Towards Seamless Digitization in OPC UA
V
´
aclav Jirkovsk
´
y
1 a
, Petr Kadera
1 b
, Marek Obitko
2 c
and Ond
ˇ
rej Flek
2
1
Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
2
Rockwell Automation R&D Center, Prague, Czech Republic
Keywords:
Motion Control, Drive, OPC UA, Ontology, SQWRL, Validation.
Abstract:
The advent of Industry 4.0 has brought about the need for seamless communication and integration among
diverse industrial automation systems. While current standards such as OPC UA address syntactic interoper-
ability, they fall short in addressing semantic heterogeneity, which poses a challenge to the true understanding
of exchanged data. The existing approaches, which rely on textual information models such as the OPC UA
companion specifications, suffer from possible ambiguity and difficulties in development and validation. This
research aims to overcome these challenges by exploring the development of an explicit, user-friendly, and
machine-readable model specification for the conditional implementation of Rockwell Automation drives’
motion axis attributes within OPC UA. The paper delves into suitable formalisms for model specification, tak-
ing into account user interaction and expressivity, and proposes mechanisms for model validation and future
extensions. These efforts pave the way for enhanced semantic interoperability in industrial automation, thus
contributing to the advancement of Industry 4.0.
1 INTRODUCTION
The domain of industrial automation has experienced
significant changes and paradigm shifts over time. Al-
though some changes were introduced quietly, others
represent revolutions in the field, such as the inven-
tion and implementation of the initial programmable
logic controller (PLC) in the 1970s (David, 1995).
On the other hand, Industry 4.0 (Dalenogare et al.,
2018) is not an ex post named industrial revolution,
but an initiative planned in advance. The fourth in-
dustrial revolution (Industry 4.0) represents the inte-
gration of vertical and horizontal manufacturing pro-
cesses and product connectivity, which can help com-
panies achieve higher industrial performance.
The integration of all manufacturing processes
and resources is made possible by increasing digiti-
sation at all levels. However, devices and systems
are produced by different companies with varying
data models. Therefore, standardisation is necessary
to reduce differences in syntax and meaning, ensur-
ing easy and seamless integration and communica-
tion (Neumann, 2007).
Fortunately, there are several verified standards
a
https://orcid.org/0000-0002-1744-0753
b
https://orcid.org/0000-0002-1747-6473
c
https://orcid.org/0000-0001-7639-3312
(Profinet, CIP, etc. (Lin et al., 2013)) within the indus-
trial automation domain for robust communication.
One of the mature standards is the OPC UA (Leitner
and Mahnke, 2006), which also provides advanced
functions such as subscriptions, function calls, and
an object-orientated approach for information mod-
elling. However, these standards are capable of deal-
ing only with syntactic heterogeneity but not with
semantic heterogeneity. That is, while such stan-
dards provide means of ensuring messages can be ex-
changed between devices, there are no means of en-
suring that the message content is understood with the
highest confidence.
Awareness exists of the lack of appropriate tools
and approaches to deal with semantic heterogeneity.
Thus, various strategies have been employed depend-
ing on the communication standard and OEM. How-
ever, most are based on specifications of information
models in natural language in textual form. An exam-
ple of the aforementioned approach is the OPC UA
Companion Specifications
1
. The companion specifi-
cations are developed for two main reasons:
• To publish domain-specific information models.
• Standardise the use of OPC UA in specific envi-
ronments.
1
https://opcfoundation.org/about/opc-technologies/
opc-ua/ua-companion-specifications/
326
Jirkovský, V., Kadera, P., Obitko, M. and Flek, O.
Towards Seamless Digitization in OPC UA.
DOI: 10.5220/0012947800003822
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics (ICINCO 2024) - Volume 1, pages 326-333
ISBN: 978-989-758-717-7; ISSN: 2184-2809
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

The companion specifications for the OPC UA are of-
ten called “Industry standard models” and should en-
able interoperability at the semantic level.
Unfortunately, the aforementioned approaches to-
gether with the “Industry standard model” have an im-
portant imperfection. They are written in textual form
in natural language. It causes three main obstacles to
easy integration and interoperability of systems:
• Semiotic heterogeneity (different interpretations
by different users).
• Difficult development in comparison to an explicit
specification in machine-readable form.
• Validation of the models developed.
The research presented in this paper is motivated
by demands for a suitable explicit and user-friendly
specification of conditional implementation related to
Rockwell Automation drives’ motion axis attributes.
Conditional implementation defines conditions (e.g.
usage of specific devices or values of certain motion
axis attributes) in which the motion axis attribute is
valid and should be presented within the OPC UA in-
formation model. Research questions which will be
answered in this paper are:
• What is a suitable formalism for model specifi-
cation? Concerning user-friendly interaction and
expressivity.
• What mechanism should be used for model vali-
dation?
• Reusability and extensions of the model.
This paper is organised as follows: First, related
work with respect to modelling formalism and their
expressivity, and validation capabilities, is presented.
Next, conditional implementation of motion axis at-
tributes is introduced, followed by presentation of
proposed solution of Daplex-based specification to-
gether with SWRL-based validation. Then, the pro-
posed solution is demonstrated on the examples of
conversions of MAA conditions into SWRL rules. Fi-
nally, the conclusions and future work are presented.
2 RELATED WORK
Hardware quality and quality engineering are a very
complex issue, which is studied in a separate branch
of study with detailed elaborated theory. However,
the simple definition of the term Quality from (Bruel
et al., 2021) may be reused: “Quality means that the
hardware does the right things and does them cor-
rectly.” The “things” are known as hardware require-
ments. While (Bruel et al., 2021) deals with hardware
systems, the same approach can be applied, for exam-
ple, to software systems.
The overall hardware quality is highly dependent
on the quality of these requirements. They define the
system to satisfy the user needs. Furthermore, the re-
quirements allow for the validation of the candidate
implementation against the definition. On the other
hand, quality is not dependent only on requirements,
but also on formal specifications of the system. The
difference or at least nuance may be perceived in the
meaning of these terms:
• requirements — properties of system parts rele-
vant to users and environment,
• specification — technical properties of system
parts.
This work is focused on the formal specification of
the system and, more precisely, on the formal speci-
fication of the information models. However, the dis-
tinction between requirements and specification is not
important, and formalisms suitable for specification
may also be exploited for formal specification of re-
quirements. Next, the focus of the presented work is
on explicit knowledge, that is, the knowledge speci-
fied outside a (control) code of an application, in con-
trast to implicit knowledge hardcoded within a code.
The following paragraphs present the selected and
possible approaches for dealing with the formal spec-
ification of information models with respect to user-
friendliness and possible validation support. The
OPC UA companion specifications are omitted from
these approaches. The important effort of the OPC
Foundation was to specify the OPC UA information
models to ensure seamless interoperability. Unfortu-
nately, the set of OPC UA specification documents is
written in natural language rather than as a formal and
explicit specification.
Formal Specification Languages (FSL). (Pang et al.,
2016) are used to specify the design requirements of
instrumentation and control systems. Their main pur-
pose is to provide the possibility of formalising design
requirements written in natural language. FSLs are
typically based on templates and patterns to assist in
requirements formalisation. Thus, the template may
be perceived as a preformatted textual representation
of semi-formal requirements. The template consists
of: fixed keywords, and attribute placeholders. And
when a template is instantiated, then its placeholders
are substituted by concrete values. The following list-
ing illustrates an FSL template example:
<system> s h a l l <a ct ion>
where <system> and <action> are placeholders.
Every pattern/template has rigorous semantics, fixed
keywords, and attribute placeholders. However, there
Towards Seamless Digitization in OPC UA
327

could be obstacles during the application of templates
across various domains, or inconsistencies may arise
during the addition of new templates.
An approach that provides the opposite solution
to the “hardcoded” solution for knowledge and in-
formation capture uses ontologies. Ontologies are
used for the description and serialisation of a given
conceptualisation (a mental image of some domain).
Specifically, the term ontology may be defined using
the well-known definition provided by Thomas Gru-
ber (Gruber, 1995) — an ontology is “explicit specifi-
cation of a shared conceptualisation.”
There are various formats/languages for seriali-
sations of ontologies, and the format selection has
direct impact on utilisable expressivity, as well as
possible additional tools such as editors, reasoners,
data storage, etc. Therefore, developers must con-
sider the intended purpose of an ontology. Today, Se-
mantic Web technologies (RDF (Lassila et al., 1998),
RDFS (Brickley et al., 2014), and OWL (McGuinness
et al., 2004) formats, in particular) are widely used for
ontology development, mainly because of scaleable
expressivity and also availability of additional (and
supporting) tools.
Web Ontology Language (OWL) is a Semantic
Web standard designed to represent rich and com-
plex knowledge about resources. OWL is a com-
putational logic-based language such that the knowl-
edge expressed in OWL can be reasoned by computer
programmes either to verify the consistency of that
knowledge or to make implicit knowledge explicit.
OWL is an important part of the W3C’s Semantic
Web technology stack, which includes on lower lev-
els RDF, RDFS, SWRL, SPARQL, etc. The OWL to-
gether with a reasoner (e.g., Pellet) is able to conduct
important reasoning tasks such as consistency check-
ing, individuals classification, etc. Thus, a subsequent
validation task may also be formulated as a task for
OWL ontology and a reasoner. However, the Open
World Assumption as the key feature of OWL ontolo-
gies (Horridge and Bechhofer, 2011) may cause ob-
stacles during constraints validation with the help of
automated reasoning.
On the other hand, the Semantic Web Rule Lan-
guage (Horrocks et al., 2004) should help overcome
the aforementioned obstacle. SWRL combines OWL
DL with function-free Horn logic, and therefore it al-
lows Horn-like rules to be combined with OWL DL
ontologies. Rules are of the form of an implication
between a body and a head. The meaning of a rule is:
whenever the conditions specified in the body hold,
then the conditions specified in the head must also
hold. The following listing illustrates a simple exam-
ple of the rule:
Person (?p ) ∧ h a s Si b l ing (?p , ? s ) ∧ Male (? s )
−> h asB rot her ( ?p , ? s )
with the meaning: all persons which have a sibling
and this sibling is a man, then they must have the
property hasBrother. The application of SWRL is,
for example, in (Fortineau et al., 2014), where SWRL
is used to express formal rules within ontology-based
models with an application to the nuclear industry.
Daplex (query language based on functional data
model) (Shipman, 1981) is a data definition and ma-
nipulation language for database systems, based on
the concept of data representation called the func-
tional data model. The focus of Daplex is on provid-
ing a “conceptually natural” database interface lan-
guage. Thus, the language provides user-friendly syn-
tax. Constraints have two parts: the quantification
and the main part. The variables are quantified and
specified on a given domain in the quantification part.
The main part of the constraints contains predicates
that should be satisfied. Furthermore, in addition to
data definition exploitation of Daplex, (Martins et al.,
2008) shows that Daplex provides an intuitive way of
viewing and querying data in the Semantic Web.
The suitability of the Daplex language for express-
ing constraints was demonstrated in CoLan (Bassili-
ades and Gray, 1995), a high-level declarative Con-
straint Description Language, for an application to an
Object-Orientated Database. Except for the utilisa-
tion of Daplex for constraint expressions, CoLan ex-
ploits Prolog (Clocksin and Mellish, 2003), which im-
plements the operational semantics of the constraint.
The CoLan system was dedicated to ADAM (Ellis
and Demurjian, 1991) object-orientated database and
there is no other application to another system.
The fulfillment of the requirements (user-
friendliness, expressivity, validation capabilities, and
re-usability) of the possible formalism is illustrated
in Tab 1.
FSL and Daplex represent languages with user-
friendly syntax which enable easy understanding
about statement meanings even for non-skilled users.
Both provide suitable expressivity, but FSL has no
standard way for expressing many constructs such as
cardinality expressions. And thus, re-usability of FSL
statements may result in semiotic ambiguity. Further-
more, FSL and Daplex do not have validation capa-
bilities. OWL and SWRL (as OWL extension) are
strong in their expressivity, standardisation, and re-
usability. The validation based only on the reason-
ing may be difficult for OWL due to Open World
assumption. OWL and SWRL are not user-friendly
for many applications compared to FSL and Daplex.
CoLan provides a user-friendly solution due to the use
of Daplex. The expressivity and validation capabili-
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328

Table 1: Overview of existing solutions presented in Sec.2.
User-friendly Expressivity Validation Re-usability
FSL X x — x
Daplex X X — X
OWL — X x X
SWRL — X X X
CoLan X X X x
ties (as the result of the combination of Daplex and
Prolog) are sufficient for many possible industrial ap-
plications and scenarios. However, the reusability of
this approach is limited due to the dedicated applica-
tion to ADAM database and the implicitly captured
process of validation and utilisation of Prolog.
3 PROPOSED APPROACH
The approaches presented in the previous section
cannot satisfy all demanded criteria, that is, a user-
friendly syntax for the formal specification, adequate
expressivity, and means for potential validation.
The approach proposed in this paper is to exploit
the Daplex syntax for the definition of constraints.
These constraints may be easily reused and published
in forms such as companion specifications.
Furthermore, SWRL is selected as a means for
validation instead of Prolog in CoLan solution. In
general, a constraint denotes an aspect of the informa-
tion model that may be subsequently validated. Two
steps have to be conducted to ensure SWRL valida-
tion:
• Create minimal ontology needed for SWRL rules
application.
• Conversion of Daplex statements into SWRL
rules.
Both of these steps may be performed automati-
cally.
3.1 Conversion of Daplex-Based
Constraints into SWRL
There are two basic elements of the Daplex syn-
tax which form queries, statements, and expressions.
Statements restrict the validity of queries to specific
instances of particular concepts and include data def-
inition statements and FOR loops (Shipman, 1981).
Expressions are specified within statements and are
evaluated to a set of entities. Expressions may involve
qualification, quantification, Boolean operators, and
comparisons.
In general, Daplex elements have the following
meaning when converted to SWRL:
• Daplex statements — Class assertions
• Daplex expressions — Object or data property as-
sertions related to a given statement(s)
In other words, statements restrict predicates to a spe-
cific set of individuals (from the ontological perspec-
tive), e.g., “forall x in Students” — the following
predicate will be evaluated against all individuals of
Students concept. The translation of this statement
into SWRL is:
Students (? x ) .
Next, expressions limit the query according to a
specification. In other words, they express conditions
on the properties of data and/or objects to individuals
from statements. E.g., such that x.birthdate = 2000.
The translation the above expression into SWRL is:
b i r t h d a t e (? x , ? xb ) ∧ sw rl b (? xb , 2 0 0 0 ) .
In the context of this research, daplex queries have
the following structure: the outer and nested part. The
outer part consists of a statement and an expression
— in general a Boolean predicate. The nested part
expresses an existential predicate. The nested part
is composed again of a statement and an expression.
The nested existential predicate may be as follows:
e x i s t s y i n Persons
such t h a t y . sex = ” Female ”
The existential predicate is the part of the query re-
sponsible for the aforementioned validation task, and
thus restricts the validation only to a specific subset of
individuals.
3.2 Minimal Ontology for SWRL Rules
In the ideal scenario, there is a shared ontology for a
given use case to execute SWRL rules. On the other
hand, the minimal required ontology may be derived
directly from the document specifying the Daplex
queries (if the document is considered as a shared and
verified standard/specification).
In such a case, the statements may be considered
as definitions of concepts, and expressions as defini-
tions of data/object properties. Thus, it is possible to
automate the minimal ontology creation, for example,
with the help of OWL API.
Towards Seamless Digitization in OPC UA
329

4 USE-CASE - DRIVE MOTION
AXIS ATTRIBUTES
This section presents the application of Daplex-based
queries conversion into SWRL on motion-axis at-
tributes conditional implementation. The motion axis
attributes may have specified condition(s) for which
the given attribute is applicable/valid. There are three
main categories of conditional implementation of mo-
tion axis attributes which were taken into considera-
tion in the context of this paper. The other types are
not interesting for further validation, e.g., conditions
which refer to a specific version of the integrated de-
velopment environment.
The three important types of condition are:
• Dependency on Usage of a Specific Device: for
example, Auto Sag Slip Increment and Auto Sag
Slip Time attributes are applicable if a position
feedback device is used. The other example is rep-
resented by the Commutation Polarity attribute,
which is applicable if a permanent magnet motor
is used.
• Dependency on the Value of Another At-
tribute: for example, the Break Voltage attribute
is applicable only if the value of the Frequency
Control Method attribute is set to “Basic V/Hz
only”. Another example is the Bus Observer Volt-
age Rate Estimate attribute, which is applicable
if the value of the “Converter Control Mode” at-
tribute is set to “Bus Voltage Control”.
• Dependency on the Value of Another Attribute
Together with a Specific Device Function: for
example, the Bus Voltage Error Tolerance Time
attribute is applicable only if the drive is either
operated in non-regenerative mode, or is used in
regenerative mode and the value of the Converter
Control Mode attribute is set to the “Bus Voltage
Control” value.
4.1 Example of MAA Constraints in
Daplex
In the following paragraphs, the form of MAA con-
straints in the Daplex-based syntax is presented.
The MAA condition of the type Dependency on
the usage of a specific device - “The attributes Auto
Sag Slip Increment (id 874) and Auto Sag Slip Time
(id 875) are applicable iff a position feedback device
is used.” has the daplex form as shown in Listing 1.
Listing 1: Auto Sag Slip Increment Conditional implemen-
tation
f o r a l l m i n MAA
such t h a t m. name=” Auto Sag S l i p In crement ”
e x i s t s d i n D
such t h a t d . typ e =” P o s i t i o n feedback
de vic e ”
where MAA is the set of all motion axis attributes
and D is the set of all available devices.
Next, the MAA condition of the type
‘Dependency on the value of another attribute:
“The Break Voltage attribute is applicable only if
the value of the Frequency Control Method attribute
is set to “Basic V/Hz only.””
has the daplex form as shown in Listing 2.
Listing 2: Break Voltage Conditional implementation
f o r a l l m i n MAA
such t h a t m. name=” Break V olt age ”
e x i s t s m1 i n MAA
such t h a t m1. name=” Frequency C ont r ol
Method ” and m1. va lue = 0
4.2 MAA Daplex Constraint
Conversion to SWRL
In the previous paragraphs, examples of MAA condi-
tion (i.e., dependence on a specific device and value
of another attribute) conversions into Daplex were in-
troduced. The conversion from Daplex to SWRL is
demonstrated in the last type of condition in the fol-
lowing paragraphs.
The MAA condition of the third type:
“The Bus Voltage Error Tolerance Time attribute
is applicable only if the drive is either operated in
non-regenerative mode, or is used in regenerative
mode and the value of the Converter Control Mode
attribute is set to the Bus Voltage Control value.”
has the Daplex counterpart:
Listing 3: The Bus voltage error tolerance time conditional
implementation
f o r a l l m i n MAA
such t h a t m. name=”Bus V olt age E r r o r
Tol erance Time ”
e x i s t s ( df i n DFn and m1 i n MAA
such t h a t m1. name=” Converter C o nt r ol
Mode” and m1 . value=0 and d f =”G” )
or ( d f i n Dfn such t h a t df . va lue =”N ” )
where MAA is the set of all attributes of the mo-
tion axis and DFn is the set of available device func-
tions.
The Daplex query has two parts: the outer pred-
icate and the nested predicate. The outer predicate
forall m in MAA such that m.name=“Bus Voltage Er-
ror Tolerance Time” is converted into SWRL as
Listing 4: Converted outer part of the condition into SWRL
body
ICINCO 2024 - 21st International Conference on Informatics in Control, Automation and Robotics
330

forall m in MAA
Class assertion -> MAA(?x)
such that m.name="Bus Voltage Error Tolerance Time”
Object/Data property assertion
-> name(?x,?xn) ^ swrlb:equal/contains(?xn,”Bus Voltage Error Tolerance Time”)
exists m1 in MAA and df in DFn
Implication: Class assertion -> MAA(?m1) ^ DFn(?df)
such that m1.name="Frequency Control Method” and m1.value = 0
and df.value="G"
Object/Data property assertion
-> name(?m1,?m1n) ^ swrlb:equal(?m1n,” Frequency Control Method”)
^ value(?m1,?m1v) ^ swrlb:equal(?m1v,0)
^ value(?df,?dfv) ^ value(?dfv,"G")
Figure 1: One of the output rules from Daplex to SWRL conversion.
MAA(? x ) ∧ name( ? x , ? xn )
∧ swrlb : eq ual ( ? xn , ” Bus V olt age E r r o r
Tol erance Time ” )
The nested predicate represents the disjunction of
two existential predicates df in DFn and m1 in MAA
such that m1.name=”Converter Control Mode” and
m1.value=0 and df=”G” and df in DFn such that
df=”N”. The disjunction breaks down SWRL into
two separate rules. The SWRL rules of the nested
predicate are shown in Listing 5-6
Listing 5: Converted nested part of the condition into
SWRL body (1st rule)
MAA(? y ) ∧ name( ? y , ? yn ) ∧ v alu e ( ? y , ? yv )
∧ swrlb : eq ual ( ? yn , ” C onv ert er Con t ro l
Mode ” )
∧ swrlb : eq ual ( ? yv , 0 ) ∧ DFn( ? z )
∧ value (?z , ? zv ) ∧ sw rl b : equa l (? zv , ”G” )
Listing 6: Converted nested part of the condition into
SWRL body (2nd rule)
DFn(? y ) ∧ value ( ?y , ? yv )
∧ swrlb : eq ual ( ? yv , ” N ” )
The conversion of the outer and the nested Daplex
predicates to SWRL together is illustrated in Fig. 1.
The figure illustrates only the first SWRL rule con-
cerning the regenerative drive mode. The second
SWRL rule is analogous.
For completeness, the definition of the SWRL
head (the rule consequent) in the conversion pro-
cess was not covered in the previous sections. MAA
conditions determine the body of the SWRL. Subse-
quently, the consequent section outlines the actions to
be taken once the conditions are met. There are vari-
ous possibilities on how to form the SWRL head.
1. Classify the motion axis attribute under a special
concept of the ontology. For instance, the Succes-
fullyValidated concept. The corresponding head
is presented as:
−> S u c c esful l y V a l i d a t e (?m)
2. Notify a user about the fulfilled conditions us-
ing SQWRL extension, e.g., printing to the out-
put MAA individual and its name with the help
of the select method. The corresponding head is
presented as:
−> sqw rl : s e l e c t (?m,?mn)
The overall process of rules specification as well
as possible validation is depicted in Fig 2 and may be
summarized in following steps:
• S(Q)WRL rules specification
– Input: Daplex form of the MAA conditions
– Definition of minimal required ontology if
needed using OWL API
– Specification of the set of S(Q)WRL rules
representing requirements on MAAs using
S(Q)WRL API
• Validation
– Input: MAAs for validation in the form of
Daplex existential predicates
– Create corresponding ontology individuals us-
ing the ontology and OWL API
– MAAs validation against S(Q)WRL rules
5 CONCLUSIONS
This paper addressed the challenge of semantic het-
erogeneity in industrial automation, particularly fo-
cussing on the conditional implementation of the at-
tributes of the motion axis. We explored the limita-
tions of existing approaches, such as natural language
specifications, and proposed a novel solution based on
Daplex and SWRL.
Towards Seamless Digitization in OPC UA
331

Initial Daplex
Specification of
Conditions
MAAs for Validation in
Daplex Form
Ontology
S(Q)WRL Rules
Validation Outcomes
Figure 2: Rules specification and validation process overview.
Our approach leverages the user-friendly syntax of
Daplex for defining constraints and the reasoning ca-
pabilities of SWRL for validation. We demonstrated
the feasibility of our solution by converting Daplex-
based constraints into SWRL rules and generating a
minimal ontology for their execution. The use case of
motion axis attributes illustrated practical application
and benefits of our approach. However, the main goal
of this work was not to proposed the solution which
is able to satisfy all possible requirements from dif-
ferent applications or domains. The main goal was
to demonstrate that exists user friendly and explicit
specification of information models, which should be
shared in suitable form (e.g. a standard) to overcome
the semantic heterogeneity and facilitate the interop-
erability.
To conclude, answers to research questions fol-
lows:
• What is a suitable formalism for model specifica-
tion? OPC UA seems to be the most mature stan-
dard (in the context of Industry 4.0). This standard
may be accompanied by a set of conditions in a
user-friendly and explicit way, such as presented
in this work to facilitate interoperability and inte-
gration.
• What mechanism should be used for model vali-
dation? The mechanism is given by the form of
the specification. In the context of the presented
solution, it is reasoning. The mechanism should
not only validate the model using the given condi-
tions. It should also validate that every new con-
straint is consistent with the constraints already
defined.
• The reusability of models should be facilitated by
a set of rules (“best practices”) on how to specify
the model. However, the model specification with
specific constraints is tailored to a given applica-
tion, and thus the tranferability is complicated.
ACKNOWLEDGEMENTS
This research has been supported by the Rockwell
Automation Laboratory for Distributed Intelligent
Control (RA-DIC), by institutional resources for re-
search by the Czech Technical University in Prague,
Czech Republic, by the OP VVV DMS Cluster 4.0
project funded by The Ministry of Education, Youth
and Sports, and by the European Union under the
project Robotics and advanced industrial production
(reg. no. CZ.02.01.01/00/22 008/0004590).
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