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Linear-Quadratic-Gaussian Controllers Robert Stengel
Optimal Control and Estimation MAE 546 Princeton University, 2018
Copyright 2018 by Robert Stengel. All rights reserved. For educational use only.http://www.princeton.edu/~stengel/MAE546.html
http://www.princeton.edu/~stengel/OptConEst.html
! LTI dynamic system! Certainty Equivalence and the Separation
Theorem! Asymptotic stability of the constant-gain
LQG regulator! Coupling due to parameter uncertainty! Robustness (loop transfer) recovery! Stochastic Robustness Analysis and Design! Neighboring-optimal Stochastic Control
1
The Problem: Minimize Cost, Subject to Dynamic Constraint, Uncertain
Disturbances, and Measurement Error
!x t( ) = Fx(t)+Gu(t)+Lw(t), x 0( ) = xoz t( ) = Hx t( ) + n t( )
= 12minuE E xT (t f )S(t f )x(t f ) | ID⎡⎣ ⎤⎦ + E xT (t) uT (t)⎡
⎣⎤⎦Q 00 R
⎡
⎣⎢
⎤
⎦⎥x(t)u(t)
⎡
⎣⎢⎢
⎤
⎦⎥⎥dt
0
t f
∫ ID
⎧⎨⎪
⎩⎪
⎫⎬⎪
⎭⎪
⎧⎨⎪
⎩⎪
⎫⎬⎪
⎭⎪
Dynamic System
Cost Function
2
ID t( ) = x t( ),P t( ),u t( ){ }
minuV to( ) = min
uJ t f( )
Initial Conditions and DimensionsE x 0( )⎡⎣ ⎤⎦ = xo; E x 0( )− x 0( )⎡⎣ ⎤⎦ x 0( )− x 0( )⎡⎣ ⎤⎦
T{ } = P 0( )E w t( )⎡⎣ ⎤⎦ = 0; E w t( )wT τ( )⎡⎣ ⎤⎦ =Wδ t −τ( )E n t( )⎡⎣ ⎤⎦ = 0; E n t( )nT τ( )⎡⎣ ⎤⎦ = Nδ t −τ( )
E w t( )nT τ( )⎡⎣ ⎤⎦ = 0
dim x t( )⎡⎣ ⎤⎦ = n ×1
dim u t( )⎡⎣ ⎤⎦ = m ×1
dim w t( )⎡⎣ ⎤⎦ = s ×1
dim z t( )⎡⎣ ⎤⎦ = dim n t( )⎡⎣ ⎤⎦ = r ×1
Statistics
Dimensions
3
Linear-Quadratic-Gaussian Control
4
Separation Property and Certainty Equivalence
• Separation Property– Optimal Control Law and Optimal Estimation Law can be
derived separately– Their derivations are strictly independent
• Certainty Equivalence Property– Separation property plus, ...– The Stochastic Optimal Control Law and the Deterministic
Optimal Control Law are the same– The Optimal Estimation Law can be derived separately
• Linear-quadratic-Gaussian (LQG) control is certainty-equivalent
5
The Equations (Continuous-Time Model)
!x t( ) = F(t)x(t)+G(t)u(t)+L(t)w(t)z t( ) = Hx t( ) + n t( )
u t( ) = −C(t)x(t)+CF (t)yC (t)
!x t( ) = F(t)x(t)+G(t)u(t)+K t( ) z t( )−Hx t( )⎡⎣ ⎤⎦= F(t)−G(t)C t( )−K t( )H⎡⎣ ⎤⎦ x(t)+G t( )CF (t)yC (t)+K t( )z t( )
K t( ) = P(t)HTN−1 t( )!P(t) = F(t)P(t)+ P(t)FT (t)+L t( )W t( )LT t( )− P(t)HTN−1 t( )HP(t)
C t( ) = R−1 t( )GT t( )S t( )!S t( ) = −Q(t)− F(t)T S t( )− S t( )F(t)+ S t( )G(t)R−1(t)GT (t)S t( )
System State and Measurement
Control Law
State Estimate
Estimator Gain and State Covariance Estimate
Control Gain and Adjoint Covariance Estimate
6
Estimator in the Feedback Loop
u t( ) = −C(t)x(t)
x t( ) = Fx(t) +Gu(t) +K t( ) z t( ) −Hx t( )⎡⎣ ⎤⎦
K t( ) = P(t)HTN−1
P(t) = FP(t)+ P(t)FT +LWLT − P(t)HTN−1HP(t)
Linear-Gaussian (LG) state estimator adds dynamics to the feedback signal
Thus, state estimator can be viewed as a control-law “compensator”Bandwidth of the compensation is dependent on the multivariable
signal/noise ratio, [PHT]N–1
7
Stable Scalar LTI Example of Estimator Compensation
!x = − x + K z − Hx( ) = −1− KH( ) x + Kz
Estimator transfer function
Low-pass filter
x s( ) = Ks − −1− KH( )⎡⎣ ⎤⎦
z s( )
s − −1− KH( )⎡⎣ ⎤⎦ x s( ) = Kz s( )Laplace transform of estimator
Estimator differential equation
!x = −x +w; z =Hx +nDynamic system (stable) and measurement
x s( )z s( ) =
Ks − −1− KH( )⎡⎣ ⎤⎦
8
Unstable Scalar LTI Example of Estimator Compensation
x = x + K z − Hx( ) = 1− KH( ) x + Kz
Estimator transfer function
Low-pass filter
x s( ) = Ks − 1− KH( )⎡⎣ ⎤⎦
z s( )
s − 1− KH( )⎡⎣ ⎤⎦ x s( ) = Kz s( )Laplace transform of estimator
Estimator differential equation
!x =+x +w; z =Hx +nDynamic system (unstable) and measurement
x s( )z s( ) =
Ks − 1− KH( )⎡⎣ ⎤⎦
9
Steady-State Scalar Filter Gain
0 = 2P +W −P2H 2
N; P2 −
2NH 2 P −
WNH 2 = 0
Constant, scalar filter gain
P = Signal "Power"= State EstimateVarianceN = Noise "Power"= Measurement Error VarianceH = Projection from Measurement Space to Signal SpaceW = Disturbance Covariance
K =PHN
Algebraic Riccati
equation(unstable case) P =
NH 2 ±
NH 2
⎛⎝⎜
⎞⎠⎟2
+WNH 2 =
NH 2 1± 1+WH
2
N
⎡
⎣⎢⎢
⎤
⎦⎥⎥
10
Steady-State Filter Gain
K =
NH 2 1+ 1+WH
2
N
⎡
⎣⎢⎢
⎤
⎦⎥⎥
⎧⎨⎪
⎩⎪
⎫⎬⎪
⎭⎪H
N=
1H1+ 1+WH
2
N
⎡
⎣⎢⎢
⎤
⎦⎥⎥
⎧⎨⎪
⎩⎪
⎫⎬⎪
⎭⎪
K W >>N⎯ →⎯⎯WN
11
Dynamic Constraint on the Certainty-Equivalent Cost
minuJ = min
uJCE + JS
P(t) is independent of u(t); therefore
JCE is Identical in form to the deterministic cost function
Minimized subject to dynamic constraint based on the state estimate
x t( ) = Fx(t) +Gu(t) +K t( ) z t( ) −Hx t( )⎡⎣ ⎤⎦
12
Kalman-Bucy Filter Provides Estimate of the State Mean Value
Filter residual is a Gaussian process
!x t( ) = Fx(t)+Gu(t)+K t( ) z t( )−Hx t( )⎡⎣ ⎤⎦" Fx(t)+Gu(t)+K t( )ε t( )
Filter equation is analogous to deterministic dynamic constraint on
deterministic cost function
x t( ) = Fx(t) +Gu(t) + Lw(t)13
Control That Minimizes the Certainty-Equivalent Cost
Optimizing control history is generated by a time-varying feedback control law
u t( ) = −C(t)x(t)The control gain is the same as
the deterministic gain
C t( ) = R−1GTS t( )!S t( ) = −Q− FTS t( )− S t( )F + S t( )GR−1GTS t( )
S t f( ) given
14
Optimal Cost for the Continuous-Time LQG Controller
Certainty-equivalent cost
JCE =12Tr S(0)E x 0( ) xT 0( )⎡⎣ ⎤⎦ + S t( )K t( )NKT t( )dt
0
t f
∫⎡
⎣⎢⎢
⎤
⎦⎥⎥
J = JCE + JS
= 12Tr S(0)E x 0( ) xT 0( )⎡⎣ ⎤⎦ + S t( )K t( )NKT t( )dt
0
t f
∫⎧⎨⎪
⎩⎪
⎫⎬⎪
⎭⎪
+ 12Tr S(t f )P(t f )+ QP t( )dt
0
t f
∫⎡
⎣⎢⎢
⎤
⎦⎥⎥
Total cost
15
Discrete-Time LQG ControllerKalman filter produces state estimate
xk −( ) = Φxk−1 +( ) + ΓCk−1xk−1 +( )
xk+1 = Φxk − ΓCkxk +( )
xk +( ) = xk −( ) +Kk zk −Hxk −( )⎡⎣ ⎤⎦Closed-loop system uses state estimate for feedback control
16
Response of Discrete-Time 1st-Order System to Disturbance
Kalman Filter Estimate from Noisy Measurement
Propagation of Uncertainty Kalman Filter, Uncontrolled System
17
Comparison of 1st-Order Discrete-Time LQ and LQG Control Response
Linear-Quadratic Control with Noise-free Measurement
Linear-Quadratic-Gaussian Control with Noisy
Measurement
18
Asymptotic Stability of the LQG Regulator
19
System Equations with LQG Control
ε t( ) = x t( ) − x t( )
x t( ) = Fx(t)+Gu(t)+Lw(t)x t( ) = Fx(t)+Gu(t)+K t( ) z t( )−Hx t( )⎡⎣ ⎤⎦
ε t( ) = F −KH( )ε t( ) + Lw t( ) −Kn t( )
With perfect knowledge of the system
State estimate error
State estimate error dynamics
20
Control-Loop and Estimator Eigenvalues are Uncoupled
!x t( )!ε t( )
⎡
⎣⎢⎢
⎤
⎦⎥⎥=
F −GC( ) GC0 F −KH( )
⎡
⎣⎢⎢
⎤
⎦⎥⎥
x t( )ε t( )
⎡
⎣⎢⎢
⎤
⎦⎥⎥+ L 0
L −K⎡
⎣⎢
⎤
⎦⎥w t( )n t( )
⎡
⎣⎢⎢
⎤
⎦⎥⎥
Upper-block-triangular stability matrixLQG system is stable because
(F – GC) is stable(F – KH) is stable
Estimate error affects state response
Actual state does not affect error responseDisturbance affects both equally
x t( ) = F −GC( )x t( ) +GCε t( ) + Lw t( )
21
Parameter Uncertainty Introduces Coupling
22
Coupling Due To Parameter Uncertainty
!x t( )!ε t( )
⎡
⎣⎢⎢
⎤
⎦⎥⎥=
FA −GAC( ) GAC
FA − F( )− GA −G( )C−K HA −H( )⎡⎣ ⎤⎦ F + GA −G( )C−KH⎡⎣ ⎤⎦
⎡
⎣
⎢⎢
⎤
⎦
⎥⎥x t( )ε t( )
⎡
⎣⎢⎢
⎤
⎦⎥⎥+"
Actual System: FA ,GA ,HA{ }Assumed System: F,G,H{ }
x t( ) = FAx(t)+GAu(t)+Lw(t)x t( ) = Fx(t)+Gu(t)+K t( ) z t( )−Hx t( )⎡⎣ ⎤⎦
Closed-loop control and estimator responses are coupled
z t( ) = HAx(t)+ n t( )u(t) = −C t( ) x t( )
23
Effects of Parameter Uncertainty on Closed-Loop Stability
sI2n − FCL =
sIn − FA −GAC( )⎡⎣ ⎤⎦ −GAC
− FA − F( )− GA −G( )C−K HA −H( )⎡⎣ ⎤⎦ sIn − F + GA −G( )C−KH⎡⎣ ⎤⎦{ }= ΔCL s( ) = 0
! Uncertain parameters affect closed-loop eigenvalues! Coupling can lead to instability for numerous reasons
! Improper control gain! Control effect on estimator block! Redistribution of damping
24
Doyle’s Counter-Example of LQG Robustness (1978)
x1x2
⎡
⎣⎢⎢
⎤
⎦⎥⎥= 1 1
0 1⎡
⎣⎢
⎤
⎦⎥
x1x2
⎡
⎣⎢⎢
⎤
⎦⎥⎥+ 0
1⎡
⎣⎢
⎤
⎦⎥u +
11
⎡
⎣⎢
⎤
⎦⎥w
z = 1 0⎡⎣ ⎤⎦x1x2
⎡
⎣⎢⎢
⎤
⎦⎥⎥+ n
Q = Q 1 11 1
⎡
⎣⎢
⎤
⎦⎥; R = 1; W =W 1 1
1 1⎡
⎣⎢
⎤
⎦⎥; N = 1
C = 2 + 4 +Q( ) 1 1⎡⎣ ⎤⎦ = c c⎡⎣ ⎤⎦
K = 2 + 4 +W( ) 11
⎡
⎣⎢
⎤
⎦⎥ =
kk
⎡
⎣⎢
⎤
⎦⎥
Unstable Plant
Design Matrices
Control and Estimator Gains
25
Uncertainty in the Control Effect
s −1( ) −1 0 0
0 s −1( ) µc µc
−k 0 s −1+ k( ) −1
−k 0 c + k( ) s −1+ c( )
= 0
FA = F; GA =0µ
⎡
⎣⎢⎢
⎤
⎦⎥⎥; HA = H
System Matrices
Characteristic Equation
s4 + a3s3 + a2s
2 + a1s + a0 = ΔCL s( ) = 026
Stability Effect of Parameter Variation
a1 = k + c − 4 + 2 µ −1( )cka0 = 1+ 1− µ( )ck
Routh's Stability Criterion (necessary condition)• All coefficients of Δ(s) must be positive for stability
• Arbitrarily small uncertainty, µ = 1 + ε , could cause unstability• Not surprising: uncertainty is in the control effect
• µ is nominally equal to 1• µ can force a0 and a1 to change sign• Result is dependent on magnitude of ck
27
The Counter-Example Raises a FlagSolution
Choose Q and W to be small, increasing allowable range of μ
! However, .... The counter-example is irrelevant because it does not satisfy the requirements for LQ and LG stability! The open-loop system is unstable, so it requires feedback
control to restore stability! To guarantee stability, Q and W must be positive definite, but
Q =Q 1 11 1
⎡
⎣⎢
⎤
⎦⎥; hence, Q = 0
W =W 1 11 1
⎡
⎣⎢
⎤
⎦⎥; hence, W = 0
28
Restoring Robustness (Loop Transfer Recovery)
29
Loop-Transfer Recovery (Doyle and Stein, 1979)
! Proposition: LQG and LQ robustness would be the same if the control vector had the same effect on the state and its estimate
Cx t( ) and Cx t( )produce same expected value of control, E u t( )⎡⎣ ⎤⎦
! Therefore, restoring the correct mean value from z(t) restores closed-loop robustness
! Solution: Increase the assumed “process noise” for estimator design as follows (see text for details)
W =Wo + k2GGT
30
but not the same
E uLQ t( )− uLQG t( )⎡⎣ ⎤⎦ uLQ t( )− uLQG t( )⎡⎣ ⎤⎦T{ }
as x t( ) contains measurement errors but x t( ) does not
! Analogous solution in Kwakernaak, H., and Sivan, R., Linear Optimal Control Systems, 1972
Stochastic Robustness Analysis and Design
31
Expression of Uncertainty in the System Model
• Variation may be– Deterministic, e.g.,
• Upper/lower bounds (“worst-case”)– Probabilistic, e.g.,
• Gaussian distribution• Bounded variation is equivalent to probabilistic
variation with uniform distribution
System uncertainty may be expressed as• Elements of F
• Coefficients of Δ(s)• Eigenvalues, λ
• Frequency response/singular values/time response, A(jω ), σ (jω ), x(t)
32
Stochastic Root Locus:Uncertain Damping Ratio and
Natural Frequency
Uniform Distribution of
Eigenvalues
Gaussian Distribution of
Eigenvalues
33
Probability of Instability
• Nonlinear mapping from probability density functions (pdf) of uncertain parameters to pdf of roots
• Finite probability of instability with Gaussian (unbounded) distribution
• Zero probability of instability for some uniform distributions
34
Probabilistic Control Design• Design constant-parameter controller (CPC) for satisfactory
stability and performance in an uncertain environment• Monte Carlo Evaluation of simulated system response with
– competing CPC designs [Design parameters = d]– given statistical model of uncertainty in the plant [Uncertain plant
parameters = v]
• Search for best CPC– Exhaustive search– Random search– Multivariate line search– Genetic algorithm– Simulated annealing
35
Design Outcome Follows Binomial Distribution
• Binomial distribution: Satisfactory/Unsatisfactory• Confidence intervals of probability estimate are functions of
– Actual probability– Number of trials
1e+01
1e+03
1e+05
1e+07
1e+09
1e-06 1e-05 1e-04 0.001 0.01 0.1 1
2%5%10%20%
100%
Num
ber
of E
valu
atio
ns
0.5
IntervalWidth
Probability or (1 - Probability)Binomial Distribution
Maximum Information Entropy when Pr = 0.5
Number of Evaluations
Confidence Interval
36
Example: Probability of Stable Control of an Unstable Plant
F =
−2gf11 /V ρV 2 f12 / 2 ρVf13 −g
−45 /V 2 ρVf22 / 2 1 0
0 ρV 2 f32 / 2 ρVf33 00 0 1 0
⎡
⎣
⎢⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥⎥
Longitudinal dynamics for a Forward-Swept-Wing AirplaneX-29 Aircraft
37
G =
g11 g12
0 0g31 g32
0 0
⎡
⎣
⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥
; x =
V , Airspeedα , Angle of attack
q, Pitch rateθ , Pitch angle
⎡
⎣
⎢⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥⎥
Ray, Stengel, 1991
Example: Probability of Stable Control of an Unstable Plant
p = ρ V f11 f12 f13 f22 f32 f33 g11 g12 g31 g32⎡⎣
⎤⎦T
λ1−4 = −0.1± 0.057 j, − 5.15, 3.35
Nominal eigenvalues (one unstable)
Environment Uncontrolled Dynamics Control Effect
Air density and airspeed, ρ and V , have uniform distributions(±30%)
10 coefficients have Gaussian distributions (σ = 30%)
38
LQ Regulators for the ExampleCase a) LQR with low control weighting
Case b) LQR with high control weighting
Q = diag 1,1,1,0( ); R = 1,1( ); λ1−4nominal = –35,–5.1,–3.3,–.02
C = 0.17 130 33 0.360.98 −11 −3 −1.1
⎡
⎣⎢
⎤
⎦⎥
Q = diag 1,1,1,0( ); R = 1000,1000( ); λ1−4nominal = −5.2,−3.4,−1.1,−.02
C = 0.03 83 21 −0.060.01 −63 −16 −1.9
⎡
⎣⎢
⎤
⎦⎥
λ1−4nominal = −32,–5.2,–3.4,–0.01
C = 0.13 413 105 −0.320.05 −313 −81 −1.1− 9.5
⎡
⎣⎢
⎤
⎦⎥
Case c) Case b with gains multiplied by 5 for bandwidth (loop-transfer) recovery
Three stabilizing feedback control laws
39
Stochastic Root Loci! Distribution of closed-loop roots with
! Gaussian uncertainty in 10 parameters! Uniform uncertainty in velocity and air
density! 25,000 Monte Carlo evaluations
Stochastic Root Locus
! Probability of instability! a) Pr = 0.072! b) Pr = 0.021! c) Pr = 0.0076
40
Case a Case b
Case c
Ray, Stengel, 1991
Probabilities of Instability for the Three Cases
41
Stochastic Root Loci for the Three Cases
Case a: Low LQ Control Weights
Case b: High LQ Control Weights
Case c: Bandwidth Recovery
with Gaussian Aerodynamic Uncertainty
• Probabilities of instability with 30% uniform aerodynamic uncertainty– Case a: 3.4 x 10-4– Case b: 0– Case c: 0
42
American Control Conference Benchmark Control Problem, 1991• Parameters of 4th-order mass-spring system
– Uniform probability density functions for • 0.5 < m1, m2 < 1.5 • 0.5 < k < 2
• Probability of Instability, Pi– mi = 1 (unstable) or 0 (stable)
• Probability of Settling Time Exceedance, Pts – mts = 1 (exceeded) or 0 (not exceeded)
• Probability of Control Limit Exceedance, Pu – mu = 1 (exceeded) or 0 (not exceeded)
• Design Cost Function• 10 controllers designed for the competition
J = aPi2 + bPts
2 + cPu2
43
Stochastic LQG Design for Benchmark Control Problem
• SISO Linear-Quadratic-Gaussian Regulators (Marrison)– Implicit model following with control-rate weighting and
scalar output (5th order)– Kalman filter with single measurement (4th order)– Design parameters
• Control cost function weights• Springs and masses in ideal model• Estimator weights
– Search• Multivariate line search• Genetic algorithm
44
Comparison of Design Costs for Benchmark Control Problem
Cost Emphasizes Instability Cost Emphasizes
Excess ControlCost Emphasizes
Settling-Time Exceedance
Competition Designs
Four SRAD LQG
Designs
J = Pi2 + 0.01Pts
2 + 0.01Pu2 J = 0.01Pi
2 + 0.01Pts2 + Pu
2 J = 0.01Pi2 + Pts
2 + 0.01Pu2
Stochastic LQG controller more robust in 39 of 40 benchmark controller comparisons45
Marrison, Stengel, 1995
Estimation of Minimum Design Cost Using Jackknife/Bootstrapping
Evaluation
2.24 2.25 2.26 2.27 2.28 2.29 2.3 2.31 2.320
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Design Cost Estimate x 103
Pr(J < J0)
Genetic Algorithm Results
Corresponding Weibull Distribution
46Marrison, Stengel, 1995
Neighboring-Optimal Control with Uncertain Disturbance, Measurement,
and Initial Condition
47
Immune Response Example! Optimal open-loop drug therapy (control)
! Assumptions ! Initial condition known without error! No disturbance
! Optimal closed-loop therapy! Assumptions
! Small error in initial condition! Small disturbance! Perfect measurement of state
! Stochastic optimal closed-loop therapy! Assumptions
! Small error in initial condition! Small disturbance! Imperfect measurement! Certainty-equivalence applies to
perturbation control48
Immune Response Example with Optimal
Feedback ControlOpen-Loop Optimal Control for Lethal Initial Condition
Open- and Closed-Loop Optimal Control for 150% Lethal Initial Condition
49Ghigliazza, Kulkarni, Stengel, 2002
Immune Response with Full-State Stochastic Optimal Feedback Control
(Random Disturbance and Measurement Error Not Simulated)
Low-Bandwidth Estimator (|W| < |N|)
High-Bandwidth Estimator (|W| > |N|)
! Initial control too sluggish to prevent divergence
! Quick initial control prevents divergence
50Ghigliazza, Stengel, 2004
W = I4N = I2 / 20
Stochastic-Optimal Control (u1) with Two Measurements (x1, x3)
51
Immune Response to Random Disturbance with Two-Measurement
Stochastic Neighboring-Optimal Control• Disturbance due to
– Re-infection– Sequestered “pockets”
of pathogen• Noisy measurements• Closed-loop therapy is
robust • ... but not robust enough:
– Organ death occurs in one case
• Probability of satisfactory therapy can be maximized by stochastic redesign of controller
52
The End!
53