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SYSTEMSSYSTEMSIdentificationIdentification
Ali KarimpourAssistant Professor
Ferdowsi University of Mashhad
Reference: “System Identification Theory For The User” Lennart Ljung(1999)“Practical Issues of System Identification” Lennart Ljung (2007)“Perspectives on System Identification” Lennart Ljung (2009)
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Lecture 1
Perspective on System IdentificationPerspective on System Identification
Topics to be covered include:
� System Identification.
� Place System Identification on the global map. Who are our neighbors in this part of universe?
� Discuss some open areas in System Identification.
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System Identification
System Identification: The art and science of building mathematical models of dynamic systems from observed input-output data.
System Identification is look for sustainable description by proper decision on:
Model complexity
Information contents in the
data
Effective Validation
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Dynamic systems
System: An object in which variables of different kinds interact and produce observable signals.
Stimuli: External signals that affects system.
Dynamic System: A system that the current output value depends not only on the current external stimuli but also on their earlier value.
Time series: A dynamic system whose external stimuli are not observed.
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Dynamic systems
Stimuli
It can be manipulated by the observer.
Input DisturbanceIt can not be manipulated
by the observer.
Measured Unmeasured
Dynamic system
Inputu
Measured disturbance
w
Unmeasured disturbancev
Output
y
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A solar heated house
Dynamic system
Pumpvelocity
u
Solar radiation
w
Wind, outdoor temperaturev
Storage temperature
y
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Speech generation
Dynamic system
chord, vibarationairflowv
Sound
y
Time series: A dynamic system whose external stimuli are not observed.
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Models
Model: Relationship among observed signals.
Model types
1- Mental models
2- Graphical models
3- Mathematical (analytical) models
4- Software models• Split up system into subsystems,
• Joined subsystems mathematically, 1- Modeling
2- System identification
• Does not necessarily involve any experimentation on the actual system.Building
models • It is directly based on experimentation.
• Input and output signals from the system are recorded.
3- Combined
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The fiction of a true model
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The Core
The Core: The core of estimating models is statistical theory.
• Model: m
• True Description: S
• Model Class: M
• Complexity (Flexibility): C
• Information: Z
• Estimation
• Validation
• Model Fit: F(m,Z)
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Estimation
A template problem: Curve fitting
Squeeze out the relevant information
in data.
No more satisfaction
All data contains signal and noise.
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[ ])),((),(minargˆ NmChZmFm NeMm
+=∈
Estimation
The simplest explanation is usually the correct one. So the conceptual process for estimation is:
Fit measuregood agreement with data
Complexity measureNot too complex
m̂ is a random variable since of irrelevant part of data (noise).
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The System Identification Problem
1- Select an input signal to apply to the process.
2- Collect the corresponding output data.
3- Scrutinize the corresponding output data to find out if some preprocessing …
4- Specify a model structure.
5- Find the best model in this structure.
6- Evaluate the property of model.
7- Test a new structure, go to step 4.
8- If the model is not adequate, go tostep 3 or 1.
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The System Identification Problem
1- Choice of Input Signals.
2- Preprocessing Data.
3- Selecting Model Structures.
• Filtered Gaussian White Noise. • Random Binary Noise.
• Pseudo Random Binary Noise, PRBS. • Multi-Sines.
• Chirp Signals or Swept Sinusoids. • Periodic Inputs.
• Drifts and Detrending. • Prefiltering.
• Looking at the Data. • Getting a Feel for the Difficulties.
• Examining the Difficulties. • Fine Tuning Orders and Noise Structures .
• Accepting the Models .
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The Communities around the core
1- Statistics. ML Methods, Bootstrap method,…
2- Econometrics and time series analysis.
3- Statistical learning theory.
4- Machine learning.
5- Manifold learning.
6- Chemo metrics.
7- Data Mining.
8- Artificial Neural Network.
9- Fitting Ordinary Differential equation to data.
10- System Identification.
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Some Open Areas in System Identification
• Spend more time with neighbors.
• Model Reduction and System Identification.
• Issues in Identification of Non-linear Systems.
• Meet Demand from Industry.
• Convexification.
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Model Reduction
System identification is really “system approximation” and therefore closely related to model reduction.
Linear systems – Linear models. Divide, conquer and reunite.
Non-linear systems – Linear models. Is it good for control?
Non-linear systems – nonlinear reduced models. Much work remains.
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Linear Systems – Linear ModelsDivide-Conquer-Reunite
Helicopter data: 1 pulse input; 8 outputs (only 3 shown here)
State space of order 20 wanted.
18 is 208 is102 is 2002 is
××+=××+=
dCduCxybAbuAxx&
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Linear Systems – Linear ModelsDivide-Conquer-Reunite
Next fit 8 SISO models of order 12, one for each output
11 is 121 is121 is 1221 is i
××+=××+=
iiiii
iiiii
dcudxcybAubxAx& 8,...,2,1=i
duCxybuAxx
+=+=&
189681969696
××××
dCbA
Reunite
Order reduction
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Linear Systems – Linear ModelsDivide-Conquer-Reunite
Reduce model from 96 to 20
duCxybuAxx
+=+=&
189681969696
××××
dCbA
udxCy
ubxAxˆˆˆ
ˆˆˆˆ
+=
+=&
18ˆ208ˆ120ˆ2020ˆ
××
××
dC
bA
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Convexification