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3 Outline Experiment design Open loop/ Closed loop Spectra Validatoin C(G) G0G0 max min Model complexity max min Computational resources max min User skill Prior information max min max min Performance specifications G0G0 Noise Experiment Inform. contents Feedback max min Exp. constraints Control design/Estimator=Controller True system acts as disturbance ”Feedback” beneficial !! Perf. specs C(G) G + - Robust control Parameter estimation
Citation preview
1
From Experiments to
Closed Loop Control II: The X-files
Håkan HjalmarssonDepartment of Signals, Sensors and SystemsRoyal Institute of Technology
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The Problem
Controller
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OutlineE
xper
imen
t des
ign Open loop/
Closed loop
Spectra
Val
idat
oin
C(G) G0
max
min
Model complexity
max
min
Computational resources
max
min
User skill
Prior informationmax
minmax
min
Performance specifications
G0 Noise
Exp
erim
ent
Inform. contents
Feedback
max
minExp
. con
stra
ints
•Control design/Estimator=Controller
•True system acts as disturbance ”Feedback” beneficial !!
Perf
. spe
cs
C(G)
G +-
Robust control
Parameter estimation
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Robust Control
•Frequency by frequency bounds on the model error usually required for robust stability and robust performance
•Trade-off performance vs model quality, e.g:
= |(G0 G -1 - I ) T(G,C)|
sufficiently small
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Next Topic: Parameter EstimationE
xper
imen
t des
ign Open loop/
Closed loop
Spectra
Val
idat
oin
C(G) G0
max
min
Model complexity
max
min
Computational resources
max
min
User skill
Prior informationmax
minmax
min
Performance specifications
G0 Noise
Exp
erim
ent
Inform. contents
Feedback
max
minExp
. con
stra
ints
Perf
. spe
cs
C(G)
G +-
Robust control
Parameter estimation
What are the means to control the model error in parameter estimation?
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Parameter Estimation Essentials
u yv
Model: y = G()u + H()v, 2 Rm
Prediction error: () = H()-1 (y - G()u )
G0G0= B0/A0
e0 (white noise, variance )H0=
C0/D0True system:
N
t
t1
),(minargˆ 2
Parameter estimate:
)],([ Eminarg 2*
tLimit estimate:
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Decomposing the Model Error
Model error: E [| G0 - G |2G0 - G* |2 + E [| G * - G |
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MSE Bias Error Variance Error
^ ^
Parameter estimation
Limit model
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The Bias Error
) Can only tune the L2 norm of the bias error
) Cannot guarantee stability
Much effort in literature on tuning the bias – Neglects information contents in data
d
H
HH
HuGGt 2
2
122|)(|
2|)(0|2|)(|
2|)(0|)],(E[
Parameter estimation
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Statistical Aspects of Restricted Complexity Modeling
Reduced order controller
Data + Priors
Reduced order model
Full order controllerFull order model
Model
Which way is best wrt statistical accuracy?
How should we identify a restricted complexity model such that noise impact minimized?
Example:
Parameter estimation
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An Illustrative Example
•The noise is white, Gaussian and has unit variance
•Many parameters but
Restricted Complexity Modeling Statistical Aspects
Estimate static gain:
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Method 1: Maximum Likelihood (= Least-Squares)
Method 2: Biased
Biased beats ML!!
Restricted Complexity Modeling Statistical Aspects
Full order model:
Only one parameter:
Variance contribution from
unmodeled dynamics
Bias error
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But ......
Why not take the first parameter of the ML estimate as estimate?
•Same bias as biased estimate
•Lower variance – no unmodelled dynamics that contribute
ML beats biased!!
Restricted Complexity Modeling Statistical Aspects
Variance of first parameter Bias error
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A Separation Principle
AML of ^f() AML of f()
Restricted Complexity Modeling Statistical Aspects
The invariance principle in statistics:
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Optimal Identification for Performance
Restricted Complexity Modeling Statistical Aspects
) The minimizing G is a function of G0
1. Estimate ML model GML of G0
2. Optimal reduced order estimate:
Bonus: Stability can be checked
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Applications:•Model reduction (Tjärnström and Ljung)
•Simulation (Zhu and van den Bosch)
•Estimation of model uncertainty
•I4C
Conclusion: Always model as well as possible before any model simplifications
Restricted Complexity Modeling Statistical Aspects
The Separation Principle
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SummaryRestricted Complexity Modeling
Statistical Aspects
Reduced order controller
Data + PriorsFull order controllerFull order model
•For a given data set, always model as well as possible in order to ensure best possible statistical properties
Reduced order model
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Moving Towards Real Applications•We have to accept that reality is always more complex than our models
•Bias error in general not quantifiable frequency by frequency (unless priors are introduced)
•How do we cope with this?
(and we do – there are numerous success stories)
Restricted Complexity Modeling
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Near Optimal ModelingExample continued:
) One parameter model optimal (for estimating G(0) )
regardless of system complexity!
Restricted complexity model same accuracy as ML!
Suppose
Restricted Complexity Modeling
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Restricted Complexity ModelingNear Optimal Modeling
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Non-singular Case
•LS-estimation provides near optimal models if MSE is small enough
Restricted Complexity ModelingNear Optimal Modeling
Level set of 2(t,)
•All models in confidence region qualify as good models (within a factor 2)!
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Experimental conditions can be used to:
1. Ensure near statistical optimality of restricted complexity models by making uncertainty large in certain ”directions” (Let sleeping dogs lie)
Allows the bias error to be assessed by the variance error
Restricted Complexity ModelingNear Optimal Modeling
Conclusions from Example
2. Ensure that certain system properties can be estimated accurately no matter the system complexity by making uncertainty small in certain ”directions”
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|G±-G|^
The role of the noise modelModels with small MSE good ) Also noise model important Example: 3rd order Box-Jenkins system
Noise model useful in near optimal modeling!
2nd order OE 2nd order BJ
3rd order BJ
Restricted Complexity Modeling Statistical Aspects
Near Optimal Modeling
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f()
Near optimal models and the separation princple
Using near optimal estimates in the separation principle leads to
near optimal estimates of !
N^ N
Restricted Complexity Modeling Statistical Aspects
Near Optimal Modeling
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Summary• Models inside confidence region of full-order model are near optimal
• Can be obtained by least-squares identification
• The noise model is important
• The separation principle is applicable
• Experimental conditions determine which models are near optimal!
•But we need full-order model for model error quantification - The Achilles heel.
Restricted Complexity Modeling Near Optimal Modeling
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Safe System Id
•Let’s examine the variance error!
•and then experiment design issues
How and for which system properties can this be achieved?????
Ensure that certain system properties can be estimated accurately no matter the system complexity by making uncertainty small in certain ”directions”
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The Objectiven
o true parameters (n = dimension)
m=fn(n) quantities of interest (e.g. nmp zeros, freq resp at certain freq. ....). Dimenson m<n.
P=Covariance of n
Cov(m) = fn´(no)P[fn´(n
o)]T
Criterion: J=Trace(Cov(m))
Constraint: s u()d ·Q: When can we choose u such that J is small regardless of n?
Safe System Id
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Some first insightSafe System Id
Cov(m) = fn´(no)P[fn´(n
o)]T
Criterion: J=Trace(Cov(m))Constraint: s u()d ·
Suppose fn´ normalized so that it is an ON-matrix
Choose u such that “smallest” eigenvectors of P , fn´
This gives J= sum of m smallest eigenvalues of P (which are related to u)
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FIR case
• P-1= s-n n
* u d /
where n=[1 e-j ... e-j(n-1)]T
• Eigenvalue(P-1)=1/igenvalue(P)
• P-1 and P have the same eigenvectors
Asymptotic results for P-1 (Grenander Szegö):
i) eigenvalues , u(2 k/n), k=1,..n
ii) eigenvectors , n(2 k/n) (which are orthogonal)
Safe System Id
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ExampleSafe System Id
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Eigenvalues of P vs u
Cosine for angle between n and eigenvectors of P
Safe System Id
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Input Design Recipe1. Choose freq for which n span fn´
2. Choose u as large as possible at these freq. bins.
Safe System Id
How to combat system complexity:
If n continues to span fn’ as n is increased, then the accuracy is insensitive to the model order
cf static gain example!
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Illustrations
•NMP-zero estimates
•Variance of frequency function estimates
Safe System Id
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NMP-zerosf’n=[1 zo
-1 zo-2 ....]T
•Tail elements become smaller and smaller
) Variance converges as n!1
Safe System Id
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Zero estimates for 50 realizations
Unit circle
NMP zeroMP zero
ExampleSafe System Id
NMP-zeros
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The Variance Error of Frequency Funtion Estimates
Generally very complicated function of
•Input spectrum u
•Noise spectrum v
•True system G0
But can always be expressed as n,N/ N v/ u
n = # estimated parameters
PGGG *20 ]|ˆGE[|
Covariance matrix of parameter estimate
Safe System Id
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An archetypical variance expression
Expression for ???
Variance of Frequency Function Estimates
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Large sample expression (LSE)Variance of Frequency Function Estimates
Asymptotic Expressions
•Xie & Ljung (TAC 2001): Fixed denominator + AR input
• Ninness and Hjalmarsson (TAC 2004): BJ model + AR input
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Comparison with true variance
HOE-85
LSETRUE
Variance of Frequency Function EstimatesAsymptotic Expressions
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Safe System Id: FIR systems
Suppose 1¼ ej1 and all other poles at origin:
AR-input: u=1/F w, n=degree of F.
•Last term dominates for ¼1: Insensitive to model order
•Last term small for 1 : Variance grows linearly with order
Safe System IdVariance of Frequency Function Estimates
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Summary
•Focusing the input spectrum to a certain frequency region makes the model accuracy less dependent on the model complexity in this range
• Penalty at other frequency regions
• Classical m/N v/u expression toooo optimistic variance approximation around narrow peaks of the input spectrum
Safe System IdVariance of Frequency Function Estimates
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Near Optimal ModelingSafe System Id
Suppose I practice safe system id.
Then I know that with a full order, or overparameterized, model I will get what I want.
Q: What if I use a restricted complexity model?
Well, for a near optimal model it has to be inside the confidence region of the full order model which means that it has to model the important system features accurately!
cf static gain example!
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Summary
•Use input to reveal important system features
•and be prepared to model these
•”Standard” model uncertainty estimates valid for these features
•Let sleeping dogs lie
•Ensure that application take large model uncertainties into account for other system features (the dogs)
Make sure the data speaks what you need to hear!
Modeling Paradigm:
Safe System Id
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Experiment Design for Safe System Id
•Robust stability
•NMP-zeros
•One impulse response coefficient
•(Static gain)
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Experiment Design for Robust Stability
Experiment Design
Robust stability:
Confidence bounds:
PGGG *20 ]|ˆGE[| Variance:
Can be transformed into a problem that is convex in the autocovariances of the input, cf Märta’s
talk on MondaySafe System Id: Design for higher system order than you believe the system to be
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Input Design for Estimation of NMP-zeros
Minu E u2
s.t Var zNMP· Result:
For AR-models regardless of model order use
• first order AR-input with pole = NMP-zero mirrored
• Minimum input variance =
Experiment Design
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Optimal Design vs White NoisePower with
optimal design/Power with white noise
design
NMP zero location
Experiment DesignNMP zeros
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Restricted Complexity Modeling
Experiment DesignNMP zeros
Recall that in the static gain example the optimal input (designed for a full order model) lead to that a simple model could be used with same accuracy.
5th order ARX-system with
1 NMP-zero z=1.2, 2 MP-zeros
5th order ARX-model with 1 zero
36 hour old results:
White input: z = -0.49
Optimal AR-1 input: z=1.17
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First Impulse Response Coefficient
Experiment Design
•Only g1o of interest
•White noise optimal independently of system complexity.
•Variance of estimate independent of system complexity
•y(t)= u(t-1) gives consistent estimate and same variance as full order model
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Summary• Convex reformulations
•A wide range of criteria can be handled
• There seems to be a connection between optimal designs and restricted complexity modeling
Experiment Design
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Summary of Summaries• The Separation Principle
• Near Optimal Models
• The Fundamental Importance of Experiment Design
•Insensitivity to system complexity
•Let sleeping dogs lie
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