PENNSTATE
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Project PS 5.1
Carefree Maneuvering Control Laws for Rotorcraft
PI: Asst. Prof. Joseph F. Horn
Tel: (814) 865 6434 Email: joehorn@psu.edu
Graduate Student: Nilesh Sahani
2005 RCOE Program Review
May 3, 2005
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Background / Problem Statement
 Rotorcraft are constrained by multiple flight envelope limits. It is a major contributor to pilot
workload.
 Carefree Maneuvering (CFM) systems allow us to avoid using conservative operational
envelopes. Pilots can use maximum performance and maneuverability without sacrificing
safety, reliability, handling qualities
 Can be achieved through cueing systems or AFCS
Normal Pilot Cues:
Out the window, instruments,
acceleration, vibration, etc …
Active Control
Stick
Pilot
Pilot
Force
Technical Barriers
 Robust and efficient algorithms to predict onset
of limits and control constraints to avoid the
limits
 Develop systems that do not take away a pilot’s
option to exceed a limit and do not annoy the
pilot
 Avoid adding costly sensors or systems
 Need comprehensive systems that handle
multiple limits and multiple control axes
 Integration of envelope cueing with envelope
limiting AFCS
 Quantify the payoffs of CFM in terms of
handling qualities, safety, reliability,
maintainability
 Exploit potential use of CFM technology on
UAV’s
Aircraft
Aircraft
Sensors
Sensors
Variable
Soft Stops
Stick Position
AFCS
AFCS
Inceptor
Inceptor
Constraints
Constraints
Prediction Algorithm:
e.g. Neural Networks,
On-board models
Carefree Maneuvering (CFM) Control
System Using Tactile Cueing
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Task Objectives:
 Develop limit detection and avoidance algorithms for peak responses and transient limits
 Develop an integrated CFM control architecture that features cueing and limiting in the AFCS
 Develop practical systems, demonstrate technologies in piloted simulation / flight test
 Apply CFM technology to UAV’s
Approaches:
 Develop improved transient limit prediction algorithm and constraint calculation methods
 Cooperation with industry / academic partners (Sikorsky / Georgia Tech), in order to
demonstrate systems in piloted simulations and flight tests
 Focus on comprehensive collective axis cueing and hub moment limiting systems
 Extend technologies to integrate with advanced AFCS designs / UAV flight controls
Expected Results:
 Develop limit avoidance methods for advanced AFCS designs / UAV flight controls
 Evaluate integration of envelope protection system with UAV flight controls
 An assessment of the requirements for effective carefree maneuvering control systems
 Some measure of the potential payoffs of carefree maneuvering technology
Outline of Technical Presentation
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 Piloted Rotorcraft / Cueing Systems
 Review - Longitudinal hub moment limit avoidance algorithm
 Pilot cueing techniques
 Results
 Comparison of different techniques
 UAVs / Future Rotorcraft
 Overview of control architecture – Inner loop / outer loop structure
 Problem with implementing envelope protection system within the
feedback loop
 Continuous torque limit protection for UAVs
 Generating constraints on outer loop command
 Results
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Limit Avoidance Algorithm - Transient Limits
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Stick Constraint 
Closest proximity of response to limit if no change in control input
Sensitivit y - Transient peak due to unit step
Offline trained neural networks
Find dynamic trim values
Stick constraint calculation
Input from Aircraft Sensors Signal
+
g i ( x f 0 )
ylim,l , y lim,u


ylim,u  max Q x , t 
~
t
 ulim,u 
max H x , t 
t



Q( x , t )

ylim,l  min Q x, t 
~
t
 ulim,l 
max H x , t 
t


Neural net
for f i ( x s , t )
-
ub
x f : 1c , q

t
H ( x, t )
Neural net
for H ( x s , t )
Increment in t
Longitudinal Hub Moment Limiting Algorithm
Output to Limit-Cue Arbitration and Tactile Cues
Scan through time
1
s  1
xf e
1
s  1
+
long
MH
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Background – Swoop Maneuver
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Integrated Hub Moment Limit
Exceedance Factor (IHMLF)

Desired Performance
Adequate Performance
Time integral of hub moment
above limit

Sum of all green pained areas
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Swoop Maneuver Results
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 Slight increase in maneuver time  Does not inordinately restrict agility of the
aircraft
 Reduction in integrated hub moment violations  less fatigue wear
 Reduction in absolute peak hub moment  reduced risk of catastrophic failure
Pilot comments: softstop cue for hub moment objectionable during very
aggressive maneuvers
With Cue
With Cue
Wihtout Cue
26
26
24
Maneuver Time (s)
24
Maneuver Time (s)
Wihtout Cue
22
20
18
16
14
12
10
0
5000
10000
15000
20000
25000
Integrated Hub Moment Limit Exceedance Factor
(ft-lb-s)
22
20
18
16
14
12
10
20000
25000
30000
35000
Absolute Peak Hub Moment (ft-lb)
40000
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Command Shaping for Limit Protection
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Pure automatic limit protection
Upper Limit
4
x 10
H
Hub Moment, M (ft-lb)
Lower Limit
 Does not allow pilot to override limit
 Useful for evaluating the effectiveness of the prediction
algorithm only
 Gives a standard for comparing other cueing techniques
2
+
-
0
-2
220
222
Time
(sec)
224
226
1
Control Axis, u
yp
lim
0.5
0
230
232
Time234
(sec)
4
x 10
2
0
-2
220
1
-0.5
-1
228
0.5
+
+
225
230
235
236
δ
crit
SS
238
eff
-
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Command Shaping for Limit Protection
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Deadband
Upper Limit
 Allows pilot to override the limit
 Can be implemented using stick shakers
 Provides easier upgrade than softstop
Lower Limit
(ft-lb)
4
yp
lim
-
296
0.5
0
298
4
x 10
Time300
(sec)
2
H
-2
(ft-lb)
0
1
Control Axis, u
+
2
Hub Moment, M
Hub Moment, M
H
x 10
302
304
306
308
+
+
0
-2
300
0.7
-0.5
0.6
-1
0.5
Time
(sec)
310
305
310
312
δ
crit
SS
314
eff
-
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Advanced Cueing Methods
(proposed by our colleagues at Georgia Tech)
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Frequency Distribution
 Sidestick momentum can inadvertently
climb over a softstop cue
 Can result in transient limit violation
 Technique: No change to softstop Fleeting command restraint
 Technique: Low frequency
component for pilot cueing High frequency component for
autonomous protection
4
x 10
2
+
268
270
1
yp
lim
-
4
x 10
H
-1
0 -1.5
-2
-2
-2.5
1
Control Axis, u
4
x 10
Hub Moment, M (ft-lb)
H
Hub Moment, M (ft-lb)
Dynamic Overshoot Compensation
2
Time
(sec)
272
274
269
269.5
0
276
278
270
280
270.5
271
Time
(sec)
282
δ
-2
0.5
0.5
0
0
-0.5
-0.5
-0.5
-1
-1
-0.6
268
-0.4
270
272
274
276
278
280
282
284
284
+
+
286
286
eff
crit
SS
-
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Performance Evaluation
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



Pure Autonomous
Deadband
Dynamic Overshoot Compensation
Frequency Content Distribution
 Same underlying longitudinal hub moment
limit avoidance algorithm (developed by PSU)
 Summary
(ft-lb-s) Thousands
 Limit Protection Approaches
 Softstop cueing provided better results
compared to stick shakers
 Overshoot/ Freq. Distribution
(ft-lb) Thousands
 Constraint calculation (PSU) + Pilot cueing
(GT) = Smallest maneuver time + Fewer limit
violations  Agility + Component Life
Max
Integrated Hub Moment
Limit Violation
Extent of
2nd & 3rd
Quartiles
Min
Pure Over- Freq. Dead- Pure Pure
Visual shoot Dist band Tactile Auto
GT
 Smaller maneuver time
 Fewer limit violations
 better pilot-in-the-loop performance
 Best results using frequency distribution
 “Steadier” softstop of Freq. Dist. was noticed
and preferred
20
18
16
14
12
10
8
6
4
2
0
38
36
34
32
30
28
26
24
22
GT
PSU
PSU
16.3 16.5 16.8 18.1 18.3 18.5
Average Maneuver Time (sec)
Absolute Peak Hub Moment
Conclusions from Piloted Simulation
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 Algorithm was reasonably effective in predicting the constraints
 Neural Network - Predicts critical value of the future response over some time
interval
 Sensitivity for constraint calculation
 Constraints moved too quickly for the pilot to react efficiently to a pure soft
stop cue
 Frequency distribution of constraints provided best results
 Low frequency component – softstop cue
 High frequency component – Autonomous protection
 Pilot was able to interact with the softstop much more efficiently
 Maneuvers consistently resulted in smaller maneuver time and fewer limit
violations
 Satisfactory pilot in the loop performance – no major pilot objections
Limit Avoidance for UAVs / Future Rotorcraft
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 Future rotorcraft / UAV are likely to feature model following / inversion controllers
 Structural limit protection desirable
 To save weight on future designs (particularly on large aircraft)
 To protect highly agile vehicles with high bandwidth flight controls
 Achieve limit protection with constraints in desired response (not in feedback loop)
UAV
Velocity / Angle
Commands
ACAH /
RCAH
Active
Control Stick
Pilot
Pilot Control
Trajectory
Tracking
Limit Avoidance
Desired
Response
Tracking
Controller
Constraints on
Desired Response
Limit Prediction
Aircraft
Sensors
Limit Parameter to
Command Dynamics
Problems with Constraints in Feedback Loop
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c
Saturation Inside
Feedback Path
Saturation Outside
Feedback Path
 cmd
 cmd
Command
Filter
 lim
+
-
PD Error +
Dynamics
+
q lim
Inversion
Controller
Aircraft
c
Command
Filter
+
-
PD Error +
Dynamics
Command
Filter States
Constraint on
input command
q lim
+
Inversion
Controller
Aircraft
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Inner Loop / Outer Loop Controller
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acc. command
 cmd
cmd
Command
Filter
rcmd
VD cmd
Inner Loop
Commands

Pos. / Vel.

command
Notch Filter
PID Error
Dynamics
Inversion
Controller
Inner Loop
Aircraft
Sensors
ANN
Controller implemented for GENHEL
Inner Loop
Commands
Outer Loop
Commands
Vcmd
 cmd
cmd
h cmd


PI Error
Dynamics
 cmd
cmd
rcmd
VD cmd
 Outer loop – trajectory commands
 Inner loop – velocity / angle commands
Maneuver
– for aggressive maneuvers
Switch
To Command  Maneuver switch for command
switching
Filter
 cmd
cmd
rcmd
VD cmd
Challenges:
From
Sensors
 Ensure continuity of constraints with
command switching
 Closed loop stability due to saturation
constraints in feedback path
Continuous Torque Limit
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Command
Filter
VDcmd
VD cmd lim
VD
Q
Q
Aircraft

Qlim
Torque
Limit



1
s  1
Q pred


Qm
Envelope Constraint
Calculation
Torque
Prediction
(Neural Net)
Measured
Torque
 Dynamic Trim algorithm
 Offline trained neural network for future response prediction
 Sensitivity to command input for constraint calculation
 Constraint on descent velocity corresponding to transmission torque limit
 Constraint on Inner loop command
Cont. Torque Limit Evaluation – Inner Loop
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 Envelope protection off
 Torque exceeds the limit
 Envelope protection on
 Steady state torque stays close to the limit
 Constraint on descent velocity (Inner loop)
command
 Saturation limit inside feedback loop when
using altitude (outer loop) commands
Moving Constraints out of Feedback Loop
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Move outside feedback loop
wlim
+
w
-
wlim
w
e
 Why:
ulim
u
C (s )
G (s )
y
C (s )
+
e
-
+
D
C ( sI  A) 1 B
+
G (s )
+
D
Controller dynamics :
x  Ax  Be ; u  Cx  De
e  w y
; D0
If controller output limit is ulim ,
then controller input limit is,
-
+
y
 Affects closed loop stability
 Pilot cueing of command input
1
 Example: P/PI Controllers
 P: {A=0 B=0 C=0 D=Kp}
elim  D 1 ulim  Cx 
+
ulim
Command input limit,
wlim  elim  y  D 1 ulim  Cx   y
PI: {A=0 B=1 C=Ki D=Kp}
 Heave Axis:
 Inner Loop Command – Descent Velocity
 Outer Loop Command – Altitude (Using Proportional controller)
Cont. Torque Limit Evaluation – Outer Loop
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Flight Simulation Future Upgrades
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Honeywell 2-Axis Active Sticks
Donated by Boeing - Philadelphia
Bell 206 Simulation Cockpit
Donated by Bell-Textron
Accomplishments
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2004 Accomplishments
 Continued collaboration with Georgia Tech. – Evaluated different pilot cueing
techniques
 Demonstrated in piloted simulation that accurate prediction algorithm and
appropriate pilot cueing techniques do result in smaller maneuver time and
reduced limit violations
 Integrated hub moment limiting constraints with adaptive model following controller
 Integrated continuous torque limiting algorithm with inner loop /outer loop type
controller – useful for autonomous UAVs
 Continued to produce publications
Planned 2005 Accomplishments
 Integrate Active Control Stick with simulator at PSU
 Implement CFM systems on real-time simulator at PSU
 Continue development of CFM integrated with adaptive model following controller
for UAV’s or manned aircraft
 Further assessment of CFM impact on reduction of loads and handling qualities
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Schedule and Milestones
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Tasks
• Hub moment limit detection
and avoidance system
• Develop comprehensive
collective axis cueing system
• Demonstrate in piloted
simulation at Sikorsky
• Simplify algorithms for
practical flight system
• Develop integrated CFM
cueing and control laws
• Piloted simulation / HQ
evaluation of integrated CFM
• Integrate with adaptive
control
• Quantitative assessment of
benefits of CFM
• Develop CFM for UAV’s
• Sahani MS Degree
• Sahani PhD Degree
2001
2002
2003
2004
2005
Completed
Short Term
Long Term
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Technology Transfer Activities:
• Continued collaboration with Georgia Tech RCOE in simulator work, have discussed
potential transfer to their UAV work
• Publications - AIAA Journal of CIC, AIAA Journal of GCD (accepted), AHS Journal
(under review), AIAA GNC Conference, AHS Forum
• Briefings to Boeing, Sikorsky, Lockheed
Leveraging or Attracting Other Resources or Programs:
• Collaboration with Prof. Asok Ray on ARO MURI. Task “Damage Mitigating Control for
Rotorcraft”. Incorporating CFM technology into that work.
• Active control sticks donated by Boeing
Recommendations at the 2004 Review:
• Presentation too long, needs better focus
• Talk directly to PIs from HACT project
• Review team questioned usefulness of structural
load limiting
• Resolve export control issue when dealing with DLR
Actions Taken:
• Addressed in presentation development
• Had discussions and exchange of
information with P. Einthoven at Boeing
• Addressed in following slide
• No longer planning collaboration with
DLR
Relevance of Structural Load Limiting Work
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 Last review the question was posed “Who Cares?”
 RAH-66
 Hub moment limiting was implemented in RAH-66 by limiting pitch
acceleration commands. Seemed to work adequately but …
 Constraints not as accurate our system
 Constraints were inside feedback loop for outer loop controls – should avoid
this if possible.
 NASA Heavy Lift Rotorcraft Systems
 Effort to reduce weight
 Automatic Load Limiting / Hub Moment Control identified as a desirable
technology for heavy lift rotorcraft
 Weight savings could be achieved using better loads estimates to remove
conservatism – or actively limit loads to remove conservatism
 HACT Program
 Discussion with P. Einthoven at Boeing Philadelphia. He identified the need
to more rigorously study closed-loop stability.
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Overview of Accomplishments 2001-2005
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Response to step input
Comprehensive Collective Axis Limit Avoidance
 Includes transmission torque limits, engine torque
limits, rotor RPM limits, OEI and autorotation limits
 Collaborated with Sikorsky – Evaluated in piloted
simulation
Longitudinal Hub Moment Limit Avoidance
 Developed new algorithm that provided accurate
constraints over entire flight envelope
 Evaluated in piloted simulations at Georgia Tech.
 Evaluated different pilot cueing technique
 Demonstrated in pilot sim. : CFM results in smaller
maneuver time and reduced limit violations
Integration of CFM with Model Following Controller (MFC)
 Integrated long. hub moment limit protection with MFC
 Integrated torque limiting with inner loop/ outer loop controller
architecture
Publications
 Conference: 6 Journal: 2 published + 1 accepted + 2 under
review
f (x,  ped ,  col   col )  f (x,  ped ,  col   col )
f
 lim
 col  col 0
2 col
Limited Parameter
Peak Response Critical
Quasi-Steady
Response Critical
1
 Q

  
col 

*
 col
Integrated
Response
Q
+
Qlim
-
Qcorrected

 col
Neural
Net
UH-60A
Time
Q predicted
Normal Conditions

Qlim  min Qtranslim Qenglim
+

OEI

Autorotation

Qlim  QOEI  K omg *    0 
-
Qactual
Qlim  K omg *    0 
Normal Pilot Cues:
+
Out the window, instruments,
acceleration, vibration, etc …
e
1
s  1
-
eqs
Pilot
Pilot
Force
Active Control
Stick
Aircraft
Aircraft
Sensors
Sensors
Stick Position
AFCS
AFCS
Prediction Algorithm:
e.g. Neural Networks,
On-board models
Stick Constraint 
Closest proximity of response to limit if no change in control input
Sensitivity - Transient peak due to unit step
Offline trained neural networks
Find dynamic trim values
Stick constraint calculation

Input from Aircraft Sensors Signal
ylim ,l , ylim,u



Q( x , t )


-
1
xf e
s  1
ub
x f : 1c , q


ylim,l  min Q x, t 
~
t
 ulim,l 
max H  x , t 
t
Neural net
for f i ( x s , t )

ylim,u  max Q x , t 
~
t
 ulim,u 
max H  x , t 
t
+
g i ( x f 0 )
t
Neural net
for H ( x s , t )
H ( x, t )
1
s  1
-
Increment in t
+
long
MH
Longitudinal Hub Moment Limiting Algorithm
Output to Limit-Cue Arbitration and Tactile Cues
Scan through time
With Cue
Wihtout Cue
26
(ft-lb-s) Thousands
24
Maneuver Time (s)
22
20
18
16
14
12
10
0
5000
10000
15000
20000
25000
Integrated Hub Moment Limit Exceedance Factor
(ft-lb-s)
20
18
16
14
12
10
8
6
4
2
0
Max
Integrated Hub Moment
Limit Violation
Extent of
2nd & 3rd
Quartiles
Min
Pure Over- Freq. Dead- Pure Pure
Visual shoot Dist band Tactile Auto
GT
No Limit
Protection
With Limit
Protection
Safety (PV or ILM)
(ft-lb) Thousands
Inceptor
Inceptor
Constraints
Constraints
Performance (T)
Variable
Soft Stops
38
36
34
32
30
28
26
24
22
GT
PSU
PSU
16.3 16.5 16.8 18.1 18.3 18.5
Average Maneuver Time (sec)
Absolute Peak Hub Moment
Future Path
PENNSTATE
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Additional Basic Research
• Potential for more research on basic stability issues of load limiting and
bio-mechanical issues of tactile cueing.
• Investigate use for vibratory loads limiting – need comprehensive analysis
code (RCAS)
• Incorporate CFM technologies within a damage mitigating control / life
extending control architecture
Transition to Applications / Applied Research
• Near-term potential for application to UAV systems
• Georgia Tech implementing their controller on Boeing Maverick UAV
(uses R-22 airframe) – potential application for mast bumping / torque
limiting
• Long term application on heavy lift rotorcraft – use CFM methods to save
weight.
• There needs to be further study of certification issues for active control
sticks and tactile cueing systems.