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BOEING is a trademark of Boeing Management Company.Copyright © 2012 Boeing. All rights reserved.
Robust Adaptive Control with Improved Transient Performance
Eugene Lavretsky
MITMay 03, 2012
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Presentation Overview
• Introduction
• Adaptive Control Development @ Boeing
• Transient Dynamics in Adaptive Control• Motivating Example
• Transient Analysis with All States Accessible
• Adaptive Output Feedback Design Extension
• Conclusions, Comments, and Future Research Directions
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Introduction
• Classical Model Reference Adaptive Control (MRAC)• Originally proposed in 1958 by Whitaker et al., at MIT• Main idea: Specify desired command-to-output performance of a servo-
tracking system using reference model– Defines ideal response of the system due to external commands– Later called “explicit model following” MRAC
• First proof of closed-loop stability using Lyapunov theory was given in 1965 – 66 by Butchart, Shackcloth, and Parks
Process
Reference Model
Controller
Adaptive Law
External Command
System Response
ControlCommand
Ref. ModelOutput
SystemResponse
Process
Reference Model
Controller
Adaptive Law
External Command
System Response
ControlCommand
Ref. ModelOutput
SystemResponse
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Robust and Adaptive Flight Control Technology Transitions: Advanced Aircraft and Weapon Systems
• Technology Maturation & Transitions– Extended to Munitions (00-02)– Boeing IRAD Improvements Focus on System ID, Implementation, and Actuator
Saturation Issues– Design Retrofits onto Existing Flight Control Laws– Flight Proven on X-36, MK-84, MK-82, MK-82L, MK-84 IDP 2000, Boeing Phantom
Ray, NASA AirStar– Transitioned to JDAM production programs
93 94 95 96 97 98 99 00 01 02 03
Intelligent Flight Control System (NASA/Boeing)
F-15 ACTIVE
04
MK-82 L-JDAM
Reconfigurable Control For Tailless
Fighters (AFRL-VA/Boeing)
X-36 MK-84 JDAM
Adaptive Control For Munitions
(AFRL-MN/GST//Boeing)MK-84
05
• Gen I, flown 1999, 2003• Gen II, 2002 – 2006
•flight test 4th Q 2005• Gen III, 2006
RobustAdaptiveControl Technology Transition Timeline
MK-82 JDAM
X-45C
X-45A
J-UCAS & Phantom Ray
06 07 12
Boeing IRAD/CRAD
Theoretically justified, numerically efficient, and flight proven technology
Theoretically justified, numerically efficient, and flight proven technology
MK-84 IDP 2000
X-36 RESTORE
MK-82 Laser Seeker
08
Phantom Ray
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Motivating Example
• Adaptive Servomechanism for Scalar dynamics• Global asymptotic closed-loop stability• Bounded tracking in the presence of constant unknown parameters
Process :Ref. Model :
ˆ ˆController :ˆ
Adaptive Law :ˆ
Benefits : lim lim 0
ref ref ref ref
x r
x x ref
r r ref
ref reft t
x a x bux a x b r t
u k x k r t
k x x x
k r x x
e t x t x t x x r
External Command
Lyapunov-based
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Motivating Example (continued)
• Tuning MRAC• Increase adaptation gains to get desired (fast) tracking
performance
• Design Tradeoff• Large adaptation gains lead to oscillations (undesirable transients)
• Cause and effect• Reference and transient (error) dynamics have the same time constant
• Need transient dynamics to be faster than reference model• Similar to state observer design
– separation between controller and observer poles reduces transients
,x r
Bounded Signal
Reference Dynamics :
Transient Dynamics :
ref ref ref ref
ref x r
x a x b r
e a e b k x k r
1e refa
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Motivating Example (continued)
• Reference Model in MRAC• Similar to Open-Loop Observer
• Add Observer-like Error feedback Term to Reference Model• Similar to Closed-loop Observer
• Properties• Error feedback regulates transients• Converges to “ideal” reference model• No changes to control input• Retains stability and tracking
• Main Benefit• Control of transients
Error Feedback Term
ref ref ref ref e refx a x b r k x x
ref ref ref refx a x b r
ref e x re a k e b k x k r
Error Feedback Gain
Process
Reference Model
Controller
Adaptive Law
External Command
System Response
ControlCommand
Ref. ModelOutput
SystemResponse
ek
Process
Reference Model
Controller
Adaptive Law
External Command
System Response
ControlCommand
Ref. ModelOutput
SystemResponse
ek
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Motivating Example (continued)
• Simulation Data• Tracking step-inputs
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-3
-2
-1
0
1
2
3
Time, sec
e =
x - x
ref
ke = 0
ke = 10
ke = 80
Process : 3
Ref. Model : 10 10
ˆ ˆController :ˆ 10
Adaptive Law :ˆ 10
ref ref e ref
x r
x ref
r ref
x x u
x x r k x x
u k x k r
k x x x
k r x x
Trac
king
Err
or
ke = 0 (MRAC)
ke = 10
ke = 80
Increasing Observer Feedback Gain Reduces Transient
Oscillations
Need: Formal Analysis of Transient Dynamics
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0 1 2 3 4 5 6 7 8 9 10-30
-20
-10
0
10
20
30
x
Commandke = 0
ke = 80
0 1 2 3 4 5 6 7 8 9 10-300
-200
-100
0
100
200
300
400
500
Time, sec
u
ke = 0
ke = 80
Motivating Example (continued)
• Simulation Data• Tracking performance and control input
ke = 80
Syst
em S
tate
Con
trol
Inpu
t
ke = 0 (MRAC)
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Transient Analysis for Scalar Dynamics
• Observer-like ref model Error Dynamics System State Transient Dynamics
0ref
ke a e t
max
ref e x r
t
e a k e b k x k r
0
System State Asymptotic TrackingReference StateTransient Dynamics
O o 1 Otk
refx t x t e
o(1) and uniformly bounded
0 max0
0
tke t e e
k
From Lyapunov analysis
0O o 1 Otk
e t e
Error Feedback Term
ref ref ref ref e refx a x b r k x x
Error Dynamics
Reference Model0
ek
k
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Transient Analysis for Scalar DynamicsAn Alternative Approach
• Error Feedback Gain in Observer-like Ref Model
• Transient Dynamics Singular Perturbation Model
• Fast (Boundary Layer) Dynamics = Transients
0e
kk
Positive constant
Small Parameter
0
o 1 ,as , fixed 0
ref x r
t
e a k e b k x k r
From Lyapunov Analysis
ref e x re a k e b k x k r
Slow Dynamics : 0 refe x x
o 1ref refx a x b r
t
0
d ek e
d
0
System State Asymptotic TrackingTransient Dynamics
+o 1 Otk
refx t x t e
Stretched Time Boundary Layer Dynamics
Assume to be uniformly continuous and bounded
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MIMO Generalization: State Feedback
• What• Adaptive state feedback
servomechanism design for MIMO dynamical systems in the presence of matched uncertainties
• Why• Improves and streamlines
adaptive design tuning
• How• Model Reference Adaptive Control• Observer-like reference model Reduced and Quantifiable Transients
K
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System Dynamics and Control
• Open-Loop Dynamics
• Control Objective• Design control input u such that the regulated output z tracks bounded time-varying
command zcmd with bounded errors, reduced transients, and while operating in the presence of matched uncertainties
Matched Uncertainty
Hurwitz
0 00
0
p
ref ref
z
m mz I m m p z I m m Tcmd
n mp p I p p p p p
x xA B B
m m p
C
Ie C eu x z
x B K A B K x B
z C x
Command
KnownRegressor
Unknown Parameters
Uncertain Control Effectiveness
Regulated Output
Plant State
Integrated Tracking
Error
Tref ref cmd
z
x A x B u x B z
z C x
Hurwitz
Matched Uncertainties
Regulated Output
Command
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• System Dynamics
• Reference Model• Plant w/o uncertainties
• Tracking Error
• Adaptive Control Input
• Tracking Error Dynamics
• Algebraic Lyapunov Equation Adaptive Laws Closed-Loop Stability
• Adaptive Control Tuning Cycle
MRAC @ a Glance: How is It Currently Done ?
Tref ref cmdx A x B u x B z
ref ref ref ref cmdx A x B z
refe x x
ˆ Tu x
Trefe A e B x
0Tref refP A A P Q ˆ Tx e P B lim 0
te t
Rates of adaptation Q P B
e
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Few Thoughts …
• Open-Loop Dynamics
• Reference Model ~ Luenberger Open-Loop Observer
• Tracking Error Dynamics = Transient Error Dynamics
• Need to be “faster” than system dynamics minimizes unwanted transients
• Main Idea: Use Closed-Loop Luenberger Observer as Reference Model
Tref ref cmdx A x B u x B z
ref ref ref ref cmdx A x B z
Trefe A e B x
Innovation Term
ref ref ref ref cmd refx A x B z L x x
Observer Gain
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• Open-Loop Plant
• Observer-like Reference Model
• Tracking Error
• Adaptive Control Input
• Error Dynamics
• Observer Riccati Equation Adaptive Laws Closed-Loop Stability• With prescribed degree of stability
MRAC with Observer–like Reference Model
Tref ref cmdx A x B u x B z
ref ref ref ref cmd v refx A x B z L x x
refe x x
ˆ Tu x
Hurwitzv
Tref v
A
e A L e B x
1 0T
v ref n n ref n n v v v v vP A I A I P P R P Q
1ˆ Tvx e P B
lim 0t
e t
e
1, traceT TvV e e P e
Lyapunov function
1Tv v v v v v v vP A A P P R P Q
1 1 1 1 1 0Tv v v v v v v vP A A P R P Q P
Global Asymptotic TrackingAdaptive Laws
Observer-like Gain
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Observer-Like MRAC State Feedback Design Summary
• System Dynamics• Regulated Output
• No Uncertainties LQR PI Ref Dynamics• Baseline Closed-Loop System
• Solve Observer ARE, Compute Observer Gain, and Form Ref model
• Adaptive Control• State-feedback
1ˆ
ˆ
Tv
T
x e P B
u x
, dim dim
Tp ref cmd
z
x A x B u x B z
z C x z u
ref ref ref ref cmd v refx A x B z L x x
1
ref
Tlqr
A
K
Tref ref ref ref ref cmdx A B R B P x B z
1 0T
v ref n n ref n n v v v v vP A I A I P P R P Q
refx x
System State Asymptotic Reference Model Tracking Bounded Command Tracking
1v v vL P R
~ref cmdz z z
Ratesof adaptation
1,v v v v vQ R L P R Design Cycle
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Transient Analysis for MIMO Dynamics
• Closed-Loop Transient (Error) Dynamics
• Singular Perturbed System
• Stretched Time Boundary Layer Dynamics = Transients
1
= Uniformly Bounded Function of TimeObserver Gain:
Hurwitz Matrix
v
Tref v v
tL
e A P R e B t x t
1v n nvR I
v
11ref ve A P e tv
0 O , as 0vP P v v
01 Orefv e v A v P v e v t
Positive Definite Symmetric
00 0 0
Asymptotic TrackingTransient Dynamics
, O o 1 expref reft t
x t v x t v P x t x tv
0t d e P ev d
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Closed-loop Reference Model (CRM) in Adaptive ControlTravis E. Gibson (PhD Student, MIT)
• Uncertain Plant
• Reference Model
• Control Input
• Tracking Error• Lyapunov Equation with prescribed degree of stability
• Adaptive Law
• Tuning Knobs: Observer Gain and Adaptation Rate
• Main Result
• Asymptotic Bounds on Control Rate Transients
Trefx A x B u x
ref ref ref refx A x B r l x x
T
ref n n ref n n n nA l I P P A l I I
refe x x
ˆ ˆProj , Tx e P B
ˆ Tu r x
l
0 4 4( ) O , ( ) = O
e et t
lu t u tl
Observer-like Gain
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Bounds on Control Rate Transients with CRM*Travis E. Gibson (PhD Student, MIT)
• Ref Model dominating eigenvalue
• Error time constant:
• Time constant associated with Aref :
• Inequality enforced by design
Copyright © 2009 Boeing. All rights reserved.
min Real i refiA
10 e ref
1ref
1e l
0 4 4( ) = O , ( ) = O
e et t
lu t u tl
* T.E. Gibson, E. Lavretsky and A.M. Annaswamy, Closed-Loop Reference Models in Adaptive Control: Stability, Robustness, and Transient Performance, CDC 12 submitted
• Main Result: Bounds on control rate
20
10, 100010, 10
ll
u
t
4 e
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Can We Extend Observer-like State Feedback MRAC Design To Adaptive Output Feedback ?
* E. Lavretsky, “Adaptive Output Feedback Design Using Asymptotic Properties of LQG / LTR Controllers,”
IEEE Transactions on Automatic. Control, Jun, 2012
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What, Why, and How
• Problem• Output feedback design for MIMO
systems in the presence of “unknown unknowns”
• Aerospace Applications• Very Flexible Aerial (VFA)
platforms.– System dynamics exhibit no
frequency separation between primary and flex modes
– Flex modes are not available online, have low damping ratios, and must be actively controlled / stabilized
• Control Design Architecture• Robust LQG/LTR + Adaptive
output feedback augmentation • Based on asymptotic properties of
LQG/LTR regulators
POLECAT
HELIOS
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Problem Formulation
• Plant Dynamics• Restrictions: Observable, Controllable, Minimum-Phase
• Control Problem• Using output measurements y, design control input u such that the regulated output
z tracks its bounded time-varying command zcmd with bounded errors, while operating in the presence of “unknown unknowns”
0 00 0
, 0
p
p p
ref
z
d x
m m p m mz I z I m m Td d p cmd
n m p n mp p p
x x BA B
m m p
C
C Ie eu x zAx x B
y C x z C x
MatchedUncertainty Command
KnownRegressor
Unknown Parameters
Uncertain Control Effectiveness
Measured Output
Controlled Output
Plant State
Integrated Tracking
Error
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Reference Model Construction
• System Dynamics w/o Uncertainties
• Controller Algebraic Riccati Equation
• Reference / Baseline LQR PI Controller Reference Model
1
ref
Tref ref ref ref ref cmd
A
x A B R B P x B z
1 0T Tref ref ref ref ref refP A A P P B R B P Q
Hurwitz
ref ref ref ref cmdx A x B z
Satisfies Model Matching Conditions by Design
ref ref ref ref cmdx A x Bu B z
1
Tlqr
T Tref ref ref ref lqr ref
K
u R B P x K x
LQR Gain
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Open-Loop Dynamics Reformulation
• Using Reference Model Data
,
Tref ref cmd
z
x A x B u x B z
y C x z C x
T Tx d
d pT
x
T Tref x d d p ref cmd
xK
x
x A x B u K x x B z
Hurwitz
Measured Regulated
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• Sufficient Condition for Closed-Loop Stability
• State Feedback Adaptive Law
• Output Measurements
• Output error
• (State Output) Adaptive Feedback
Design Idea
1ˆ Tvx e P B
0T
v ref n n ref n n vP A I A I P
y C x
ˆ ˆye y y C x x C e
1 TvP B C W
ˆ T T Tyx e C W x e W
~SPR
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Open-Loop DynamicsAssumptions and Squaring-up Method
• Controllable & Observable
• Number of measured outputs p is no less than number of control inputs m
• Achieving Nonzero High Frequency Gain and Minimum-phase Dynamics
,
Tref ref cmd
z
x A x B u x B z
y C x z C x
Measured Regulated output embedded into system dynamics
dim dim dimy p m u z
1
2
det det
det 0 det 0, Re 00
n n
p pB
s I A C s I A B
s I A Bp m C B B s
C
Squaring-Up ProblemFind B2 such that
Rosenbrock System Matrix is Nonsingular in the RHP
No Transmission Zeros in the RHP
Allows to control non-minimum phase dynamics with relative degree greater than 1
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Adaptive Output Feedback
• Open-Loop Dynamics
• Luenberger-type State Observer
• Control Input Closed-Loop Observer Dynamics
• Closed-Loop Plant Dynamics
,
Tref ref cmd
z
x A x B u x B z
y C x z C x
ˆ ˆˆ ˆ ˆ ˆ
ˆ ˆ
Tref v ref cmdx A x B u x L y y B z
y C x
ˆ ˆTu x ˆ ˆ ˆref ref cmd vx A x B z L y y
Observer Gain
ˆ ˆT Tref ref cmdx A x B z B x x
Estimated Parameters
ˆxe x x
Observer Error
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Adaptive Output Feedback (continued)
• Closed-Loop Observer Dynamics
• Closed-Loop Plant Dynamics
• Observer Error
• Observer Error Dynamics
• Design Task – Reduce Observer Error• Choose Observer Gain• Adapt Parameters
ˆ ˆ ˆref ref cmd vx A x B z L y y
ˆ ˆT Tref ref cmdx A x B z B x x
ˆxe x x
ˆ ˆT Tx ref v xe A L C e B x x
Estimated ParametersObserver Gain
1xe
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Adaptive Output Feedback, (Observer Design)
• “Squaring-Up” (if p > m)
• Choose parameter–dependent (v) weights
• Solve Filter Algebraic Riccati Equation
• Calculate Observer Gain, (parameter-dependent)1T
v v vL P C R
1 0T T
v ref n n ref n n v v v v vP A I A I P P C R C P Q
0 01 ,
1T
v vv vQ Q B B R R
v v
“Small” Positive Parameter
Positive constant Enforces prescribed degree of stability
1det 0 det det 0 , Re 0p m C B s I A C s I A B s
2B B B
No Transmission Zeros in the RHP
Nonzero High Frequency Gain Free to Choose
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Adaptive Output Feedback, (Observer Design)
• Parameter–Dependent Algebraic Riccati Equation, with prescribed degree of stability
• Theorem• Inverse solution exists
– Symmetric, positive-definite
• Asymptotic relations take place, as
10 0
1 0T T T
v ref n n ref n n v v vvP A I A I P Q P C R C P B B
v
1 10 O , as 0vP P v v
12
0 OTvP B C R W v
1v vP P
Computable
Dominating term, for small v
0 OT TvP C B W R v
0v
12
2 0 OTvP B B C R W v
12
0
Computable
O0m mT
vp m m
IP B C R W v
Enable MRAC design with output feedback Tuning “knob”
Defines output measurements
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Adaptive Output Feedback, (Completed)
• Parameter–Dependent Algebraic Riccati Equation
• Asymptotic Relation for Stability Proofs
• Theorem Stability & Bounded Tracking• Parameter Adaptation with Projection Operator
• Adaptive Output Feedback Control
1 0T T
v ref n n ref n n v v v v vP A I A I P P C R C P Q
12
0 OTvP B C R W S v
1 2 0T Tv v v v v v v v vP A A P P C R C P Q P
1
v
Tv ref v v
L
A A P C R C
HurwitzObserver Gain
Computable
Inverse ARE Solution
12
0ˆ ˆ ˆ ˆProj , Tx y y R W S
ˆ ˆTu x ˆ ˆ ˆref ref cmd vx A x B z L y y
SmallReference Model
Lyapunov-based Stability Proof
0 01 ,
1T
v vv vQ Q B B R R
v v
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Adaptive Output Feedback Design Summary
• System Dynamics• Measured and Regulated Output
• Set Uncertainties to Zero, Design LQR PI Controller, and Create Reference Dynamics
• Baseline Closed-Loop System
• Compute B2 such that:
• Choose Small Parameter• Solve Filter ARE, Compute Kalman Gain and Form State Observer
• Output Feedback Adaptive Laws
• Output Feedback Control
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0ˆ ˆ ˆ ˆProj , Tx y y R W S
ˆ ˆTu x
, , dim dim
Td d p ref cmd
z
x A x B u x B z
y C x z C x y z
ˆ ˆ ˆref ref cmd vx A x B z L y y
0v
1
Tlqr
ref
Tref ref ref ref ref cmd
K
A
x A B R B P x B z
2det 0
det 0, Re 00
B
n n
p p
C B B
s I A Bs
C
10 0
1 1 0T T T
v ref n n ref n n v v vv vP A I A I P P C R C P Q B B
v v
LQG
/ LT
R D
esig
n Ite
ratio
ns
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Key Design Features
• Adaptive laws and Control Input Do Not explicitly depend of the tuning parameter
• System Dynamics Reformulated Imbeds Desired Reference Model
• LQG / LTR observer tuning leads to improved reference model tracking
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0ˆ ˆ ˆ ˆProj , Tx y y R W S
ˆ ˆTu x
Td d p ref cmdx A x B u x B z
ˆ ˆ ˆref ref cmd vx A x B z L y y
0v
ˆˆ ref
ref
x xx x
x x
Squared-up LTI Dynamics LQG/LTR Observer Output Feedback Adaptive Controller Reference Model Tracking
Tref ref cmdx A x B z B u x
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Conclusions• Constructive Methods to Design Adaptive State and Output Feedback Controllers for MIMO
Systems with Matched Uncertainties and Quantifiable Transients• Based on asymptotic properties of LQG / LTR regulators• Observer-like reference model modification
• Ongoing Work• Robust and adaptive control for Very Flexible Aerial Platforms
• Future Work• Output Feedback Adaptive Control with Nonparametric Uncertainties
– State Limiter (keeps system state within bounded approximation set)• Combined / Composite Output Feedback Adaptive Design
– Using tracking and prediction errors in adaptive laws
K
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• Open-Loop Plant
• Observer-like Reference Model
• Tracking Error
• Adaptive Control Input• …• …• STOP RIGHT HERE !!!
• This is a Cancelation-Based Design May have 0 margins Recovering “ideal” control may lead to loss of robustness – A Controversy ?!
• Need Optimal / Robust Control Solutions• Are NOT cancellation-based• Have nonzero gain and time-delay margins
• Question: Can MRAC solutions be formulated using Optimal Control ?
A Technical Challenge
Tref ref cmdx A x B u x B z
ref ref ref ref cmd refx A x B z L x x
refe x x
ˆ Tu x
e
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Boeing in Seattle
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Phantom Ray
First Flight, 04-27-2011