functional modeling of control systems

Functional Modeling of Control systems

Morten Lind, Prof. Emeritus Automation and Control

DTU Electrical Engineering, Technical University of Denmark

Agenda

2 15 February 2014

• Status of Multilevel Flow Modeling • Control functions: some new challenges • Action theory and functional modeling • Preliminary findings


circulation of water

production of power

distribution of power

Multilevel Flow Modeling The basic idea

conversion of energy

Functions of systems and subsystems are described in relation to a context of use or purpose



production of oil and gas

distribution of gas

The idea apply to most engineering domains


Functions of systems and subsystems are described in relation to a context of use or purpose


Research in Multilevel Flow Modeling

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Action theory


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Functions are Context Dependent

supply of power

production of power



A function of the pump impeller in the context of water circulation

A function of the power plant in the context of power supply

A function of the pump in the context of power production


The Means-End Relation and Functions

The grains are transported and ground by rotation of the runner stone The runner stone is rotated by energy produced by the waterwheel The waterwheel produce energy by filling the buckets with water from the flume

by = ”by means of”

Functions of a Watermill

The concept of function is related to the Means-End relation


Motivations and Background

• Operator Decision support • Integrating process and automation

design with operation • Intelligent control (autonomy)

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Motivations

Concepts of means and ends, goals and functions play a significant role in human understanding of complex systems (and mundane reality)

– Support of operators decision making in diagnosis and counteraction planning

• Information presentation • Situation assessment and decision making • Knowledge representation and reasoning support

– Analysis of control and safety requirements for complex systems

– Integrated process and automation design – Intelligent Automation (agent systems)

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2014


Conceptual foundations

• Complexity and concept of function • The means-end relation

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Complex Industrial Systems and Infrastructures

From a Technology View To a Functional View


Two types of operational complexity

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The complexity of purposes of operation: The objective of a control agent is dependent on the overall operational goals and the objectives of other agents. Goals may change and be conflicting.

The complexity of dynamic nonlinear physical interactions: The contol agent must manage the dynamic interactions with the process and the other agents


What is a power plant?

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The natural framework The power plant is a natural object Modeling the power plant by the structural relations and the physical and chemical laws that govern its behaviour e.g. by differential equations

The social framework The power plant is a man made object - an artifact The power plant is purposeful i.e. Its functions are directed towards the satisfaction of human and societal needs. The purpose of automation systems is to ensure that design intentions are achieved

The framework for functional modeling (MFM)


Maintain water level withinsafe limits

Maintain conditionfor energy transport

Keep room temperaturewithin limits

Transport ofwater fromsupply to expansion tank

Circulationof water

Transport ofenergy fromboiler toradiator

Radiator

Pump

Water supply

Boiler Valve

Expansiontank

Maintain comfort

Com

pone

nts

Func

tions

Obj

ectiv

es

ENDS

MEANS

Goa

ls

Ends

Means

Purpose

Function

Behaviour

Structure

The Means-end relation and Functions


Multilevel Flow Modelling

• Basic principles • Control functions • Safety functions • Operating Modes

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Multilevel Flow Modeling

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• Process functions are represented by elementary flow and control functions interconnected to form function structures representing a particular goal oriented view of the system.

• MFM is founded on fundamental

concepts of action: Each of the elementary flow and control functions can be seen as instances of more generic action types.


ht

Vcfi

V0

fo

Function structure of a simple tank process

source sink storage

transport


MFM model of a watermill

The grains are transported and ground by rotation of the runner stone The runner stone is rotated by energy produced by the waterwheel The waterwheel is producing energy by the filling the buckets with water from the flume

Explanation:


The Watermill

mfs2

so3 bal2st2tr7 tr8 tr9 tr10st3

Filling the buckets with water from the flume


The Watermill

so2

mfs2

so3 bal2st2tr7 tr8 tr9 tr10st3

Producing energy filling the buckets with water from the flume


The Watermill

efs2

so2 tr4

mfs2

so3 bal2

st1

st2

tr5

tr6 si4

si3

tr7 tr8 tr9 tr10st3

Rotating the stone by energy produced by the waterwheel


The Watermill

tr1

efs2

so2 tr4

mfs2

so3 bal2

st1

st2

tr5

tr6 si4

si3

tr7 tr8 tr9 tr10st3

Transporting the grain by rotating the runner stone


mfs1

tr1

tr2

tr3

so1 bal1

si1

si2

efs2

so2 tr4

mfs2

so3 bal2

st1

st2

tr5

tr6 si4

si3

tr7 tr8 tr9 tr10st3

The Watermill The grains are transported and ground by rotation of the runner stone


ob1

mfs1

tr1

tr2

tr3

so1 bal1

si1

si2

efs2

so2 tr4

mfs2

so3 bal2

st1

st2

tr5

tr6 si4

si3

tr7 tr8 tr9 tr10st3

The Watermill


Another example: A heat transfer loop

MFM model without control functions


Control functions


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Cascade control A Control loop in MFM


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The heat transfer loop extended with controls

MFM model with flow regulator MFM model with flow and

temperature regulator


Cause - effect reasoning in MFM

Applications •Fault analysis •Alarm filtering •Rasoning about control •Counteraction planning …


Event propagation in MFM models

Event propagation within a flow structure

Event propagation across flow structures


The MFM Workbench: An integrated MFM model editor and reasoning platform

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Integrating process, control design and operation

Process design Control design

Ope

ratio

n


MFM Application Examples

• Nuclear Power • Oil and Gas

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MONJU Nuclear Power Plant

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Control System for MONJU Plant

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T

S F

n

B C

B C

F

T

＋－

＋－＋－＋－＋－＋－

＋－＋

－

＋

－

＋－

T P

F

B C

Δ P

B C

Power demand master

Reactor power

program

Reactor vessel outlet sodium temperature

program

PHTS flow

program

SHTS flow

program

Feed water flow

program

Reactor power

controller

CRDM controller

PHTS flow

controller

PHTS circulation

pump controller

SHTS flow

controller

SHTS Circulation

pump controller

EV outlet steam temperature

setting

Main steam pressure setting

Rector

Control rod

IHX

EV

SH

SHTS circulation pump

PHTS circulation pump

EV outlet steam temperature controller

Feed water flow

controller

SH outlet steam temperature

setting

Main steam temperature controller

Main steam pressure controller

turbine controller

Feed water control valve Feed water pump

Condenser

Steam control valve

Feed water control valve differential

pressure controller

Feed water control valve differential

pressure setting

Moisture separator

(average)


MFM of Monju Breeder Reactor

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Model developed with Prof. Yoshikawa (HEU) as part of Chinese 111 project on MFM based risk monitoring of NPP. Topics of special interest • Modeling safety barriers

and defense in depth. • Modeling operating

modes


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Water heating functions

Water circulation functions

Feed water pumping and control functions

MFM model of Monju of hot water warming mode


Mode transitions and MFM models

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?? ??


Gas Separation Plant

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MFM of three-phase separation process


Detailled MFM of separation function and reasoning results

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Sto3 lo (pressure is low)


Research challenges

• Modeling methodology and tools – ”From structure to function”

• Operating modes and transitions • Combining MFM with dynamic simulation models (DSM)

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Modeling the interaction between the process and control agents • Functions in Multilevel Flow Modeling are actions • Control agents act on the process Using Von Wrigth’s theory of action as a common basis for modeling process actions and control actions


Actions are defined by two situations (Von Wright)

Situation Explanation Illustration

Hypothetical state with no agent

The state of affairs changes from si to sh due to the dynamics of the environment

Actualized with one agent

The agent acts and the state of affairs changes from si to sA instead of sh

si

sh

A

si sA

sh


The elementary change types

Change schema Description

~pTp p happens

pTp p remains

pT~p p disappear

~pT~p p remains absent


The elementary interventions

Intervention

Change Action schema Description

~pTp (p happens)

~pT[pI~p] produce p

pTp (p remains)

pT[pI~p] maintain p

pT~p (p disappear)

pT[~pIp] destroy p

~pT~p (p remains absent)

~pT[~pIp] suppress p


Elementary actions and transitions

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Composite actions and transitions

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Control functions

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General Control function types: 1. Direct control (loops with setpoint control and disturbance rejection) 2. Start-up, shut down and transition between modes 3. Optimizing control

Direct control functions (type 1) are included in the current MFM function library Type 2 and 3 control functions should be included


There is a control action type (1) corresponding to each of the elementary interventions

Intervention Control action

produce steer

maintain regulate

destroy trip

suppress interlock


Relations between elementary action types and control functions(type 1)

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Advantages of the action types

• They comprise a complete set • They correspond to control action types known from control

engineering • The existing MFM functions have a logical foundation in the action

types • They are generic – the specific meaning depends on the state of affairs

p


Control of mode transitions

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Abstract states representing the modes


Elementary actions and transitions

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Example: translocation of objects Intervention Omission

Move-to(x,y)

The object y is moved to location x(from somewhere)

x

? y? let-move-to(x,y)

The object y move by itself tolocation x x

? y

Keep-at(x,y)

The object y is kept y at location x.x

y

?

let-stay-at(x,y)

The object y stay by itself atlocation x

xy

Move-from(x,y)

The object y y is moved away fromlocation x (to somewhere)

x

?

y

let-move-from(x,y)

The object y move by itself awayfrom location x. x

?

y

Keep-away-from(x,y)

The object y is kept-away-from(x,y)

x

? y

?

let-stay-away-from(x,y)

The object y stay away by itselffrom location x

x

? y

?


Composite actions and transitions

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The transfer relation

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Reasoning about control

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The control relation

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What does it mean that an agent (system 1) control an object (system 2)?

p is the desired state of system 2

q is the actual state of system 2


Promoting: transitions

p maintainp~pmaintain

~p

producep

produce~p

let phappen

let ~phappen

let ~premain

let premain


Opposing: transitions

p suppress~p~psuppress

p

destroy~p

destroyp

let ~pdisappear

let pdisappear

let premainabsent

let ~premainabsent


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Thank you for your attention

functional modeling of control systems

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