Iterative System Identification & Control Design: an...
Transcript of Iterative System Identification & Control Design: an...
Iterative System Identification & Control
Design:an approach to Adaptive
Control
Bob BitmeadMechanical & Aerospace Engineering
University of California, San Diego
MAE283B Approximate System Identification & ControlTuesday, April 3, 2012
Iterative System Identification & Control
Design:an approach to Adaptive
Control
Bob BitmeadMechanical & Aerospace Engineering
University of California, San Diego
MAE283B Approximate System Identification & Control
?
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
The Big Picture
2Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
The Big Picture
2Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
The Big Picture
2Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
The Big PictureThe interaction between modeling and model-based control design
when the objective is to achieve good feedback controland the model is derived from experimental data plus physics
premise: all models are necessarily approximatethe approximation properties are central
2Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
The Big PictureThe interaction between modeling and model-based control design
when the objective is to achieve good feedback controland the model is derived from experimental data plus physics
premise: all models are necessarily approximatethe approximation properties are central
2
Aha!
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
The Big PictureThe interaction between modeling and model-based control design
when the objective is to achieve good feedback controland the model is derived from experimental data plus physics
premise: all models are necessarily approximatethe approximation properties are central
2
Aha!The model fit and quality of fit affects the achieved controlThe presence of the feedback controller affects the model fit
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
The Big PictureThe interaction between modeling and model-based control design
when the objective is to achieve good feedback controland the model is derived from experimental data plus physics
premise: all models are necessarily approximatethe approximation properties are central
2
Aha!The model fit and quality of fit affects the achieved controlThe presence of the feedback controller affects the model fit
This course is about formulating the connections
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
The Big PictureThe interaction between modeling and model-based control design
when the objective is to achieve good feedback controland the model is derived from experimental data plus physics
premise: all models are necessarily approximatethe approximation properties are central
2
Aha!The model fit and quality of fit affects the achieved controlThe presence of the feedback controller affects the model fit
This course is about formulating the connectionsdo this so as to gain a synergism
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
The Big PictureThe interaction between modeling and model-based control design
when the objective is to achieve good feedback controland the model is derived from experimental data plus physics
premise: all models are necessarily approximatethe approximation properties are central
2
Aha!The model fit and quality of fit affects the achieved controlThe presence of the feedback controller affects the model fit
This course is about formulating the connectionsdo this so as to gain a synergism
better models ⇒ better control ⇒ better models ⇒ ...
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
The Big PictureThe interaction between modeling and model-based control design
when the objective is to achieve good feedback controland the model is derived from experimental data plus physics
premise: all models are necessarily approximatethe approximation properties are central
2
Aha!The model fit and quality of fit affects the achieved controlThe presence of the feedback controller affects the model fit
This course is about formulating the connectionsdo this so as to gain a synergism
better models ⇒ better control ⇒ better models ⇒ ...
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximation
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantity
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantityParsimony is needed
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantityParsimony is needed
Modeling is an iterative process
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantityParsimony is needed
Modeling is an iterative processModel development
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantityParsimony is needed
Modeling is an iterative processModel development
Deductive Physics, Inductive System Identification
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantityParsimony is needed
Modeling is an iterative processModel development
Deductive Physics, Inductive System IdentificationRinse and repeat
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantityParsimony is needed
Modeling is an iterative processModel development
Deductive Physics, Inductive System IdentificationRinse and repeat
Modeling has a purpose
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantityParsimony is needed
Modeling is an iterative processModel development
Deductive Physics, Inductive System IdentificationRinse and repeat
Modeling has a purposePrediction, simulation, control
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantityParsimony is needed
Modeling is an iterative processModel development
Deductive Physics, Inductive System IdentificationRinse and repeat
Modeling has a purposePrediction, simulation, control
Differing purposes make differing demands of model quality
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantityParsimony is needed
Modeling is an iterative processModel development
Deductive Physics, Inductive System IdentificationRinse and repeat
Modeling has a purposePrediction, simulation, control
Differing purposes make differing demands of model qualityModeling from data depends on the experiment conducted
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantityParsimony is needed
Modeling is an iterative processModel development
Deductive Physics, Inductive System IdentificationRinse and repeat
Modeling has a purposePrediction, simulation, control
Differing purposes make differing demands of model qualityModeling from data depends on the experiment conducted
including the presence of a feedback controller
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Modeling
Modeling involves approximationA general-relativistic, quantum-mechanical partial differential equation model for an ore crusher
Model approximation is a fungible but constrained quantityParsimony is needed
Modeling is an iterative processModel development
Deductive Physics, Inductive System IdentificationRinse and repeat
Modeling has a purposePrediction, simulation, control
Differing purposes make differing demands of model qualityModeling from data depends on the experiment conducted
including the presence of a feedback controller
3Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Control
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Control
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Control
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
Stabilization
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
StabilizationDisturbance rejection
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
StabilizationDisturbance rejection
Reference tracking
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
StabilizationDisturbance rejection
Reference trackingReduction in the effect of system variability
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
StabilizationDisturbance rejection
Reference trackingReduction in the effect of system variability
the feedback amplifier; Black, Nyquist, Bode
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
StabilizationDisturbance rejection
Reference trackingReduction in the effect of system variability
the feedback amplifier; Black, Nyquist, BodeAdvanced Control is Model-based
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
StabilizationDisturbance rejection
Reference trackingReduction in the effect of system variability
the feedback amplifier; Black, Nyquist, BodeAdvanced Control is Model-based
Model approximation is an in-built issue
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
StabilizationDisturbance rejection
Reference trackingReduction in the effect of system variability
the feedback amplifier; Black, Nyquist, BodeAdvanced Control is Model-based
Model approximation is an in-built issueThe nominal model — the specific model used for design
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
StabilizationDisturbance rejection
Reference trackingReduction in the effect of system variability
the feedback amplifier; Black, Nyquist, BodeAdvanced Control is Model-based
Model approximation is an in-built issueThe nominal model — the specific model used for designThe model error — the range of possible deviation from nominal
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
StabilizationDisturbance rejection
Reference trackingReduction in the effect of system variability
the feedback amplifier; Black, Nyquist, BodeAdvanced Control is Model-based
Model approximation is an in-built issueThe nominal model — the specific model used for designThe model error — the range of possible deviation from nominal
Performance — nominal model behavior
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
StabilizationDisturbance rejection
Reference trackingReduction in the effect of system variability
the feedback amplifier; Black, Nyquist, BodeAdvanced Control is Model-based
Model approximation is an in-built issueThe nominal model — the specific model used for designThe model error — the range of possible deviation from nominal
Performance — nominal model behaviorRobustness — variation due to model error
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about ControlFeedback control is used for a number of simultaneous purposes
StabilizationDisturbance rejection
Reference trackingReduction in the effect of system variability
the feedback amplifier; Black, Nyquist, BodeAdvanced Control is Model-based
Model approximation is an in-built issueThe nominal model — the specific model used for designThe model error — the range of possible deviation from nominal
Performance — nominal model behaviorRobustness — variation due to model error
4Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Adaptive Control
5Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Adaptive Control
5Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Adaptive Control
5Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Adaptive Control
5
“Adaptive Control Theory has been a colossal waste of paper”
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Adaptive Control
5
“Adaptive Control Theory has been a colossal waste of paper”Bob Bitmead
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Adaptive Control
5
“Adaptive Control Theory has been a colossal waste of paper”Bob Bitmead, Adaptive Control Theorist
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Adaptive Control
Adaptive Control seeks to combine on-line model adjustment and real-time controller design based on operational data taken during closed-loop plant operation
5
“Adaptive Control Theory has been a colossal waste of paper”Bob Bitmead, Adaptive Control Theorist
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Adaptive Control
Adaptive Control seeks to combine on-line model adjustment and real-time controller design based on operational data taken during closed-loop plant operation
5
“Adaptive Control Theory has been a colossal waste of paper”Bob Bitmead
PlantController
IdentifierControlDesign
+
disturbance
output
input
model
+reference
, Adaptive Control Theorist
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Adaptive Control
Adaptive Control seeks to combine on-line model adjustment and real-time controller design based on operational data taken during closed-loop plant operation
5
“Adaptive Control Theory has been a colossal waste of paper”Bob Bitmead
In spite of linear modeling and linear control design, this is a fiercely nonlinear problem
PlantController
IdentifierControlDesign
+
disturbance
output
input
model
+reference
, Adaptive Control Theorist
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Messages about Adaptive Control
Adaptive Control seeks to combine on-line model adjustment and real-time controller design based on operational data taken during closed-loop plant operation
5
“Adaptive Control Theory has been a colossal waste of paper”Bob Bitmead
In spite of linear modeling and linear control design, this is a fiercely nonlinear problem
PlantController
IdentifierControlDesign
+
disturbance
output
input
model
+reference
, Adaptive Control Theorist
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
More Adaptive Control Messages
6Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
More Adaptive Control Messages
6Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
More Adaptive Control Messages
6Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
More Adaptive Control MessagesAdaptive Control involves simultaneous modeling and control design
Nonlinear nasties can appear — escapes, chaos, oscillationThis greatly upsets our linear thinking for models and controllers
Is there a way forward?
6Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
More Adaptive Control MessagesAdaptive Control involves simultaneous modeling and control design
Nonlinear nasties can appear — escapes, chaos, oscillationThis greatly upsets our linear thinking for models and controllers
Is there a way forward?
Adaptation is a learning approach to dealing with uncertaintyIt is well understood that learning is often antithetical to control
Control is designed to disguise system variationLearning is focused on exposing system variation
6Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
More Adaptive Control MessagesAdaptive Control involves simultaneous modeling and control design
Nonlinear nasties can appear — escapes, chaos, oscillationThis greatly upsets our linear thinking for models and controllers
Is there a way forward?
Adaptation is a learning approach to dealing with uncertaintyIt is well understood that learning is often antithetical to control
Control is designed to disguise system variationLearning is focused on exposing system variation
Dual Adaptive ControlStrike a balance between learning and controlling
Fel’dbaum 1960s
6Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
More Adaptive Control MessagesAdaptive Control involves simultaneous modeling and control design
Nonlinear nasties can appear — escapes, chaos, oscillationThis greatly upsets our linear thinking for models and controllers
Is there a way forward?
Adaptation is a learning approach to dealing with uncertaintyIt is well understood that learning is often antithetical to control
Control is designed to disguise system variationLearning is focused on exposing system variation
Dual Adaptive ControlStrike a balance between learning and controlling
Fel’dbaum 1960s
6Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Modeling Approximation Formulalim
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Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
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Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Modeling Approximation Formula
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Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Modeling Approximation Formula
It existsIt is a discrete frequency domain formula
Connected to time domain prediction errorsIt is useful
Model approximation is the subjectPlant, noise, plant model, noise model involved
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Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Modeling Approximation Formula
It existsIt is a discrete frequency domain formula
Connected to time domain prediction errorsIt is useful
Model approximation is the subjectPlant, noise, plant model, noise model involved
There are some free variables - design handlesInput spectrumData filter
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Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Modeling Approximation Formula
It existsIt is a discrete frequency domain formula
Connected to time domain prediction errorsIt is useful
Model approximation is the subjectPlant, noise, plant model, noise model involved
There are some free variables - design handlesInput spectrumData filter
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Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Modeling Approximation Formula
It existsIt is a discrete frequency domain formula
Connected to time domain prediction errorsIt is useful
Model approximation is the subjectPlant, noise, plant model, noise model involved
There are some free variables - design handlesInput spectrumData filter
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Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Closed-loop Modeling Formulalim
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MAE 283B, Lecture 1, Slide Approximate System Identification & Control
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8Tuesday, April 3, 2012
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Closed-loop Modeling Formulalim
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8Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Closed-loop Modeling Formula
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8Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Closed-loop Modeling Formula
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MAE 283B, Lecture 1, Slide Approximate System Identification & Control
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Robust Control Formulae!
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Robust Control Formulae
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Robust Control Formulae
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Robust Control Formulae
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9Tuesday, April 3, 2012
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9Tuesday, April 3, 2012
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9Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course Outline
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course Outline
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course Outline
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophy
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopter
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief review
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loop
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structure
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structureLecture 7 - Influencing model fit in closed loop
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structureLecture 7 - Influencing model fit in closed loopLecture 8 - Designing controllers with approximate models, robust control
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structureLecture 7 - Influencing model fit in closed loopLecture 8 - Designing controllers with approximate models, robust controlLecture 9 - Iterative approaches to modeling and control design
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structureLecture 7 - Influencing model fit in closed loopLecture 8 - Designing controllers with approximate models, robust controlLecture 9 - Iterative approaches to modeling and control designLectures 10, 11 - Industrial example — Victoria Sugar Mill iterative design
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structureLecture 7 - Influencing model fit in closed loopLecture 8 - Designing controllers with approximate models, robust controlLecture 9 - Iterative approaches to modeling and control designLectures 10, 11 - Industrial example — Victoria Sugar Mill iterative designLectures 12, 13 - Cautious controller tuning, Controller certification
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structureLecture 7 - Influencing model fit in closed loopLecture 8 - Designing controllers with approximate models, robust controlLecture 9 - Iterative approaches to modeling and control designLectures 10, 11 - Industrial example — Victoria Sugar Mill iterative designLectures 12, 13 - Cautious controller tuning, Controller certificationLecture 14 - Iterative versus Adaptive Control
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structureLecture 7 - Influencing model fit in closed loopLecture 8 - Designing controllers with approximate models, robust controlLecture 9 - Iterative approaches to modeling and control designLectures 10, 11 - Industrial example — Victoria Sugar Mill iterative designLectures 12, 13 - Cautious controller tuning, Controller certificationLecture 14 - Iterative versus Adaptive ControlLecture 15 - Observability in a stochastic context
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structureLecture 7 - Influencing model fit in closed loopLecture 8 - Designing controllers with approximate models, robust controlLecture 9 - Iterative approaches to modeling and control designLectures 10, 11 - Industrial example — Victoria Sugar Mill iterative designLectures 12, 13 - Cautious controller tuning, Controller certificationLecture 14 - Iterative versus Adaptive ControlLecture 15 - Observability in a stochastic contextLecture 16 - Adaptive control in action — TCP network congestion control
10Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structureLecture 7 - Influencing model fit in closed loopLecture 8 - Designing controllers with approximate models, robust controlLecture 9 - Iterative approaches to modeling and control designLectures 10, 11 - Industrial example — Victoria Sugar Mill iterative designLectures 12, 13 - Cautious controller tuning, Controller certificationLecture 14 - Iterative versus Adaptive ControlLecture 15 - Observability in a stochastic contextLecture 16 - Adaptive control in action — TCP network congestion control
10
Modeling
mostly
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structureLecture 7 - Influencing model fit in closed loopLecture 8 - Designing controllers with approximate models, robust controlLecture 9 - Iterative approaches to modeling and control designLectures 10, 11 - Industrial example — Victoria Sugar Mill iterative designLectures 12, 13 - Cautious controller tuning, Controller certificationLecture 14 - Iterative versus Adaptive ControlLecture 15 - Observability in a stochastic contextLecture 16 - Adaptive control in action — TCP network congestion control
10
Modeling
mostly
Modeling
and control
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
Course OutlineLecture 1 - Big picture, outline. You are here ➷Lecture 2 - Modeling from data, some philosophyLecture 3 - Two examples of modeling for control; jet engine, helicopterLecture 4 - Prediction error modeling, a brief reviewLecture 5 - Approximation, bias formulæ in open and closed loopLecture 6 - Influencing model fit, experiment design, filtering, model structureLecture 7 - Influencing model fit in closed loopLecture 8 - Designing controllers with approximate models, robust controlLecture 9 - Iterative approaches to modeling and control designLectures 10, 11 - Industrial example — Victoria Sugar Mill iterative designLectures 12, 13 - Cautious controller tuning, Controller certificationLecture 14 - Iterative versus Adaptive ControlLecture 15 - Observability in a stochastic contextLecture 16 - Adaptive control in action — TCP network congestion control
10
Modeling
mostly
Modeling
and control
Adaptation
Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
A quick note
11Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
A quick note
11Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
A quick note
11Tuesday, April 3, 2012
MAE 283B, Lecture 1, Slide Approximate System Identification & Control
A quick noteThe presentation in this course will be mostly focused on scalar systems; SISO or single-input/single-output
This is because the prediction-error approach to MIMO systems is not really well set out
There are central parameterization issuesTools such as subspace methods work well but the tying together of modeling and control design is still needed
The extension of the methods of iterative modeling and control design to the MIMO framework would be quite worthwhile
802.11g ⇒ 802.11n
11Tuesday, April 3, 2012