A FRAMEWORK FOR DISTRIBUTED AND INTELLIGENT

PROCESS CONTROL

Qurban A. Memon

UAE University, United Arab Emirates

Keywords: Distributed network control, Process Control, Intelligent agents.

Abstract: The customized development of the Distributed Control System for process control in an environment of

intelligent and tagged field devices etc., is the main focus of this work. The proposed solution consists of

two-layer approach: use of decentralized intelligent agents at the local process level, and four-tier modular

architecture at central controller level to help implement distributed intelligence. The design and

development issues for such a customized design are investigated.

1 INTRODUCTION

The decentralization of control, and expanding

physical setups have resulted into today's distributed

process control (DCS) systems (M. Ioannides, 2004,

J. Alonso, 2000). The research in this area is quite

active because of these developments, for example,

Profibus fieldbus networks and wireless Profibus for

real time industrial control systems (E. Tovar, 1999,

A. Willing, 2003). Recently focus is reported with

respect to distributed intelligence for reduced

operational changes. These efforts have respective

generated agent based approach (Bernan, 2002, F.

Maturna, 2005). Currently, active Radio Frequency

Identification (RFID) devices are being deployed for

a variety of process control industry solutions (A.

Juels, 2003, J. Bohn, 2004). To some industries,

RFID is bringing a level of automation and control

similar to what process control devices brought to

manufacturing decades ago. I. Satoh, 2004,

presented a framework which exploits agents to

enhance capabilities of the users in an environment

of tagged devices. In another work (S. Naby, 2006),

the author discusses idea of integrating software

agents into RFID architectures to accumulate

information from tags and process them for

customer/object or system specific use, for example

a concurrent mission. As a summary, the

optimization in network performance combined with

distributed intelligence in an environment of non-

stationary and reconfigurable devices provides a

new direction of research.

The process under investigation is shown in Figure

1, which shows reprogrammable and reconfigurable

control devices including some tagged and

distributed field intelligent devices. In short, the

challenges for DCS development include process

reconfigurability and intelligent decision making

within a Profibus/Profinet compatible network. For

comparison, a model is to be used to set a baseline

for performance. As the network is distributed,

hence a multiple input multiple output (MIMO)

baseline is considered that requires performance

matching to that of the centralized MIMO. For

Figure 1 to achieve similar performance to that of

the centralized MIMO, each parameter update needs

to be communicated over the network at times. In

order to categorize time delay d(t) in the network,

we divide it into three categories:

)

)( )( )( )( 321 tdtdtdtd +

where d

(t), d

(t), and d

(t) represent time delay

when a device communicates with controller, time

delay when a group of devices communicate with

controller; and when all devices communicate with

controller respectively. One obvious approach could

be to minimize either of these delays so as to

optimize the performance to match a centralized

MIMO system.

2 PROPOSED APPROACH

A set of processes is proposed to introduce

intelligence at field level to gain respective

240

A. Memon Q. (2008).

A FRAMEWORK FOR DISTRIBUTED AND INTELLIGENT PROCESS CONTROL.

In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - SPSMC, pages 240-243

DOI: 10.5220/0001480702400243

 SciTePress

independence. This way, minimized communication

with the main controller thwarts communication

bottlenecks caused by interoperability of devices, or

simple operational requirements at local level. This

also improves survivability of the local processes in

cases when central controller fails in providing

critical timely decision. The configurability of

devices may be provided by collecting operational

parameters at the device(s) level followed by

estimation of parameters of concerned entities at the

central level. This leads to two separate domains:

2.1 Local Process

The local entities tend to be distributed throughout

the environment to support overall operations. The

job of these entities can be done effectively by

agents. The agents collaborate, learn and adjust their

abilities within the constraints of the global process.

Agent design mechanism: A lot of work is done on

agents alone and details can be found in (R.

Brennan, 2001) Agents are active software entities

that can request for additional capabilities once they

discover that the task at hand can not be fulfilled.

The programming of these agents is done at the

central level where a set of heuristics is used for

reasoning at the local level, and is stored as a

function block diagram (like an internal script). The

agents know about their equipment, continuously

monitor its state, and can decide whether to

participate in a mission or not. The collaborating

agents join (on their own will) and thus form a

cluster in order to enable a decision making. In

addition to agents, there are other computing units

that exist at the local level and help to form a cluster.

These are known as cluster directory (CD) and

cluster facilitator (CF) respectively. The following

steps describe agent collaboration in clusters:

• Agent N receives a request from central process.

• It checks its scripts, and solves local steps.

• For external steps, it receives contact details of

other agents with external capability from CD.

• Agent N creates CF

and passes on these details.

• CF

passes the request to specified agents, and

thus cluster is formed.

For efficient collaboration, CD must remain updated

for recording the information of its members, such

as agent name, agent locator, service name, service

type, and so on. Upon joining or leaving the cluster,

an agent must register or cancel registration

respectively through CF. Through a query, an agent

can find out other members’ services and locators.

Through these steps, a trust is developed and thus

members hold higher authority than non-members.

Recently developed tools may be used to help

design cluster facilitator (CF) and domain ontology,

using for example DARPA Markup Language

(DARPA, DAML, 2000). The DAML extends XML

(Extensible Markup Language) and RDF (Resource

Description Framework) to include domain

ontology. It provides rich set of constructs to create

ontology and to markup information for attaining

machine readability and understandability.

Furthermore, the Foundation for Intelligent Physical

Agent (FIPA, 2003) Agent Management

Specification is extended to develop the agent role

called CF to manage cluster directory (CD) and

cluster ontology. Using assistance from DAML-

based ontology, the members of the cluster are able

to form cluster and communicate with other agents.

(Motion control through PLC)Controller

Profibus-enabled

device

Profibus-enabled

device

Profibus-enabled

device

Distributed field device

Distributed tagged devices

Interface

Drive

PC (for SCADA)

Distributed intelligent devices

Figure 1: Typical Process Control Network.

Domain

Ontology

Cluster

Directory

Cluster Ontology

}

DAML File(s)

File access

Figure 2: Linking CF with DAML.

The interaction among domain ontology, CD and CF

can be best understood using Figure 2. Figure 2

shows how CF gets access to DAML files and

facilitates the common goal of the cluster. There are

tools available like Jena semantic web that can be

used to handle the cluster director (CD) built using

DAML, and to develop a Java class “Directory”.

Thus, main functions of CD can be summarized, as:

• Add and Remove the information of an agent

• Get the list of agent names of all members

• Get the information of individual agent by name

A FRAMEWORK FOR DISTRIBUTED AND INTELLIGENT PROCESS CONTROL

241

• Get ontology used by members in the cluster

• Add external ontology if provided by an agent

Using local process mechanism and main functions of

CD, the partial directory can be described as shown in

Figure 3. It shows information of CF (lines 1-9) and

members of cluster (lines 20-22), the cluster directory

also records meta-data about cluster such as cluster

name (line 12), cluster description (lines 13-15),

ontology used in cluster (lines 16-18), etc.

An example can be illustrated to show how

ontology may be updated (Fig. 4(b)) and that how

interactions may develop in a local process. It should

be noted here that basic cluster ontology provided by

CF remains the same but all members’ domain

knowledge (ontology) may not be the same. For

example, user agent holds basic knowledge of the

local process but does not understand the knowledge

that a distributed field device holds. Through DAML-

based ontology, members can communicate with each

other to acquire requested service, as shown in Figure

4. It is clear from Figure 4 that when distributed field

device agent joins the cluster, it informs CF about

corresponding ontology it provides (Figure 4(a)).

Thus the CF maintains local process ontology plus the

distributed field device ontology. When a user agent

wants to perform a task, it asks CF about domain

ontology and the agents that provide external

capability. In response, CF informs the user agent if

ontology is to be acquired (Figure 4(c)). Thus, the

user agent can communicate with the distributed field

device agent (Figure 4(d)).

2.2 Central Process

This process embodies core, like definition of

controller tasks, and definition of domain ontology

of each cluster. The other components are removal

of agent deadlocks, estimation of local

characteristics and decision making in cases when

situation develops beyond the capabilities of agent

clusters. It can be argued that if only small scale

changes are to be decided at the central level like

reconfiguration of device processes then intelligence

can further be distributed to the agents at local level.

In Figure 5, the model architecture of four tiers is

shown to implement objectives of the central

system. At the bottom layer (Tier 1), active readers

or Profibus/Profinet enabled devices collect data,

often collected on a trigger similar to a motion

sensor. These readers should be controlled by one

and only one edge server to avoid problems related

to network partitioning. This layer also provides

hardware abstraction for various Profibus/Profinet

compatible hardware and network drivers for

interoperability of devices. The edge sever (Tier 2)

regularly poll the readers for any update from device

agents, monitors tagged devices and distributed

devices through readers, performs device

management, and updates integration layer. This layer

may also work with system through controls and open

source frameworks that provide abstraction and

design layer. The integration layer (Tier 3) provides

design and engineering of various objects needed for

central controller as well as for field processes and for

simulation levels of reconfigurability. This layer is

close to business application layer (Tier 4). The

monitoring of agents behavior, its parameters and

cluster characteristics are done at this layer to assess

the degree of reconfigurability.

1. <cluster:CF rdf:ID="theCF">

2. <cluster:agentName>"CF"</cluster:agentName>

3. <cluster:agentDescription>

4. "DCS Cluster Facilitator"

5. </cluster:agentDescription>

6. <cluster:locator>

7. "http://dcs.ee.uaeu.ac.ae/DCS/agent/CF"

8. </cluster:locator>

9. </cluster:CF>

10.

11. <cluster:Cluster rdf:ID="DCSCluster">

12.<cluster:clusterName>"DCS"</cluster:clusterName>

13. <cluster:clusterDescription>

14. "Distributed Control System"

15. </cluster:clusterDescription>

16. <cluster ontology>

17. "http://dcs.ee.uaeu.ac.ae/DCS/ontology/dcs.daml"

18. </cluster:ontology>

19.

20. <cluster:hasCF rdf:Resource="#theCF"/>

21. <cluster:consistOf rdf:Resource="#agent1"/>

22. <cluster:consistOf rdf:Resource="#agent2"/>

23. </cluster:Cluster>

Figure 3: DCS Cluster Directory.

DFD agent

DCS ontology

DFD ontology

User

agent

..........> File access

-------> Agent Communication

DFD : Distributed Field Device

Figure 4: Ontology update provided by DFD.

This layer also takes care of parameters like

handling device processes, resource allocation and

scheduling of processes. The separation of edge

server and integration layer improves scalability and

reduces cost for operational management, as the

edge is lighter and less expensive. The processing at

the edge reduces data traffic to central point.

Similarly, the separation of integration from

business applications helps in abstraction of process

entities.

ICINCO 2008 - International Conference on Informatics in Control, Automation and Robotics

242

Distributed

Tagged/Intelligent

devices

Reader 1 or

Profibus/Profinet enabled

device

Reader 3 or

Profibus/Profinet enabled

device

Reader 2 or

Profibus/Profinet enabled

device

Edge Server

Integration layer

1 65432

Tier 1:

Devices with

overlapping

fields

Tier 3:

Integration

Tier 2:

Edge Server

Tier 4:

Packaged

Applications

Figure 5: 4-Tier Reference Architecture.

The Tier 3 also enables it as self-healing and self-

provisioning service architecture to increase

availability and reduce support cost. Control

messages flow into the system through business

application portal to the integration layer, then on to

the edge and, eventually, to the reader. Provisioning

and configuration is done down this chain, while

reader data is filtered and propagated up the chain.

3 CONCLUSIONS

The main idea behind two processes is

decentralization. The communication delay is

reduced at the cost of increased intelligence at the

local level. In fact, if we look at equation (1) we see

that d

(t), d

(t) and d

(t) minimize to a level when

problem of the node device exceeds the threshold

level of the agent intelligence. If collaborative

intelligence exceeds combinatorial complexity then

there is no need of communication between devices

and the controller and requirements of the central

process reduce to that of the design of agents only.

Thus, the performance matches to that of the

centralized MIMO system. The four-tier modular

architecture at central level helps in implementation

of distributed intelligence at field level and in

designing of agents. The functionality more

appropriate to the layer has been fit into respective

tiers at central level. Additionally, design and

reconfigurability can help introduce features in

agents to thwart intrusive agents, during real time.

This set of gains has not been claimed in either of

the approaches (E. Tovar, 1999, A. Willing, 2003).

ACKNOWLEDGEMENTS

This work was financially supported by UAE

University under a grant no. 01-04-7-11/07.

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