Design and Application of a Reconfigurable Control to a

Cyber-Physical System

Imane Tahiri

, Alexandre Parant

, François Gellot

, Alexandre Philippot

and Véronique Carré-Ménétrier

CReSTIC, University of Reims Champagne-Ardenne, Moulin de la Housse, Reims, France

Keywords: Discrete Event Systems, Fault Tolerant Control, Control Reconfiguration, Supervisory Control Theory,

Digital Twins, Cyber-Physical Systems.

Abstract: In the previous edition of ICINCO, authors have presented a theoretical comparison between centralized and

distributed control reconfiguration of Discrete Event Systems (DES). In this paper, we propose to enlarge the

proposition until the implementation step into a Programmable Logic Controller. The control is based on a

distributed architecture including time-delayed events and supervisory control theory. Moreover, in a context

of Industry 4.0, the verification and simulation phases are performed on a digital twin before implementation

on the real system.

1 INTRODUCTION

The continuous improvement of existing products,

the massive arrival of new ones on the market and

changes in environmental and safety legislation mean

that industries have to adapt in order to remain

competitive (Koren et al., 1999). In an Industry 4.0

context, modern manufacturing systems face an

aggressive international market composed of multiple

unpredictable changes; the paradigm of

Reconfigurable Manufacturing System has been

created to respond to these changes with limited cost.

(ElMaraghy, 2005; Koren et al., 1999).

Manufacturing systems are becoming more and

more complex with the arrival of Internet of Things

(IoT), the mass customization of products and the

increasing use of software in factories (W.

ElMaraghy et al., 2012). The increased complexity in

systems induces a large amount of information that

can lead the system to behave abnormally

(ElMaraghy et al., 2005).

Fault Tolerant Control (FTC) is intended to keep

the system available by mitigating unwanted behavior

https://orcid.org/0000-0003-2203-2604

https://orcid.org/0000-0001-8433-8671

https://orcid.org/0000-0001-8797-1704

https://orcid.org/0000-0001-5229-9452

https://orcid.org/0000-0002-9576-9108

that may occur when a failure happens. In case of

failure, the system must identify the resources

affected and substitute them with resources available

for reconfiguration; the system must then have

hardware and/or software redundancies (Dangoumau

et al., 2000).

Two types of FTCs are defined according to their

behaviour when a fault occurs: Passive Fault Tolerant

Control (PFTC) and Active Fault Tolerant Control

(AFTC) (Zhang & Jiang, 2008). PFTC are designed

to respond to a multitude of predefined failures, while

AFTC adapts the control to a failure actively. A

diagnostic block detects faults in the system and the

AFTC modifies the system controller to take the fault

into account. As a part of a AFTC process, (Tahiri et

al., 2019) presented an approach for reconfiguring the

control of a Cyber-Physical System (CPS).

CPS is one of the major technologies in the

evolution of industries towards the fourth industrial

revolution with IOT and cloud computing (Xu et al.,

2018). A CPS is composed of a set of virtual

computing elements interconnected and connected to

the physical world to link them together.

718

Tahiri, I., Parant, A., Gellot, F., Philippot, A. and Carré-Ménétrier, V.

Design and Application of a Reconﬁgurable Control to a Cyber-Physical System.

DOI: 10.5220/0009896107180725

In Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2020), pages 718-725

ISBN: 978-989-758-442-8

In this work, CPSs were considered as discrete

event systems and are based on a comprehensive

methodology ranging from specification to

verification of the control implemented in a

controller.

This paper describes the implementation of this

approach on a flexible manufacturing system and on

its digital twin from University of Reims Champagne-

Ardenne experimental platform: Cellflex 4.0

(https://www.univ-reims.fr/meserp). Section 2 briefly

presents the methodology of (Tahiri et al., 2019). Its

implementation and the description of the platform

are introduced in Section 3. Section 4 takes the steps

of the benchmark approach practically before

concluding with a discussion.

2 PROPOSED APPROACH

The approach used in this paper allows a system to be

reconfigured when a sensor failure occurs without

using redundant hardware. The system has a

diagnoser that detects failures and two different

controllers: one controlling the system in normal

behavior and the other taking action when a sensor

failure occurs (Figure 1). The second controller is

based on time estimation. Information lost due to

sensor malfunction are replaced by time-delayed

information to keep the system running.

Controller

(N)

Controller

(F)

Plant

Reconfiguration

Diagnoser

Control actions

Sensors signals

Sensors behaviors

Figure 1: Reconfiguration loop of the control.

2.1 Supervisory Control Theory

In this paper, we are interested in the problem of

reconfiguring the controller after a failure has

occurred in a specific class of system: Discrete Event

System (DES). The control law used is based on the

Supervisory Control Theory (SCT) introduced in

(Ramadge & Wonham, 1989). SCT allows supervisor

design that keeps the plant in a safe state of operation

according to the given control specifications.

SCT uses two separate automata (Figure 2). On

the one hand, a plant is modeled as an event generator.

On the other hand, the supervisor which receives as

input all events generated by the plant (controllable

and uncontrollable) and has the specifications

describing the desired behavior of the system. The

supervisor restricts the behavior of the system by

allowing or disallowing controllable events according

to the specifications.

Plant Supervisor

Con trol action s

Sensors signals

Figure 2: Control loop of the SCT.

The controller development is done in three steps

(Figure 3):

1. The operating part modelling of the system,

of the safety constraints (the forbidden

system behavior) and of the liveness

constraints (the authorized system

behavior);

2. The supervisor synthesis from the safety

constraints and from the operating part

model;

3. The synchronization of the supervisor with

the liveliness constraints to obtain the

controller.

S: Supervisor synthesis

| |: Synchronous composition

Actuators model

Detectors model

Specifications

Safety Liveliness

Plant

behavior

model (P)

Supervisor

(SUP)

Controller

(CONT)

PLC

| |

Implementation

Figure 3: Controller overview steps.

2.2 Distributed Controller

Most of the approaches used for the controller

calculation are based on a centralized architecture.

The risk of these approaches is combinatorial

explosion due to the complexity of the systems. The

modelling of the operational part, of the safety and of

the liveliness constraints become laborious. To

overcome this problem, the method implemented in

this paper uses a distributed approach based on the

work of (Qamsane et al., 2017) :

1. The plant is decomposed into several plant

elements PE (n x PE);

Design and Application of a Reconﬁgurable Control to a Cyber-Physical System

719

2. The local safety and liveliness specifications

are modelled for each PE as well as global

specifications for the plant;

3. The local controllers for each PE are

synthesized using the local safety and

liveliness specifications;

4. The global safety and liveliness

specifications are used to synthesize the

distributed controller of each PE;

5. The distributed controllers are interpreted in

Grafcet (IEC60848 standard);

6. All the obtained grafcets are implemented in

an industrial Programmable Logic

Controller (PLC) thanks to programming

languages (IEC61131-5 standard).

The method used in (Tahiri et al., 2019b)

introduces three additional steps allowing:

- The synthesis of two controllers for each PE:

one for normal behavior and one considering

the occurrence of a fault;

- The synthesis of reconfiguration rules in

addition to the global specifications;

- The interpretation of the reconfiguration

rules in Grafcet to define the switching

between the grafcets of the two controllers

of each PE.

3 IMPLEMENTATIONS ON A CPS

The specification, verification and validation of the

control follow V-cycle structure (validation and

verification model), which allows a return to the

design stages if the tests performed are inconclusive

(Figure 4).

Figure 4: Control design cycle.

The design phases are performed using the

approach presented in the previous section. The

contribution of this paper is the use of a digital twin

to simulate and verificate the reconfigurable control

before its validation on real manufacturing system.

The engineer who designs the control can forget

constraints or make programming errors during

implementation. Feedback loop in the control design

cycle allows adjustments of local and global

constraints or corrections in the PLC program.

3.1 Using Digital Twins for

Development in Industry 4.0

Industry 4.0 has become a priority in research and

industry in recent years. The aims of Industry 4.0 are

to reduce development time, customize the product

on-demand, improve decision making and resources

management (Lasi et al., 2014).

All of the plant components are integrated and

connected to a central computer, the cyber-physical

system (CPS), that coordinates the whole (Rodič

Blaž, 2017). CPS forms a network of digital elements

interacting with physical inputs and outputs. CPS and

digital twin aim to bridge the physical and digital

worlds. The difference lies in the approach used: the

central components of CPS are sensors and actuators

whereas digital twins are centered on a model-

oriented approach. The digital twin can be integrated

into the CPS to improve simulation modelling (Rodič

Blaž, 2017) or to improve its management in real-

time (Tao et al., 2019).

A digital twin is a virtual representation of a real

system. It contains different models that are

interconnected to reproduce the behavior of the real

system: the physical model, the functionality model

and the communications interfaces (Schluse &

Rossmann, 2016).

Digital twins will make it possible in the coming

years to integrate simulation as an integral phase of

the life cycle and one of the main system

functionalities (Rosen et al., 2015). Their use for

development, verification and validation will reduce

development costs and enable the design of safer and

more robust systems (Schluse & Rossmann, 2016).

Digital twins can be divided into 3 sub-categories

depending on the type of exchange between the real

physical system and the digital one: the digital model,

the digital shadow and the digital twin (Kritzinger et

al., 2018). The digital twin used in this paper is

classified in the sub-category “digital model”, it

digitally represents the real system but there is no

automatic data exchange between the two systems. A

change of state in one of the systems must be

manually transferred to the other system. This level

of integration is still enough to carry out the

verification and simulation phases of the

reconfigurable control.

ICINCO 2020 - 17th International Conference on Informatics in Control, Automation and Robotics

720

3.2 Description of the CPS

The implementation of the reconfigurable control is

carried out on the flexible manufacturing system

Cellflex 4.0.

Storage

station

Process

station

Filling and

screwing station

Cap supply

station

Transfer

station

Conveyor

Import/Export

station

Figure 5: Workstation of the Cellflex 4.0.

The aim of the system is to fill bottles to group

them in batches of six in sixpacks and finally store

them. First, the import-export station brings the

sixpacks onto the central conveyor that connects each

workstation. Each sixpack is placed on a wagon. At

the same time, caps are fed to the filling station by the

cap supply station while the bottles are being filled.

The bottles are closed in the filling station and

transported to the transfer station. When six bottles

are available, a sixpack is fed to the transfer station

and the bottles are placed three by three on it. Then,

the sixpacks returned to the import-export station for

export from the system. If the station is full, the

sixpacks are temporarily stored in the storage station

(Figure 5).

4 SYNTHESIS OF THE

RECONFIGURABLE

CONTROL

In this paper, we implement the control on the

Cellflex 4.0 cap supply station (Figure 6).

This station consists of eight actuators controlled

by various technologies and fifteen sensors. The PE

defined for the station are the cap dispenser, the

ejector cylinder, the rotary cylinder, the suction cup

of the rotary cylinder, the conveyor, the handling arm,

the gripper of the handling arm and the conveyor of

the handling arm.

https://www.univ-reims.fr/meserp/projets/factories-of-

future-champagne-ardenne-f.f.c.a/cper-f.f.c.a,24346,

40021.html

Figure 6: Cellflex 4.0 cap supply station.

4.1 Example of a Distributed

Controller Synthesis

We have synthesized the distributed controllers for

each PE defined previously. We will only detail the

distributed controller design of the ejector cylinder in

this article, but the design steps of each PE can be

found at the following links

4.1.1 Synthesis of the Local Controller

The first step in the synthesis of the distributed

controller is the modelling of the PE model of normal

and timed mode.

Figure 7: The ejector cylinder model of normal mode.

The ejector cylinder model of normal mode is

obtained by synchronizing the actuator model and the

sensor model (Figure 7). The ejector cylinder is

monostable; thus, it has only one actuator activated

by the action EJ and two sensors indicating if the

Design and Application of a Reconﬁgurable Control to a Cyber-Physical System

721

cylinder is retracted cer or if the cylinder is extended

ces. The ejector cylinder model take into account the

mutual exclusivity of ces and cer. They can’t be

active at the same time, the occurrence of this event

is the consequence of a fault (sensor stuck-on for

example).

In this example, we consider that the sensor ces is

faulty and the information sent is no longer reliable.

The activation time d

and deactivation time d

of the

sensor have been estimated and measured by clock

and ck

. The ejector cylinder model of timed

mode is obtained by replacing ces information by the

timed information (Figure 8).

Figure 8: The ejector cylinder model of timed mode.

The normal behavior of this element corresponds

to the output of the cylinder with the action EJ until

the sensor ces is activated; then this action must be

inhibited to retract the cylinder until the sensor cer is

activated. To prevent transitions in the model that

deviate from this behavior, the liveliness constraints

have been defined in normal mode by:

+ q

) . ↓ EJ = 0 (1)

+ q

) . ↑ EJ = 0 (2)

The liveliness constraints of the timed mode are

defined by:

+ q

) . ↓ EJ = 0 (3)

+ q

) . ↑ EJ = 0 (4)

These liveliness constraints reflect the functional

safety of the ejector cylinder: activation and

deactivation commands must be active until the

corresponding sensor is activated.

The synchronization of previous models with the

corresponding local specification equations gives the

local controllers LC

corresponding to the normal

behavior and LC

corresponding to the timed

behavior.

Figure 9: a) LC

, b) LC

of the ejector cylinder.

4.1.2 Synthesis of the Distributed Controller

The ejector cylinder has physical interfaces with the

cap dispenser and the rotary cylinder on which the

suction cup is located. The caps are ejected from the

magazine and feed the rotary cylinder.

The ejector cylinder output is conditioned by the

presence of a cap in the magazine: the sensor pm

indicating this presence is used, it is active at 0. The

rotary cylinder must be on the conveyor side (sensor

c_vrc) so that it does not block the ejector output. The

cylinder’s retraction is conditioned by the cap being

grasped by the suction cup: the sensor c_vt of the

suction cup indicating the under-pressure section cup

is used. These global liveliness constraints are

grouped in the form of equation in table 1.

Table 1: Global liveliness constraints.

PE Condition if Then

Ejector

cylinder

𝑐

𝑣𝑟𝑐. 𝑝𝑚



1

Ord EJ

𝑐

𝑣𝑡  1

Inh EJ

The synchronization of global liveliness

constraints with LC

and LC

gives the distributed

controllers DC

and DC

corresponding to normal

and to faulty behavior (Figure 10).

Figure 10: a) DC

, b) DC

of the ejector cylinder.

ICINCO 2020 - 17th International Conference on Informatics in Control, Automation and Robotics

722

The distributed controller of each mode is then

interpreted in Grafcet (Figure 11) following the

approach described in (Qamsane et al., 2017).

Figure 11: a) Grafcet interpretation of DC

, b) Grafcet

interpretation of DC

of the ejector cylinder.

The differences between the two grafcets are

minimal, there are only two transitions that are

modified. It is the information from the faulty sensor

that is replaced by time delays. Nevertheless, the

interest of this approach lies in the global construction

methodology which is generalizable to any type of

DES. The two grafcets allow the operator to

distinguish in which mode the system is in to detect a

sensor failure.

4.1.3 Synthesis of the Reconfiguration Rules

Last step is to synthesize the reconfiguration grafcets

from the reconfiguration rules.

The reconfiguration rules allow to switch from

normal mode to timed mode when a failure occurs or

to switch from timed mode to normal mode when the

failure is corrected.

The reconfiguration rules are defined by logical

equations such as:

RC : If X

and f

= 1 Then

(F: G

}) and (F: G

{})

Else If X

and f

= 0 Then

(F: G

}) and (F: G

{})

(5)

With:

- F: forcing operation

- G

: grafcet interpretation of DC

- G

: grafcet interpretation of DC

: Boolean variable associated with step “i”

of G

and X

ij,

corresponding variable

associated with step “ji” of G

: Boolean variable indicating the

occurrence of a failure on the sensor (f

=1).

Equation 5 defines transition from G

to G

: when

step X

of G

is active and the failure has occurred.

Then, step X

of G

is forced and G

is deactivated. It

also defines the reverse switch: when X

of G

active, and the failure has been repaired. Then, step

of G

is forced and G

is deactivated.

The sensor ces failure is associated with the

variable f

ces

. We have defined two reconfiguration

rules: one allowing the passage from one grafcet to

the other before ces activation and the other passage

before its deactivation. These rules are defined by:

: If X

and f

ces

= 1 Then

(F: G

}) and (F: G

{})

Else If X

and f

ces

= 0 Then

(F: G

}) and (F: G

{})

(6)

: If X

and f

ces

= 1 Then

(F: G

}) and (F: G

{})

Else If X

and f

ces

= 0 Then

(F: G

}) and (F: G

{})

(7)

Reconfiguration rules are interpreted in Grafcet to

obtain the reconfiguration grafcet (Figure 12).

Figure 12: Reconfiguration grafcet.

The grafcets of each PE and the reconfiguration

grafcet are translated into Ladder Diagram (LD)

language to be implemented in a PLC. We used Tia

Portal (Total Integrated Automation Portal) software

developed by Siemens to write the PLC program.

Grafcet is commonly used and well know in

industry but it is a specification tool. We illustrate our

example by ladder language but it is at the discretion

of the end user to chose the language. Other technique

using Petri net can be used. However, to our

appreciation, PN is more academic than industrial in

its use.

Design and Application of a Reconﬁgurable Control to a Cyber-Physical System

723

4.2 Verification and Simulation

Program verification tests is defined in two steps:

1. Verification of deadlock and liveness

properties of the code before implementation

by Model-Checking.

2. Simulation on the digital twin of the Cellflex

4.0 (Figure 13).

The contribution consists of designing two

modes: normal behavior and one tacking into account

the fault detection. The switch between them is

ensured by the set of reconfiguration rules presented

by several Grafcets. The reconfiguration Grafcets are

strongly solicitate. It is, therefore, necessary to ensure

the non-blocking of all the implanted control

Grafcets. For this, the distributed controllers are

verified through a model-checker before the

implementation in a PLC. This contribution is not

presented into the paper.

The second step is the use of the proposition in a

context of Industry 4.0. The digital twin is designed

with the Siemens NX MCD (NX Mechatronics

Concept Designer) software. The digitally designed

elements of the Cellflex are imported into NX MCD

and then, the physical interactions are defined to

replicate the behavior of the real system. The digital

twin also has the same control and command

interfaces as the Cellflex.

The program is integrated into a simulated PLC

using PLCSIM Advanced software developed by

Siemens, it allows the link to be made with the digital

twin by simulating TCP/IP communication. The input

and the output mnemonics of the digital twin and the

Figure 13: Digital twin cap supply station.

https://www.univ-reims.fr/meserp/projets/factories-of-

future-champagne-ardenne-f.f.c.a/cper-f.f.c.a,24346,

40021.html

simulated PLC must match to synchronize the PLC

program with the sensors and actuators of the digital

twin.

Several simulations have been performed on the

digital twin, without failure in the first instance, to

estimate the times required for the activation and

deactivation of the sensor ces.

Then, we simulated the sensor failure and

checked the system behavior with the grafcet of timed

mode. A video comparing the system behavior with

and without the failure can be find at the following

link

The system retains similar behavior and

performance despite the failure thanks to the

implementation of the reconfigurable control and

precise time estimation timed mode. The

reconfiguration grafcet allow instantaneous control

changeover without latency in the system.

5 CONCLUSIONS

We have presenting and implementing a

reconfigurable fault-tolerant control on a flexible

manufacturing system and its digital twin in this

paper.

The proposed approach uses SCT and distributed

controller synthesis to reconfigure the controller

when a failure occurs on a sensor. The control was

designed using finite state machine and interpreted

using Grafcet. The implementation has been carried

out on a simulated PLC connected to the digital twin

to check the behavior and performance of the system.

This approach can be done iteratively. In this paper,

only one failure is considered but failures of other

sensors can be easily added. When the DC

of each

PE are validated, it is then sufficient to repeat the

methodology of section 4 by adding only the sensor

specific timed information. The sensor information

must be replaced by a timed information and interpret

the distributed controller in grafcet. The appropriate

reconfiguration rules must be added to the

reconfiguration grafcet. The grafcets are verified by

model-checking and implemented in the PLC to

perform the simulation phases.

The digital twin used to implement the approach

was designed after the actual manufacturing system.

In this situation, the design and development of a

digital twin is an expensive and tedious phase to

obtain a reliable twin that reproduces the behavior of

ICINCO 2020 - 17th International Conference on Informatics in Control, Automation and Robotics

724

the real system as closely as possible. The digital twin

must also be kept up to date with every change in the

real system, including a new design and development

phase.

Despite these drawbacks, the use of the digital

twin reduces the cost and time of the simulation

phases while preserving the real system. Errors can

occur during the design of the reconfigurable control.

For instance, some safety or liveliness constraints

may be forgotten or may not be enough for the correct

operation of the system. Errors can also occur during

the implementation of the control in the PLC. As the

digital twin is contained in software, it cannot be

physically damaged. However, it still exposed to

software issues. Embedded software has restriction

has well and it needs high computing power to run

accurately.

ACKNOWLEDGEMENTS

This work is integrated in the project FFCA

(Factories of Futur Champagne-Ardenne). The

authors would like to thank the region Grand-Est

within the project FFCA (CPER PFEXCEL).

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