec
R, deg/sec
Q, deg/sec
Q, deg/sec
P, deg/sec
P, deg/sec
- 0 degree WOD
Time [sec]
Time [sec]
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Simulation Results - Hovering Flight
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• SAS outputs (%)
Result with Original SAS configurations
Result with Optimized SAS configurations
- 30 degree WOD
YSAS, %
YSAS, %
PSAS, %
PSAS, %
RSAS, %
RSAS, %
- 0 degree WOD
Time [sec]
Time [sec]
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Simulation Results - Hovering Flight
• Pilot stick inputs (%)
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Lateral cyclic input : Left  0%, Right  100%
Longitudinal cyclic input : Forward  0% , Aft 100%
Collective input : Down  0%, Up  100%
Pedal input : Left  0%, Right  100%
Result with Original SAS configurations
Result with Optimized SAS configurations
- 30 degree WOD
PED, %
PED, %
COL, %
COL, %
LON, %
LON, %
LAT, %
LAT, %
- 0 degree WOD
Time [sec]
Time [sec]
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Simulation Results - Hovering Flight
• Angular rate autospectrum (dB)
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Autospectra identified by CIFER
Result with Original SAS configurations
Result with Optimized SAS configurations
- 30 degree WOD
R, dB
R, dB
Q, dB
Q, dB
P, dB
P, dB
- 0 degree WOD
Frequency [rad/sec]
Frequency [rad/sec]
PENNSTATE
Simulation Results - Hovering Flight
• Pilot stick input autospectrum (dB)
1 8 5 5
Autospectra identified by CIFER
Result with Original SAS configurations
Result with Optimized SAS configurations
- 30 degree WOD
PED, dB
PED, dB
COL, dB
COL, dB
LON, dB
LON, dB
LAT, dB
LAT, dB
- 0 degree WOD
Frequency [rad/sec]
Frequency [rad/sec]
PENNSTATE
H infinity Controller for SAS
1 8 5 5
• Include frequency-dependent weight functions for control inputs and outputs
• Produce a controller K∞
 to reduce the tracking deviations
 to reject disturbances
• We is a high-gain low-pass filter for good tracking and disturbance rejection
• Wu is a low-gain high-pass filter to improve the robustness and to limit the
control activity
Gust Filter
(Wg)
dw
ref
d
+ +
+
-
dt
Aircraft
(UH-60)
+
+y
Weighting
(We)
ee
eu
Weighting
(Wu)
u
H∞ controller
(K∞)
PENNSTATE
H infinity Controller Design
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• Obtain a controller solving a classical 4-block problem
 based on 8-rigid-state linearized aircraft model
 3 diagonal components of weighting functions
 iterate to find the optimal weighting parameters




x  Ax  B1w  B2u




z  C1 x  D11w  D12u




y  C2 x  D21w  D22u

 x   A
  
 z    C1
 y  C
   2
,

x=
Aircraft
We
Wu

B1
B2   x 

D11 D12   w



D21 D22   u 

, w
=
dw
dt
,

z=
ee
eu
A
G   C1
C2
14-state H∞ controller

,u
=
rsas
psas
ysas
B2 
D11 D12 
D21 D22 
B1
,

y=
p
q
r
PENNSTATE
Simulation Results - Hovering Flight
1 8 5 5
• Angular rate responses (deg/sec)
Result with Original SAS configurations
Result with Optimized SAS configurations
Result with H infinity controller
- 30 degree WOD
R, deg/sec
R, deg/sec
Q, deg/sec
Q, deg/sec
P, deg/sec
P, deg/sec
- 0 degree WOD
Time [sec]
Time [sec]
PENNSTATE
Simulation Results - Hovering Flight
1 8 5 5
• SAS outputs (%)
Result with Original SAS configurations
Result with Optimized SAS configurations
Result with H infinity controller
- 30 degree WOD
YSAS, %
YSAS, %
PSAS, %
PSAS, %
RSAS, %
RSAS, %
- 0 degree WOD
Time [sec]
Time [sec]
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Simulation Results - Hovering Flight
• Pilot stick inputs (%)
1 8 5 5
Result with Original SAS configurations
Result with Optimized SAS configurations
Result with H infinity controller
- 30 degree WOD
PED, %
PED, %
COL, %
COL, %
LON, %
LON, %
LAT, %
LAT, %
- 0 degree WOD
Time [sec]
Time [sec]
PENNSTATE
Simulation Results - Hovering Flight
• Angular rate autospectrum (dB)
Result with Original SAS configurations
Result with Optimized SAS configurations
Result with H infinity controller
Autospectra identified by CIFER
- 30 degree WOD
R, dB
R, dB
Q, dB
Q, dB
P, dB
P, dB
- 0 degree WOD
Frequency [rad/sec]
1 8 5 5
Frequency [rad/sec]
PENNSTATE
Simulation Results - Hovering Flight
• Pilot stick input autospectrum (dB)
Autospectra identified by CIFER
Result with Original SAS configurations
Result with Optimized SAS configurations
Result with H infinity controller
- 30 degree WOD
PED, dB
PED, dB
COL, dB
COL, dB
LON, dB
LON, dB
LAT, dB
LAT, dB
- 0 degree WOD
Frequency [rad/sec]
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Frequency [rad/sec]
PENNSTATE
Rotorcraft Flight Simulator
1 8 5 5
• Flight dynamics model is based
on Genhel
• Use FlightGear environment for
visualization
• Integrated with time-varying
airwake data from CFD
• Integrated with CHARM
freewake model
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Schedule and Milestones
Tasks
• Update GenHel Simulation for
shipboard simulation
• Develop simplified MATLAB
Sim for control design
• Interface GenHel with ship air
wake solutions and ship motion
• Develop maneuver controller
• Validation of DI simulation
(using JSHIP data)
• Develop stochastic airwakes
disturbance model and develop
physical understanding
• Develop real-time simulation
capability at PSU
• Incorporate CHARM free wake
into the model
• Task tailored control law
design, support with real-time
simulator at PSU
• Lee PhD Degree
• Derek Bridges PhD Degree
2001
2002
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2003
2004
2005
Completed
Short Term
Long Term
2006
PENNSTATE
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2004 Accomplishments
• Developed stochastic airwake disturbance model for 0° and 30° WOD, use for
off-line analysis, real-time simulation and flight control design
• Real-time simulation facility is ready, integrated with time-varying airwake model
and CHARM freewake model
• Developed task-tailored control laws using CONDUIT and H infinity control
method
• Presented results at 2004 AIAA AFM conference, paper published in AIAA
Journal of Aircraft, paper submitted to Journal of Aerospace Engineering
(special issue on shipboard aviation)
Planned Accomplishments for 2005
• Will present results at 2005 AHS Forum and submit as journal article
• Continue to update and improve model, include the deck ground effects
• Further study in task tailored control laws to improve disturbance rejection
• Expand flight control design efforts, autonomous landing flight control system,
position hold over ship deck
• Investigate use of equivalent airwake disturbances as tool for validating ship CFD
airwake models.
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Technology Transfer Activities:
• Presented results at 2004 AIAA AFM Conference
• Briefing to Navy Flight Dynamics Group at in Summer 2004, planning further interaction.
Leveraging or Attracting Other Resources or Programs:
• Obtained JSHIP flight test data for validation, Cdr. Kevin Delemar at NRTC is contact
• Integrating with CHARM free wake model
• Integrated model and controllers with simulation facility developed under DURIP funds
Recommendations at
the 2004 Review:
Get with Navy to focus the project and
also to interface with CFD activities (flow
field).
Actions Taken:
Met with Navy. Received recommendations and
we are planning more interaction. Proposed use
of equivalent airwake disturbance model as tool
for validation of CFD airwakes.
PENNSTATE
Overview of Accomplishments
2001-2005
1 8 5 5
Stochastic Airwake Disturbance Model
• Method for extracting equivalent disturbances from simulation
with full CFD airwake (can also be applied to flight test data)
• Derived spectral filters to represent airwake disturbances
PSD of vertical gust component, (ft/sec)2/(rad/sec)
Advanced Simulation Model for Shipboard Operations
• Interface with time accurate CFD solutions of ship airwake
• High order Peters-He inflow
• Tunable OCM pilot model
• MATLAB / Simulink version of model for rapid
development and control design
• Validation against JSHIP flight test data
• Implemented in real-time simulation facility at PSU
“Best Fit” Spectral Filter
Lw =10.7156 ft, sw = 4.81 ft/sec
Extracted from simulation
with full time-varying airwake
H s  
4s w
2
Lw 
L
L  
1  1.5913 w s  0.9173 w s  
V 
V
 V  

2
3
Lw
L 
L 
s  3.3169  w s   1.8985  w s 
V
V 
V 
1  2.5601
von Karman Turbulence Model
Lw = 37.8667 ft, sw = 2.8067 ft/sec
H s  
4s w
2
Lw 
L
L  
1  2.7478 w s  0.3398 w s  
V 
V
 V  
2
3
Lw
L 
L 
s  1.9754 w s   0.1539 w s 
V
V 
V 
1  2.9958
Frequency (rad/sec)
Task-Tailored Control Design for Shipboard Operations
• Optimized SAS for operation in airwake using CONDUIT®
• Use spectral filters in control synthesis
• Optimized SAS using H∞ synthesis
Airwake
Spectral
Filters
Longitudinal
acceleration
feedback to
improve gust
response
Pitch attitude
feedback to
provide
closed-loop
stability
Publications
• 5 conference papers, 1 journal paper published, 1 journal paper under review
Optimize for
minimal gust
response
Future Path
PENNSTATE
1 8 5 5
Additional Basic Research
• Should pursue similar analyses to study effects of building airwakes on UAVs
operating in urban areas, proposed as follow on for next RCOE
• Potential to investigate impacts on shipboard handling qualities requirements
– Maritime ADS-33.
• Could make further efforts to pursue the fully coupled problem, model effect
of rotor wake on ship airwake, would need more CFD expertise
Transition to Applications / Applied Research
• Apply equivalent airwake disturbance method to validate ship airwake CFD
analysis. Airwake disturbance can be extracted from flight test and compared
to simulation with CFD wake
• Use stochastic airwake model as a simplified and more compact model for
use in trainers
• Apply maneuver controller and simulation for analysis of new aircraft and new
ship designs
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