Smart adaptive systems in multivariable control and ...mmeafinalreport.fi/files/WP1.2 Keynote SIMS2011 Smart adaptive... · Smart adaptive systems in multivariable control and diagnostics

1

SIMS 2011Västerås 29 Sept 2011

Smart adaptive systemsin multivariable control and

diagnosticsEsko Juuso

Control Engineering Laboratory,Department of Process and Environmental Engineering

University of OuluFinland


Outline • Fuzzy logic + LEà Smart adaptive systems

• Data analysis– Generalised norms– Generalised moments

• Nonlinear scaling– Scaling functions– Constraints– Methodology based on skewness

• Applications– Condition and stress indices– Operating conditions– Modelling and control– Intelligent analysers

• Conclusions

2


What is essential in fuzzy logic?

• Membership functions• Meaning of the values• How to define them?

– Data– Expertise

• Automatic?• Recursive?

• Rules?– Expert systems?– Is there any

structure?– Is it just domain

expertise?– Equations?

Neural networks?


Fuzzy set systems à Linguistic equations

3


Fuzzy relational modelsData-based systems

Self-organisingTuning of rules

Linguistic fuzzy systemsExpertise

Trial and errorTuning of membership

functions

linear sets nonlinear setsMembership functions

simple

complicatedSetofrules

+Automatic, adaptive- Is it still understandable?

+fast start- tuningin practice?

AdaptationDecomposition

ClusteringStructured rulesLocal models

Increasingcomplexity


Features: norms• A generalised norm about the origin

which is the lp norm

• Special cases

– absolute mean

– rms value

sNN t=

,)1()( /1

1

)(/1 pN

i

p

ipp

p

p xN

MM å=

== aa

ta

t

.)(

pp

p xM aa

t º

,11

)()(

1

)( å=

==N

iiav x

Nxx aaa

,)1( 2/1

1

2)()(

2

)( å=

==N

iirms x

Nxx aaa

p is a real number

4


Features: norms• equal sized sub-blocksà Recursive analysis

• A maximum from several samples

• Increasing

[ ] ,)(1)(1/1

1

/1

1

/1pK

ii

p

S

pK

i

ppi

p

Sp

pKSS

S MK

MK

M úû

ùêë

é=

þýü

îíì

= åå==

at

at

at

{ }pi

p

Ki

p MMS

/1

,...,1)(max)max( a

ta

t

=º

qqpp MM /1/1 )()( at

at £ qp <

,1

1)(

1)(

å=

-= N

i ix

Nx

a

a 2/1

1

2)(

2

)( )1( å=

=N

iix

Nx aa,1

1

)(1

)( å=

=N

iix

Nx aa… …


LE: nonlinear scalingà linear models (interactions)

Data

Meaning

Expertise

5


Nonlinear scaling: constraints

- Monotonous- Incresing


Nonlinear scaling

Linear

Asymmetrical linear

6


Second order polynomialsTuning

(1) Core

(2) Ratios

(3) Support

• Centre point

• Corner points

• Calculation

}{ )max(,)(,)(),min( jjhjlj xccx

jc)](,)[( hjl cc

úûù

êëéÎ+ 3,

31

ja

)]max(),[min( jj xx

úûù

êëéÎ- 3,31

ja

+++

+++

---

---

D-=

D-=

D-=

D-=

jjj

jjj

jjj

jjj

cb

ca

cb

ca

)3(21

,)1(21

,)3(21

,)1(21

a

a

a

a

êêêêêêêêê

ë

é

£-

££---+-

££---+-

³

=---

+

+++

)min(2

)min(22

)(4

)max(22

)(4

)max(2

2

2

jj

jjjj

jjjjj

jjjj

jjjjj

jj

j

xxwith

cxxwitha

xcabb

xxcwitha

xcabb

xxwith

X


Dynamic simulator

Extension principle (& fuzzy arithmetic)

Fuzzy arithmetic

7


Variable time delay

Indices of variables

Values of variables


Generalised moments• Normalised moments

• Skewness– Positive– Symmetric– Negative

• Generalised moment

k = 3 Skewnessk = 4 Kurtosis( )[ ]

kX

k

kXEXE

sg

)(-=

03 >g

03 <g03 =g

( )k

X

k

p

p

k

MXE

sg

ata

úûù

êëé -

=

)(

Central value

8


Data mining and modelling

We can analyse datain various ways.

• Do we know wherewe are?

• Can we tell it in anunderstandable way?

• Can we use it?


Detecting operating conditions

9


lp Norms: cavitation

pp

p

p MM /1)( at

at =

Order of moment:p = 2.75 selected

Order of derivation:4 selected

Frequency range: as low as possible

Signal length = several sample times ß Phenomena

Sample time 3 s


Features: norms• a generalised norm about the origin

• Example: cavitation

– Relative

– Relative

– Relative

One featureà Cavitation index

sNN t=,)1()( /1

1

)(/1 pN

i

p

ipp

p

p xN

MM å=

== aa

ta

t

)max( 75.24

3 M

)max( 14

3 M

)max( 24

3 M

10


Nonlinear scaling


Cavitation index

1)4( -<CI

1)4( ³CI

01 )4( <£- CI

10 )4( <£ CI

Severity

Not acceptable

Still acceptable

Usable

Good

VDI 2056)max(( 75.2

431

4)4( MrelativefIC

-=

Improvedsensitivity

11


Signal processing- Derivation- Integration

Feature extraction- Norms- Histograms

Interpolation

NonlinearScaling

LE models

Signals

Process measurements


Laboratory analysis

Condition indicesStress indices

Condition indices

Stress indices

Process Cases & Faults

Cavitation in water turbines

Only one feature needed!


Lime kilns

Length > 100 mSlow rotation: rotation time 42-45 s

~ 4 m

12


Nonlinear scaling


Scaled norms

Impacts

LevelVDI 2056

Improvedsensitivity

13




Interpolation

NonlinearScaling

LE models

Signals



Laboratory analysis

Condition indices

Stress indices

Condition indices

Stress indices

Process Cases & Faults

Supporting rolls of a lime kiln

Several fault typesTwo features needed!


Condition and stress indicesMethodology

• Norms: a good order α + proper p and τ

• Nonlinear scaling– Scaling functions and constraints

– New methodology based on skewness

• Signal distributions

Applications• Cavitation

• One norm with optimised order

• Supporting rolls of a lime kiln– Two norms: level & impacts

Vibration severity criteria

)max(1

14

15 M

)max(25.4

25.44

15 M

)max(( 75.24

314

)4( MrelativefIC-=

14


Modelling and simulation

• Normal operationà model• Deviations• Anomalies• Case based reasoning (CBR)

à Detecting operating conditions


•1. case NS NS PS PS PS PS PS•2. case PS PS NS NS NS NS NS•3. & 4. case PB PB NS NS NS NS NS

•Fuzzy rules

Continuous brewing

15




Interpolation

NonlinearScaling

LE models

Signals



Laboratory analysis

Condition indices

Stress indices

Condition indices

Stress indices

Process Cases & FaultsQuality

Continuous brewing

Several operating conditionsNormal modelFluctuations


Web break sensitivity

16






Interpolation

NonlinearScaling

LE models

Signals



Laboratory analysis

Condition indices

Stress indices

Condition indices

Stress indices

Process Cases & FaultsEfficiency


Several operating conditionsCase Based Reasoning (CBR)

17


Trend Analysis in Diagnostics

Alarm

Very good

Warning

There was a problem, but things are now getting better?


Condition indexSeverity

Not acceptable

Still acceptable

Usable

Good

VDI 2056

Improvedsensitivity

1)1( -<CI

1)1( ³CI

01 )1( <£- CI

10 )1( <£ CI

18


Deviation index

( ).)()()(31)( kIkIkXkI T

jTjj

Dj D++=

Recursive updatesfor scaling functions


Modelling and simulation in Control

• Dynamic models• Time delays

• Control design• Model based control

– Feedforward– IMC– MPC– Switching– Special cases– …

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· Nonlinear· Start-up· Set point changes· Disturbances

· Irradiation· Malfunctioning

· No time for on-lineadaptation

· Nonlinear· Start-up· Set point changes· Disturbances

· Irradiation· Malfunctioning

· No time for on-lineadaptation

· Availability· Solar elevation· Clouds· Seasonal

differences· Demand

LE SimulationControl

Solar energy

The controller needs to be goodin the whole operating area!!(Oscillations –> slow opearation)

Solar collector field


Considerable differences between loops! Cloudsà braking

Temperature

20


Model-based tuning

Working point model

Operating conditions

Dynamic models

Distributed parameter models

Special caseswith fuzzy set systems

Can we makeall these models

consistentwith each other?


Multilevel LE control of a solar collector field

LE control Adaptation Braking

Prediction Cascadecontrol

AsymmetrySmooth, efficient operation

21


PI type LE controller

Working point

Linguistic values of- effective irradiation- temperature difference- ambient temperature

Nonlinear scaling of the error

Nonlinear scaling of the change of error

LE Controller: Adaptive Scaling

Cascade control

Smart actionsto avoid oscillations


Predictive braking action

Braking rate coefficient- initial error- braking constant

LE Controller: Adaptive ScalingAsymmetrical action

&Working pointcontrol

22


CascadeControl

(wp)

Too lowsetpoint fortemperature

Test results


Cascade controlreduces overshootefficiently.

Cascade control is notstrong enough toreduce overshoot

Inlet temperature changesconsiderably

Irradiation disturbances

23


Clear weather


Cloudy weather

Slightly lower temperatures

24


Power on a clear day

Fast start-up

Occational situations with very high working point


Power on a cloudy day

Occational situations with very working point

Several start-ups in coudy conditions

Slightly lower power

25


Energy collectionHigh efficiency in energy collection

High energycollectioneven on cloudydays


Linguistic values of- effective irradiation- temperature difference- ambient temperature

Intelligent analysers

• Working point

• Predictive braking coefficient

• Change of working point

26


Intelligent analysers• Fast change of inlet temperature

• Too fast increase of outlet temperature

• Too high temperature difference

àSmart actions


Smart adaptive systems

ControlDecision making

What is really controlled?

ControlDecision makingControl

Decision making

On-line modelling- identification

Performanceanalysis

Adaptationadaptation mechanisms,gain scheduling, scaling

Intelligent analyser(Software sensor)Intelligent analyser

(Software sensor)Intelligent analyser(Software sensor)

Process understandingàModelling à more efficient (new)measurements

MeasurementTechnology

High-level control & Diagnosticsweighting of stragies, switching,

cascade control,plant-wide control, expertise

27


Complex Models

• Interactive• Multimodel• Process phases with different models

• Biosystems• Nature


Activated sludge plantVariables

• Load– suspended solids (SS),– chemical oxygen demand (COD),– biological oxygen demand (BOD)– concentrations of nitrogen and phosphorus

• Additional nitrogen and phosphorus needed• Biomass population ???

– sludge volume index (SVI) or diluted sludge volumeindex (DSVI)

• Poor setling (bulking)– Lack of nutrients– Lack of oxygen

28


Cascade modelling

• PCA, Takagi-Sugeno, RBF, LVQ, nerofuzzy, LETS, ...• Process knowledge

X


Variables• Control

– sludge age,– COD/nutrient rate,– sludge loading,– recycle ratio

• treatment efficiency =reduction of– total nitrogen,– total phosphorus,– total COD

• Effective time delays– flow rates– kinetics

• Data pre-processing• Interpolation

29


Shortage of nutrientsToo much nutrients

High oxygenLow oxygen

High temperatureLow temperature

High flowLow flow

Very good

Low reduction

Settling problems

Very good

Warnings


Submodels

Water treatment

Fuzzy LE blocks

BioMass

Load

- Load- Nutrients- Oxygen- Temperature

Condition ofthe biomass

30


Multimodel system for water treatment

BioMass 1

BioMass 2

BioMass 3

BioMasspopulation

Weight factors are model parameters

e.g. very good, normal, problematic




Interpolation

NonlinearScaling

LE models

Signals



Laboratory analysis

Condition indices

Stress indices

Condition indices

Stress indices

Process Cases & FaultsEfficiency

Wastewater treatment

Several operating conditionsChanges with time

slow + fast

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Water treatment

• Compact LE models

• New scaling approach– Skewness & generalised norms– Improved sensitivityà warnings

• Variable time delays

• Detection of operating conditions– Early detection of changes à control actions

• Hybrid models are needed– Uncertainty (features of influent, microbial composition)– Mechanistic + Data-based + Intelligent

Multimodel system

Interactive models

LE models


32


Smart use of intelligent systems

IntelligentFunctions & features

(analysis,modelling, control,

diagnosis,…)

Methodologies(intelligent,

statistics, learning,optimisation,…)

Connections (OPC, agents, HLA, wireless, industrial ethernet, …)

Hybridsystems

Application-specific components and smart systemsà new functionalities


Conclusions

• Expertise• Data

• Fuzzy reasoning• Statistical analysis

• Generalised norms andmoments

Smart adaptive systems• Interactions

– Fuzzy set systems– Linguistic equations

• Meaning– Membership functions– Membership definitions

• Nonlinear scaling

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Smart adaptive systems in multivariable control and ...mmeafinalreport.fi/files/WP1.2 Keynote SIMS2011 Smart adaptive... · Smart adaptive systems in multivariable control and diagnostics