Knowledge Specification for Supervisory Control

°ght 0 IFAC ArtificiallnteIligence in Real-Time Control,

~=Lumpur, Malaysia, 1997

Knowledge specification for supervisory control Christophe Savignac and Benoit Bergeon Laboratoire d'Automatique et de Productique Universite Bordeaux I 351, Cours de la Liberation, 33405 TALENCE, France Tel : (33) 05 56 84 24 00, Fax: (33) 05 5684 66 44 E-mail: {savignac.bergeon}@)ap.u-bordeaux..fr

ABSTRACT

Copyright © 1998 IFAC

The proposed approach has been used in earlier projects : supervisory control of an electromechanical system [3, 10, 2] and computer-aided control system design [1]. This paper is organized as follows : a recall of our approach of the supervision is made in section two. The technical description and the mathematical model of the process to supervise is presented in section three. The fourth section deals with a method to model the necessary static knowledge for the supervision system. The last section treats with the description of the control laws of the 3 tanks system according to the three levels of abstraction.

Keywords : knowledge engineering, supervisory control, specifications, process description.

2 Problem statement

This article presents a method to design a cognitive model of the control laws of a plant. The modelisation is based on three given abstract levels : structural, behavioural and fimctional. The structural and fimctional levels are described by using SAGACE method. For the behavioural level, block diagrams are used. The SAGACE method and the block diagrams are modelised by using generic semantic networks. This method permits to represent the knowledge used to design a supervisory system.

1 Introduction Because of the complexity of the modern, industrial processes and of the evolution of the objectives and the oonstraints of production, the work of the human operator has become more and more difficult. New problems have appeared : they are linked with an overload of information and the risk of making mistakes. The need to help the operator in his work has become obvious. This has led to the development of tools to automate the tasks of supervision performed by human operators. Knowledge Based Systems (KBS) are usually used for supervisory cootrol applications due to the large amount of knowledge to handle, the complexity of the plants and the tasks to be pcrlonned. Moreover, the KBS allow to structure and clarify the desaiption of the process and the solving strategy and they also permit to reduce the cost of development and the maintenance. To model this knowledge, the method S2D2 (Static, Semantic and Dynamic Design) belonging to MOISE methodology [5] ~ch can produce a conceptual model of the future system ~ used.. This method modelizes separately the knowledge mto two components : the knowledge of the study domain (cooceptual domain model) and tho knowledge of the problem solving strategy (conceptual model of the problem SOlving strategy).

The problem of the specification of a supervisory system using KBS is not recent and many authors have worked on this problem [2,6, 7,9]. But in many cases, the description does not permit to take into account all the aspects of the process to supervise. In particular, the behaviour description generally includes only the discrete event part neglecting the continuous one. The control laws in continuous or discrete time of the process are not included in the knowledge base. Moreover, the description model and the problem solving strategy are not well separated. Our approach consists in dividing all the necessary knowledge to supervise the process into two distinguished parts [2] : - the knowledge of the different models of the plant, its different control laws and all the parameters necessary for the supervision of the plant. - the problem solving strategy (for control and for monitoring) including tasks such as: the changing of set points of the different regulations, controUers commutation algorithm, control algorithm, fault detection algorithm, diagnosis, ... These two parts form the conceptual model for the supervision system. So, this conceptual model is composed of different modules each of them using different methods and algorithms. This leads both to a problem of communication and a problem of introduction into a computer. To overcome those two difficulties, two steps have to be foreseen :

285

- first. to describe, in the same language, the different modules (method, algorithm) of the conceptual knowledge of the domain in order to obtain a unified frame in and between them owing to the semantic networks and the task trees; - secondly, to show the links permitting the communication between the different modules. In this paper, the aim of the study is to describe and define a part of the static knowledge (the control laws) necessary to the conception of the supervisory control system. This description is made by using some tools representing the three levels of abstraction of the static knowledge (structuraJ.. behavioural and functional model). SAGACE method is used to describe the structural and functional levels. Block diagrams are used to describe the behavioural level. SAGACE method and the block diagrams are modeled by using generic semantic networks which can be instantiated on every part of the process and its controllers.

where : h(t) : state vector formed with the level in each tank (hl(t),

h2(t), h3(t)), A: cross section ofeach tank in m 2 , (A=O.OlS4 m 2), Qij(h(t)) : flow rate between tank i and tank j :

Qii(h(t)) =

az;.Sn.~2g. lh;(t) -

hj(t)1

with: ~ : outflow coefficient, depending on the opening of the valve ij (zero if the valve is closed and one if it is open). For this configuration (h1>h3>h2), the value of the outflow coefficients are : azl=~=az3=1, Sn : section of connection pipe between the tanks and between the tanks and the reservoir in m', (Sn=S.lO-S m'), g : gravity constant in m/s>, (g=9.81 m'/s), hj(t) : water level in the tank i in m.

3 Technical description of the process to be supervised 3.1 Technical configuration The experimental study is based on a pilot three-tank system. The plant consists of three cylinders with cross section A. They are serially connected to each other by cylindrical pipes with a cross section Sn whose flow rates are modulated by manually adjustable ball valves. The three water levels hi' h2 and h3 are measured via piezoresistive pressure sensors. The pwnps are tmidirectional and they are directly controlled by their flow.

3.3

Linearisation of the process

As the plant is nonlinear, a linearised model rotmd an operating point is used to design a control law. In order to cover the set of the functioning domain of the process, several control laws round different operating points have to be calculated. So the supervisory system has to use a bank of models and the control law associated to each model and has to manage the changing of model and regulation according to the operating point.

OowQ2

fIowQI

1IDk 1

4 Modelization tools

1IDk3

4.1

Semantic networks

All the domain knowledge is modeled in terms of concepts and objects. Both concepts and objects are described within the semantic network formalism. The following reduced set oflinks is used in the proposed networks [2] :

Fig. 3.1 Physical structure o/the 3 tanks system

3.2

Mathematical model

For this MIMO system, a detailed mathematical model describing its dynamic behaviour can be developed. Using Torricelli's rule, the three levels ht, h2 and h3 are governed for hl>h3>h2 by the following nonlinear representation :

286

- AKO (A-Kind-Ot): the classical specialisationinheritance link between concepts, - ATO (ATtribut-ot): declaration of concept properties; minimal and maximal cardinalities are provided for this link, - VAL-IN (VALued-IN): set of possible values of an attribute, - ISA: value taken by the attribute of an instantiated object.

4.2

SAGACE method [8]

Semantic network representation :

PresentatiOD of the method

~

SAGACE is an industrial modeling method for complex systems. It is used to obtain a hierarchical and descending description of a process. With this method, it is possible to obtain different levels for the model. The two main concepts of the method are the processor and the flow. Each processor can be considered as processing entering flows and emeting, after being treated. leaving flows.

t

- fimction : - type (transport, stocking, transformation), - form of the type (matter, energy, information) - name(s) of entering flow(s), - name(s) ofleaving flow(s). - nature of the processor (system, sub-system, component) A flow is characterised by : - form (matter, energy or information (reference, command or mesure», - name of source processor, - name of destination processor.

~ .......... --,.

VAL-IN VAL-IN

)

~ )

(T. E. F. s.

PI

(M,E,II

(~......,.-.

CCIDIpCDIIlt I

)

FLOW

)

FLOW

Fig. 4.1 Network "processor"

~

A processor is considered as a dynamic black box, whose entrances and leavings are identified each time. A processor can be represented by a subtil arrangement of elementary processors which act on the stocking, the transport and the transformation of processed objects. There are two types of particular processors : the source processor (without identifiable entering) and the well processor (without identifiable leaving).

A processor is characterised by :

- ....

.....

VAL-IN

VAL-IN

PROCEISOR(I)

There are three kinds of flows : matter, energy and information (subdivided into subtypes : IC for the reference information, lA for the command and IR for regulated variable).

Cognitive model

~

)

fl.l]

PROCESSOR

VAL-IN

'-:tioa~type

FLOW

T

[I,w]

o ~"

~

furm SOIa'CC

p-ocessor

•

VAL-IN VAL-IN

~

~

destmtion VAL-IN ~ p-ocessor )

{M, E, le, IA,!R}

PROCESSOR U {exterior} PROCESSORU{extc:rior)

Fig. 4.2 Network ''flow''

4.3 block diagrams This study shows only SISO or MIMO linear processes. To represent the behavioural level of such processes, the state space representation is used :

X(t) = Ax (t) + Bu(t). { y(t) = Cx(t) + Du(t) So, it is necessary to know only four elementary classes of objects to describe the behaviour of a continuous or discrete time process : the integrator or the delay, the adder, the gain matrix and the signal. The first three ones have common characteristics: each of them has an input and an output signal, only the intrinsic characteristics are different. So it is possible to gather these three objects into one which is called operator. This operator has some proper characteristics :

Owing to semantic networks, two classes of objects having - type (integrator, adder, gain matrix), those characteristics can be created. In filet, these latters - name(s) of input signal(s), are the attributes of the classes of considered objects and - name of output signal, can be represented by using the link ATO. - value of the sampling period, Besides, SAGACE being a hierarchical and descending value of the coefficient(s) of the gain matrix. method, a processor can be divided into several subprocessors with an inferior level. The link AKO is able to . A signal is characterised by : show this network of the class of the process object. The value of an attribute of an object belongs to either - name of source operator, concept (concepts are written capital letters), a pre-defined - name of destination operator, type (real, integer, ... ) or a type explicitly defined by its - type (reference, error, command, output, state, elements. measurement, disturbance), - dimension.

287

The dimension attribute is used to give the number of the signal components. The sampling period is only used if it is a discrete time representation (zero for continuous time systems).

For instance, at the level one : Iqrc&nac:e)

As for SAGACE, these characteristics are the attributes of the operator object and thus can be represented in using the link ATO. The same is done for the signal object and two semantic networks are obtained to describe the static knowledge of the behavioural level of the process:

~

OPERATOR

~

~

type

--...paiod

VAL-IN

~{-.-."""piII}

_of,noI._. . .

VAL-IN

~

-? ~

VAL-IN

-

iopIl oipoI

~

""""' .....

VAL-IN)

VAL-IN

~

VAL-IN

~_of_

~

VAL-IN

~

Leaving ftowt

..... iaIqer

MorE

liIIoC rcoI

SIGNAL SIGNAL

Fig. 5,1 Levell

Fig. 4.3 Network "operator"

~ I

type

lreference, error, oormmd, _e

A block diagram representing its dynamic behaviour between its input(s) and its output(s) can be associated with each processor. The linearised process and the regulation can be represented owing to the state space representation :

outpot, .................. cIioIurbmce}

SIGNAL ~ IOURleOperllor

~ desIiDoIianoponlor ~

VAL-IN)

dinBIIioa

VAL-IN )

OPI!RATORlJleotterior}

VAL-IN)

OPERATORU I-"r}

VAL-IN)

.......

u(t)

Fig. 4.4 Network ''signal''

5

y(t)

Conceptual model Fig. 5.2 Block diagram ofa state space representation

5.1 Domain model (static knowledge) The process model is based on a triple perception of the latter which corresponds to three given abstract levels: structural, behavioural and functional [2]. The describing method which is used for the structural and functional levels is SAGACE method. For the behavioural level, block diagrams which constitute the classical mode of representation in control theory are used. In the paper, only the behavioural level is treated.

This block diagram can be described by using five operators (three gain matrix , an adder and an integrator) and six signals (u(t), u1(t), i(t), x(t), y(t) and r(t». Example:

~

NoIintC

Owing to those modeling methods, all the control laws of the process can also be desciibed. In &et, with SAGACE, the structural and functional point of view of the control law are represented and with the block diagrams, the behavioural level is shown. - level zero corresponds to the global control law; - level one is formed with the structure of the control law; - level two is formed, for example, with the detail of the processor process with its actuator and its sensor.

288

type

--. paiod

IS-A

~

pill

IS-A

~

0

_oCrows

~

~ ........ pill . . . .

.... oipoI

-

IS-A

~ -? ~_oC_~

~

....... ~

~

3 3 {I,O,O; O,I,O;O,o,l}

1(1)

~

)'(1)

Fig. 5.3 Example of instantiation ofthe flow network

5.2 Problem knowledge)

solving

model

(dynamic

So, with the task manager, the dynamic knowlegde can handle the object of the static knowledge : by reading the value of an attribute or modifying the instantiation of the attribute of an object.

Task aDd task tree The problem solving strategy desaibes the strategy used to generate the operating procedure, i.e. sequence of elementary actions to be performed in a certain context. and which enables the plant to reach a final steady state defined by operators. The specification of the problem solving strategy is based on the concept of task [4]. The model emphasizes a functional top-down decomposition where each task is desaibed by : - task identifier - input and output parameters : entities from the domain model, - conditions : a set of conditions which must be validated to perform the task, - type : a task can be either compound (desaibed by its sub-tasks) or elementary (involving only one primitive procedure). The control flow (sequence, iteration, decision, ...) appears in the decomposition of a compound task. - body : either a primitive procedure or a set of sub-tasks according to the task type. The problem solving strategy is provided with the graphical task language of S2D2 [5]. This language is based on a set of generic tasks (periodical task, scheduling task, repetitive task) which permits to desaibe the strategy for the control and the monitoring [3].

The problem solving strategy for the CODtroi The method used for the control is based on a state approach. It consists in finding a way in a phase graph. This phase graph is composed of nodes and arcs : - nodes correspond to the different operating points of the process with their own characteristics (model corresponding to the operating point, controller, validity area of the model). These nodes are represented in the static knowledge by the class of object "situation". - arcs correspond to all the different possible transition between the different nodes. These arcs are represented in the static knowledge by the class of object "phase". A third class of object is needed for the control, namely the object "action" which defines an effective action on the process (open or close the valves, set the reference of the regulation loop, change the controller, ...). The problem solving strategy is constituted by two main

tasks : - generate an operating phase plan from an initial situation to a final one (situation determined by an operator) by using a backward chaining algorithm. - execute the operating phase plan , i.e. to execute the different phases permitting to pass from a situation to another, until the final situation is reached. This strategy is used to control a power plant [2]. COIltrol plant

ooilenoplm g~ ~

In order to interpret the resulting knowlegde base (static and dynamic), two control components are needed :

t

c:xearte opc:rating phase plan

generate operating phase plan

- a generic task manager which executes the task

~

based on its status, type, .... - a semantic network manager which provides the inheritance and inference mechanism for the static knowledge.

request of initial and generate operating final situations

pbases

Fig 5.5 Partial task tree for "control plant" tasks manager

semantic networks manager

6

I

CONlROL

I

I

,,-

static knowledge (semantic networks)

dynamic knowlegde (tasks)

I DESCRIPTION I Fig 5.4 The S2D2 architecture

Conclusions and prospects

Thanks to the four object classes: processor, flow, operator and signal, any linear process can be described in the structural, behavioural and functional model. So it is possible to obtain a representation of the control law (or several control laws) and of the observer of the process by using generic semantic networks and to include this representation in a knowledge based system. With the objects action, phase and situation, the process can be controlled by a state approach (planning). This approach permits to pass from a initial situation to a final one defined by a human operator. The supervisory system

289

is thus able to determine the succession of elementary actions to reach the final situation [2].

[9]

In our process, the solving problem strategy must take into account the changing of models, regulation and observer. These changings will be handled by tasks which will set off algorithms thus allowing the optimal commutation of the regulation. Another advantage of the state approach is to react in front of a possible fiillure of the process. In &et, if a mult occurs in the process, the planning can regenerate a new plan from the current situation taking the mult into account (degraded mode of functioning).

[10] A. Zolghadri, B Bergeon. Z. Benzian, J.L. Ermine and M. Monsion (1993). Fault diagnosis and supervision of a cutting tool robot. EurQPe8Il Journal of Diagnosis ans Safety in Automation. pp 151-174.

References [1]

B. Alkhatib, B. Bergeon, C.M. Falinower and J.L. Ermine and M. Monsion (1993). A second generation expert system for control design. In Prmrints of the 121!! IFAC World Congress, Vot. 4, pp 375-379, Sydney, Australia.

[2]

Z. Benzian, B. Bergeon, J.L. Ermine and C.M. Falinower (1995). Knowledge engineering for a power plants operations support system. Proc. of IFAC Large Scale Systems, Vot. 1, pp 627-632, London, United Kingdom.

[3]

B. Bergeon, A. Zolghadri, Z. Benzian, J.L. Ermine and M.Monsion (1993). Specification of a real-time knowledge based supervision system. In Prmrints of th 12th IFAC World Congress, Vot. 1, pp 113-118, Sydney, Australia.

[4]

B.Chandrasekaran, T. R Johnson, J. W. Smith (1992). Task-structure analysis for knowledge modeling, Comm. of the ACM. 35(9), pp 124-137.

[5]

J.L. Ermine (1993). Genie Logiciel et Genie Cognitif pour les Systemes a Base de Connaissances. Collection Tee et Doe, Lavoisier, Paris.

[6]

J.L. Ermine, M. Chaillot, P. Bigeon, B. Charreton, D. Malavieille (1996). MKSM, methode pour la gestion des connaissances. in Ingenierie des systemes d'information. Eds Hermes ~lln (1997). SIllFT: A New Programming Language for Simulation. Proc. SPIE's 11 III AeroSense' 97, Orlando, Florida.

(7) A.

[8]

D. Sinclair (1997). Using Object-oriented Methodology to Bring a Hybrid System from Initial Concept to Formal Definition. Proc. of the International Workshq> HART 97, Grenoble, France.

J.M. Penalva (1990). SAGACE : one representation des connaissances pour la supervision des procedes continus. Proc. loemes Journees 1nl. Les Systemes experts et leurs glications. vot. 2, pp.41-52, Avignon.

290