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

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

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

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 pN

MM å=

p xM aa

)( å=

iiav x

Nxx aaa

,)1( 2/1

)( å=

iirms x

Nxx aaa

p is a real number

Features: norms• equal sized sub-blocksà Recursive analysis

• A maximum from several samples

• Increasing

[ ] ,)(1)(1/1

M úû

ùêë

þýü

îíì

= åå==

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

qqpp MM /1/1 )()( at

at £ qp <

)( )1( å=

Nx aa,1

)( å=

Nx aa… …

LE: nonlinear scalingà linear models (interactions)

Meaning

Expertise

Nonlinear scaling: constraints

- Monotonous- Incresing

Nonlinear scaling

Linear

Asymmetrical linear

Second order polynomialsTuning

(1) Core

(2) Ratios

(3) Support

• Centre point

• Corner points

• Calculation

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

jc)](,)[( hjl cc

úûù

êëéÎ+ 3,

)]max(),[min( jj xx

úûù

êëéÎ- 3,31

,)1(21

,)3(21

,)1(21

êêêêêêêêê

££---+-

)min(2

)min(22

)max(22

)max(2

xxwith

cxxwitha

xxcwitha

xxwith

Dynamic simulator

Extension principle (& fuzzy arithmetic)

Fuzzy arithmetic

Variable time delay

Indices of variables

Values of variables

Generalised moments• Normalised moments

• Skewness– Positive– Symmetric– Negative

• Generalised moment

k = 3 Skewnessk = 4 Kurtosis( )[ ]

03 <g03 =g

úûù

êëé -

Central value

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

lp Norms: cavitation

p MM /1)( 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

One featureà Cavitation index

sNN t=,)1()( /1

)(/1 pN

MM å=

)max( 75.24

)max( 14

)max( 24

Nonlinear scaling

Cavitation index

1)4( -<CI

1)4( ³CI

01 )4( <£- CI

10 )4( <£ CI

Severity

Not acceptable

Still acceptable

Usable

VDI 2056)max(( 75.2

4)4( MrelativefIC

Improvedsensitivity

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

Nonlinear scaling

Scaled norms

Impacts

LevelVDI 2056

Improvedsensitivity

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

)max(25.4

)max(( 75.24

)4( MrelativefIC-=

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

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

Interpolation

NonlinearScaling

LE models

Signals

Laboratory analysis

Condition indices

Stress indices

Condition indices

Stress indices

Process Cases & FaultsEfficiency

Several operating conditionsCase Based Reasoning (CBR)

Trend Analysis in Diagnostics

Very good

Warning

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

Condition indexSeverity

Not acceptable

Still acceptable

Usable

VDI 2056

Improvedsensitivity

1)1( -<CI

1)1( ³CI

01 )1( <£- CI

10 )1( <£ CI

Deviation index

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

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– …

· 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

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

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

CascadeControl

Too lowsetpoint fortemperature

Test results

Cascade controlreduces overshootefficiently.

Cascade control is notstrong enough toreduce overshoot

Inlet temperature changesconsiderably

Irradiation disturbances

Clear weather

Cloudy weather

Slightly lower temperatures

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

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

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

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

Cascade modelling

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

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

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- Nutrients- Oxygen- Temperature

Condition ofthe biomass

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

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

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

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

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