me 233 review · 2016. 5. 12. · 1 me 233 advanced control ii continuous time results 2 kalman...
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ME 233 Advanced Control II
Continuous time results 2
Kalman filters
(ME233 Class Notes pp.KF7-KF10)
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Outline
• Continuous time Kalman Filter
• LQ-KF duality
• KF return difference equality
– symmetric root locus
• ARMAX models
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Stochastic state model
Consider the following nth order LTI system with
stochastic input and measurement noise:
Where:
• deterministic (known) input
• Gaussian, white noise, zero mean, input noise
• Gaussian, white noise, zero mean, meas. noise
• Gaussian
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Assumptions
• Initial conditions:
• Noise properties (in addition to Gaussian),:
Conditional estimation
• Conditional state estimate
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• Conditional state estimation error covariance
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CT Kalman Filter
Kalman filter:
Where:
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Steady State KF Theorem:
1) If the pair (C, A) is observable (or detectable):
The solution of the Riccati differential equation
Converges to a stationary solution, which satisfies the
Algebraic Riccati Equation (ARE):
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Steady State KF Theorem:
2) If in addition to 1) the pair (A,B’w) is controllable (stabilizable), where
The solution of the Algebraic Riccati Equation (ARE):
is unique, positive definite (semi-definite) , and the close loop observer
matrix
is Hurwitz.
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Steady State Kalman FilterTheorem:
3) Under stationary noise and the conditions in 1) and 2),
The observer residual
of the KF:
becomes white
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KF as a innovations (whitening) filter
White noise
input Colored noise
output
White noise
output
plant
Kalman filter
W(s) Y(s)+
C Bw (s)
-
+
L
Y(s)
Y(s)^
~
C (s)
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LQR dualityCost:
Where:
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Kalman Filter & LQR DualityComparing ARE’s and feedback gains, we obtain the following
duality
LQR KF
P M
A AT
B CT
R V
CQT B’w = BwW 1/2
K LT
(A-BK) (A-LC)T
duality
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W(s) Y(s)+
C Bw (s)
-
+
L
Y(s)
Y(s)^
~
C (s)
KF return difference equality
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KF root locus for SISO Systems
W(s) Y(s)+
C Bw (s)
-
+
L
Y(s)
Y(s)^
~
C (s)
roots are plant poles
roots are Kalman filter poles
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KF symmetric root locus for SISO Systems
roots are plant poles
roots are Kalman filter poles
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KF Loop gain and phase margins (SISO)
Consider the closed loop sensitive transfer function
-
+
L
Y(z) Y (z)o
Y (z)o
~
C (z)
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KF Loop phase margins (SISO)
Since,
The phase margin of is greater than or equal
to 60 degrees.
-
+
L
Y(z) Y (z)o
Y (z)o
~
C (z)
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KF Loop gain margins (SISO)
Estimator was designed for
Estimator is guaranteed to remain asymptotically stable for
-
+
L
Y(z) Y (z)o
Y (z)o
~
C (z)
SISO ARMAX model:
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SISO ARMAX stochastic models
(Hurwitz)
Kalman filter innovations (residual)
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Y(s)
U(s)
+
Y(s)~
SISO ARMAX stochastic models
one random
white noise
input
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Outline
– Continuous time Kalman Filter
– LQ-KF duality
– KF return difference equality
• symmetric root locus
– ARMAX models
Additional material:
• Derivation of continuous time Kalman Filter
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Derivation of the CT Kalman Filter
1. Approximate the CT state estimation problem by a
DT state estimation problem .
2. Obtain the DT Kalman filter for the DT state
estimation problem.
3. Obtain the CT Kalman filter from the DT Kalman
filter by taking the limit as the sampling time
approaches to zero.
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CT Kalman Filter
Consider the following nth order LTI system with
stochastic input and measurement noise:
Where:
• deterministic input
• Gaussian, white noise, zero mean, input noise
• Gaussian, white noise, zero mean, meas. noise
• Gaussian
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Derivation of the CT Kalman Filter
1. Approximate the CT state estimation problem by a
DT state estimation problem .
2. Obtain the DT Kalman filter for the DT state
estimation problem.
3. Obtain the CT Kalman filter from the DT Kalman
filter by taking the limit as the sampling time
approaches to zero.
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Derivation of the CT Kalman Filter
1. Approximate the CT state estimation problem by a
DT state estimation problem :
• State and output equations:
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Derivation of the CT Kalman Filter
• Covariances (from pages 48-52 in random process
lecture 8):
Notice that:
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Derivation of the CT Kalman Filter
• Covariances:
Notice that:
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Derivation of the CT Kalman Filter
2. Obtain the DT Kalman filter for the DT state
estimation problem.
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Derivation of the CT Kalman Filter3) Obtain the CT Kalman filter from the DT Kalman filter.
• State Equation:
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Derivation of the CT Kalman Filter
Taking limit as
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Derivation of the CT Kalman Filter
Kalman filter gain
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Derivation of the CT Kalman Filter
Riccati equation
Subtracting from both sides and dividing by
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Derivation of the CT Kalman Filter
Taking
we obtain