ME 233 Review - ME233 Advanced Control Systems II, UC …€¦ · Random processes (ME233 Class...
Transcript of ME 233 Review - ME233 Advanced Control Systems II, UC …€¦ · Random processes (ME233 Class...
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ME 233 Advanced Control II
Continuous time results 1
Random processes
(ME233 Class Notes pp. PR6-PR13)
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Such that for any time ,
Random Process
A random processes is a continuous function
of time
Is a random variable defined over the same probability space
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ExampleE
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ExampleE
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sample function
process realization
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Random process
Let be a collection of times
This is often a huge amount of redundant information
is the joint PDF of
Let be a random process
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2nd order statistics
Expected value or mean of X(t),
Let be a random vector process
Auto-covariance function:
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Auto-covariance function
Define:
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Strict Sense Stationary random sequence
is Strict Sense Stationary (SSS) if the joint probability, is
invariant with time
A random process
for any time shift T,
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Ergodicity
is ergodic if we can recover an ensemble average
from the time average of any realization:
A Strict Sense Stationary random process
with probability 1
(almost surely)
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Wide Sense Stationarity
is Wide Sense Stationary (WSS) if:
A random sequence
1) Its mean is time invariant
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Wide Sense Stationarity
is Wide Sense Stationary (WSS) if:
A random sequence
2) Its covariance only depends on the correlation
shift
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Wide Sense Stationarity
The auto-covariance function can be defined only as
a function of the correlation time shift
Notice that:
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Cross-covariance function
The cross-covariance function:
Let and
be two WSS random vector processes
for any time t
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Cross-covariance function
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Power Spectral Density FunctionFor WSS random process, the power spectral density
function is the Fourier transform of the auto-
covariance function:
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Power Spectral Density FunctionSince,
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White noise
A WSS random process is white if:
Where is the Dirac delta impulse
white noise is zero mean if
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White noise
The power spectral density function for white noise
is:
Proof:
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White noise
0
WW ()
w0
WW (w)
Infinite bandwidth
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White noise vector process
A WSS random vector sequence is
white if:
where
and is the Dirac delta impulse
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MIMO Linear Time Invariant Systems
Let
be the impulse response of an LTI SISO system
with transfer function
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MIMO Linear Time Invariant Systems
Let be WSS
Then the forced response (zero initial state)
is also WSS
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MIMO Linear Time Invariant Systems
We will assume that
• The WSS random process
is zero mean, I.e.
Thus, the output random process is also zero mean
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MIMO Linear Time Invariant Systems
If
Let be WSS
Then:
G(t)
G()
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MIMO Linear Time Invariant Systems
Let be a WSS random process
G(w)
G(s)
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MIMO Linear Time Invariant Systems
Let be a WSS random process
G(s)
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MIMO Linear Time Invariant Systems
If
Let be a WSS vector random
process
Then:
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MIMO Linear Time Invariant Systems
Proof:
Then:
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MIMO Linear Time Invariant Systems
If
Let be WSS
Then:
G(t)
G()
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MIMO Linear Time Invariant Systems
Let be a WSS random process
G(s)
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MIMO Linear Time Invariant Systems
Proof: Remember that
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MIMO Linear Time Invariant Systems
If
Let be WSS
Then:
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MIMO Linear Time Invariant Systems
Proof: Use
and
then
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White noise driven state space systems
Consider a LTI system driven by white noise:
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White noise driven state space systems
Assume that W(t) is white, but not stationary
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White noise driven state space systems
Assume state Initial Conditions (IC):
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White noise driven state space systems
Taking expectations on the equations above, we obtain:
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White noise driven state space systems
Subtracting the means,
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White noise driven covariance propagation
with
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White noise driven covariance propagation
Also,
where:
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White noise driven covariance propagation
Also,
where:
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Stationary covariance equation
For W(t) WSS,
and A Hurwitz,
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Stationary covariance equation
For W(t) WSS,
Satisfies:
and A Hurwitz,
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The next section contains
some Proofs of the CT
results
Please go over them by
yourselves…
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Proof of continuous time results – Method 1
We first prove that:
By starting from the Discrete Time (DT) results
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Proof of continuous time results – Method 1
Approximate the state equation ODE
using the Euler numerical integration method.
• We have to be careful in dealing with white
noise
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Approximate
1. Define as the time average of
Similarly, taking expectations
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Approximate for W(t) white
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Approximate for W(t) white
since for W(t) white Dirac impulse
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Approximate for W(t) white
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Approximate for W(t) white
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Where is the time average of
Approximate for W(t) white
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Numerical Integration
The state equation
By the discrete time state equation
where
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1. Obtain DT state equations by approximating the CT state equation solution:
Thus,
where
Proof of continuous time results – Method 1
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Proof of continuous time results – M1
2. Obtain the CT covariance propagation equation from from the DT covariance propagation, using the approximated DT state equation:
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Proof of continuous time results – M1
3. Take the limit as of
and noticing that
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58
Proof of continuous time results – M1
3. Take the limit as of
Thus,
t t
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Proof of continuous time results – Method 2
We now proof that:
Directly from continuous time (CT) results
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60
Proof of continuous time results – M2
1) Lets calculate
using
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61
Proof of continuous time results – M2
2) We now need to calculate
using
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62
Proof of continuous time results – M2
2) We now need to calculate
using
(notice that the Dirac impulse occurs at the edge t)
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63
Proof of continuous time results – M2
2) Continuing,
(make integral symmetrical w/r 0)
0
ΔT
ΔT1
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64
Proof of continuous time results – M2
2) A similar calculation for
yields
(notice that the Dirac impulse occurs at the edge t)
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65
Proof of continuous time results – M2
2) Continuing,
(make integral symmetrical w/r 0)
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66
Proof of continuous time results – M2
2) Thus
and
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67
Proof of continuous time results – M2
Now we proof that:
Notice that:
where,
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Proof of continuous time results – M2
Therefore,
Notice that and are uncorrelated for
0
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69
Proof of continuous time results – M2
Thus,