Fuzzy Controller based on PLC S7-1200

Application to a Servomotor

Isaías González Pérez, A. José Calderón Godoy and Manuel Calderón Godoy

Industrial Engineering School, University of Extremadura, Avenida de Elvas, Badajoz, Spain

Keywords: Fuzzy Control, Process Automation, PLC, Servomotor.

Abstract: This paper presents the design and validation of a fuzzy logic controller implemented with an industrial

programmable logic controller (PLC). The chosen device belongs to the S7-1200 series of Siemens, whereas

the code has been developed in Ladder Diagram language using the software TIA Portal. The fuzzy

controller is of Mamdani type and is applied to control the speed of a servomotor. A comparison with a

Simulink/Matlab fuzzy controller is done to validate the developed software module and to show the

feasibility of the PLC to manage this kind of control algorithm.

1 INTRODUCTION

Fuzzy logic emerged from studies of Lofti A. Zadeh

in 1965. It is a mathematical formalism to represent

the human reasoning so it is very useful in Expert

Systems and Artificial Intelligence applications.

Fuzzy based control or fuzzy control began to

develop at 70’s. Nowadays it is applied in several

processes with demonstrated effectiveness and great

interest from the scientific and technologic

community.

The main advantages of fuzzy logic for process

control are fast decision capability, applicability to

nonlinear systems, and intuitive definition of the

controller behaviour. Furthermore, there is no need

of either historical data or mathematical models like

other intelligent controllers such as neural networks

or genetic algorithms.

PLC are electronic devices to control sequential

processes. Their main features are high reliability

and robustness. They are widely applied in industrial

processes, but are used in other fields such as home

and building automation, renewable energies

systems, etc.

It is evident the interest of combining and

integrating an advanced control method, like fuzzy

logic, with traditional automation devices, PLC. This

way, PLC can be applied in systems where it is

difficult to obtain accurate models or with

nonlinearities, delays, etc.

The calculation power of modern PLC allows

implementing advanced control strategies in their

programs. The implementation of fuzzy controllers

in PLC makes them useful for many applications in

industrial environment.

Lots of PLC manufacturers offer additional

software packages or modules to program fuzzy

controllers, FLC (Fuzzy Logic Controller). The

disadvantages are the consequent cost increment

and, mainly, an absolute lack of flexibility to modify

their codification.

On the other hand, due to the growing interest

and application to process control, fuzzy control has

a specific section in the open international standard

IEC 1131, which is referred to PLC standardization.

In 1997, the part IEC 1131-7 defined the Fuzzy

Control Language, FCL, i.e., a group of functions to

program applications of fuzzy control. In addition,

several research works study advanced fuzzy

methods such as fuzzy modelling and control

(Piegat, 2001; Zhang and Liu, 2006), and neuro-

fuzzy controllers (Joelianto, 2013).

Scientific literature about controllers based on

fuzzy logic implemented by means of PLC is scarce.

In the case of Siemens s7-1200 model, no works

have been found in the studied bibliography. Despite

that scarcity, there are some examples which are

exposed from this point.

In (Ruan and Van der Wal, 1998), a fuzzy

controller is developed in an Omron PLC (Sysmac

C200HS) to control the output power of a nuclear

reactor. The programming of the FLC is performed

using specific software and module. In (Li and Tso,

1999) a fuzzy controller with an Omron PLC is

156

González Pérez I., José Calderón Godoy A. and Calderón Godoy M..

Fuzzy Controller based on PLC S7-1200 - Application to a Servomotor.

DOI: 10.5220/0005013601560163

In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2014), pages 156-163

ISBN: 978-989-758-039-0

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

applied to a thermal process. The limitation in the

processing capacity of the PLC led to the authors to

run a program in a PC to solve the knowledge base.

Karasakal et al. (Karasakal, 2005) use a Siemens

PLC s7-200 to implement a fuzzy PID controller

with auto tuning and compare it with classical PID.

Bogdan et al. (Bogdan, 2007) implement a Self-

Learning Fuzzy Logic Controller, SLFLC, for

Simatic PLC which is applied to control the position

of a servo motor. Song et al. (Song, 2007) develop a

fuzzy controller based on a Siemens PLC s7-200 to

automate the processing of egg powder. Sun et al.

(2009) apply a fuzzy controller with a Siemens PLC

s7-300 to a sewage disposal system of a chemical

plant. The authors indicate the high cost of modules

for fuzzy logic of the manufacturer. They allege that

the developed controller is accurate and flexible due

to the ability to adapt to user demands.

Aydogmus (Aydogmus, 2009) present a fuzzy

controller implemented with a Siemens PLC s7-200

to control a tank level. This researcher describes his

proposal as a low cost solution because of the fact

that it has been developed without using fuzzy logic

software packages. In (Saad and Arrofiq, 2012) a

method to develop fuzzy-PID controllers in PLC for

PWM-driven induction motors is presented. In

(Cingolani and Alcalá, 2012) an open code library

based on Java, iFuzzyLogic, to design and

implement fuzzy controllers following the standard

IEC 61131-7 for Fuzzy Control Language is

exposed. Furthermore, a review of 25 software

packages dedicated to develop fuzzy controllers is

performed, highlighting the interest received by this

control technique.

On the other hand, Bosque et al. (Bosque, 2014)

assert that the programming flexibility and the cost

of PLC contribute to the implementation of fuzzy

control in industrial environments.

The main objective of this work is the design and

validation of a software module to implement fuzzy

logic controllers with a PLC. The case of speed

control for a servomotor is considered for this

purpose.

The rest of the paper is organized as follows.

Section 2 describes the main features of the PLC, the

servomotor and the software involved. In section 3

the programming of the module for fuzzy control

with the PLC s7-1200 is shown. In section 4 a fuzzy

controller designed for speed control of the

servomotor is described. The experimental results

are shown in section 5. Finally, conclusions and

further works are outlined.

2 SYSTEM DESCRIPTION

The module for fuzzy control has been developed

for the PLC s7-1200 of Siemens. TIA Portal V11

(Totally Integrated Automation Portal) of Siemens is

the software used to program and configure the PLC.

WinCC flexible 2008 Runtime is used to design a

Human-Machine Interface, HMI, based on PC to

monitor the process under control and to store the

data for further analysis.

The CPU model of the PLC is the 1214C which

incorporates Ethernet/PROFINET interface and 2

analogue inputs. In addition, an added Signal Board,

SB 1232 AQ1, module provides an analogue output

that will be used to apply the control signal to the

servomotor.

The servomotor corresponds to the Servo

Fundamentals Trainer (33-001) by Feedback (Figure

1). On the one hand, it comprises a mechanical unit

(33-100) which contains the DC motor, an analogue

tachogenerator, encoders, potentiometers, magnetic

brake and other supporting electronics. On the other

hand, an analogue unit (33-110) and a power supply

(01-100) provide the power supplies and signals.

Connection between both units is by way of a ribbon

cable for signal transmission.

Figure 1: Servo Fundamentals Trainer (33-001).

This equipment allows open and closed loop

speed and position control. It can be linked with a

PC through USB connection. In our case, these

possibilities are not used because both the control

and data acquisition are carried out by the PLC.

2.1 System Integration

The PLC is responsible of operations such as sensors

data acquisition, fuzzy control algorithm execution

and driving of actuators according to the control

signal generated. Furthermore, the PLC

communicates with the system for monitoring

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(HMI) through the Ethernet network where both

devices are integrated. Figure 2 illustrates the

scheme of connections between the components of

the system. The HMI application runs in the

computer connected to the PLC via Ethernet by

using the PROFINET interface. This application

accesses to data blocks in the PLC memory to be

stored and displayed.

Figure 2: Connections between PLC, HMI and Servo.

3 FUZZY CONTROL MODULE

Fuzzy logic allows using the common language to

describe problems; this is, to process inaccurate and

qualitative information in terms of fuzzy sets.

Because of this, fuzzy logic is better than classical

logic to represent the human knowledge and

reasoning. Fuzzy control consists on leading the

process output to a desired value with control

actions calculated according to a fuzzy description

of such process. Fuzzy control is the main field of

application of fuzzy logic and uses the experience in

manual operation over a plant to design the control

system.

The general structure of a fuzzy controller is

depicted in the block diagram of Figure 3. The

fuzzification of each natural value of the inputs

consists in determining the degree of membership to

each defined fuzzy set. The inference engine uses

the fuzzy rules to process the input information and

to generate the controller output. The defuzzification

process converts the result of the fuzzy rules into a

numeric or crisp value, non-fuzzy, which acts as the

controller output signal.

In the module here presented each one of those

parts has been solved by means of a subroutine,

called function or FC, in the PLC.

Figure 3: Block diagram of a fuzzy controller.

Mamdani type fuzzy controllers can be

implemented with the developed module. This kind

is more intuitive and adapted to the human language

with respect to Sugeno type.

Ladder Diagram language has been used to

develop the required code to perform the operations

of the fuzzy algorithm.

The maximum number of variables is 6, for each

one of them can be defined up to 5 subsets. Each

subset is determined by means of 4 points. The

available fuzzy logic operations are AND and OR.

The first one can be applied by the Product or the

Minimum procedure, whereas the OR operation is

solved by the Maximum method. The rule base can

be composed of up to 9 rules. The implication

method is Min. The aggregation method is Max.

Only one output variable is considered, so the

controllers developed are of MISO (Multiple Inputs

Single Output) type.

The design of the controller and its parameters

must be performed in a stage before the

configuration of the PLC. Hence, the developed

module does not serve to design the fuzzy controller,

but the implementation of such controller. Once the

engineer or designer has established the controller

parameters (I/O, rules, etc.); these ones will be

programmed in the PLC via the TIA Portal software.

3.1 Fuzzy Module Structure

The fuzzy module has been developed to be versatile

and user friendly. Furthermore, the use of Ladder

diagram with a modular design provides a flexible

very useful for future improvements structure.

The user has to specify and configure in the PLC

the following parameters of the fuzzy controller:

─ Input variables: number, points defining fuzzy

subsets.

─ Output variable: points defining fuzzy subsets.

─ Rules: number, premises and conclusions.

─ Fuzzy logic operation: AND or OR, with

Minimum or Product options for AND.

In order to optimize the CPU performance

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according to the available resources, several

functions, FC, have been included. Each FC is

designed to carry out a determined processing of

information. These blocks have input and output

data, but do not require associated data block which

would occupy memory and would slow down the

calculations. The required data (variables, points of

membership functions, etc.) are stored in Data

Blocks, DB, which are available for the FCs.

Figure 4 shows the flowchart of the module for

fuzzy control in the s7-1200 PLC. From the main

program, OB1, the FCs that implement the different

parts of the fuzzy control algorithm are called

sequentially. Reading of input data and writing of

the output signal are made directly in the OB1. The

control signal is applied to the servomotor by means

of the voltage analogue output obtained through the

Signal Board.

3.1.1 Fuzzification

The Trapezoidal function acts as basis to define the

membership functions, so there are 5 available

functions: Trapezoidal, S, Z, Triangular and

Singleton. In the case of functions S and Z type, due

to their configuration as particular cases of the

Trapezoidal one, they are not soft, simplifying this

way their codification.

The maximum number of subsets is 5 and the

linguistic labels are S1, S2, S3, S4 and S5 for all the

variables. To define these functions the user has to

introduce the values of 4 points signalled as A, B, C

and D in Figure 5, for all subsets of each variable. It

is not required to specify the range of input variables

due to the fact that it is implicitly expressed with the

points that define the subsets as said before.

The calculation of membership degrees begins

with a comparison between the actual value of the

variable and the points that define the functions.

If the input is located in an interval which

corresponds to a membership degree of 0 or 1, such

value is directly assigned (no more operation is

needed). In another case, the equation that defines

the straight line is determined and used to calculate

the membership degree. Figure 6 shows the

described sequence as flow diagram, where MD

means membership degree.

3.1.2 Rules

A maximum of 9 rules can be formulated, all of

them of Mamdani type. This number of rules has

been considered adequate for the application here

exposed, but a higher number can be programmed.

Figure 4: Flowchart of the module for fuzzy control in

PLC.

Figure 5: Points required to define the Trapezoidal

membership function.

Each one of these rules can incorporate all of the

input variables that have been defined. In addition,

each rule has a weight factor associated, which can

vary between 0 and 1.

A FC, called “Rule definition”, carries out the

selection of the fuzzy subsets of the inputs according

to the user specifications to constitute the

antecedents or premises. To this aim, the user

indicates if an input must be included in each rule

using a 1 bit memory position.

Later, for each variable an integer number

defines the subset associated to such variable. Based

on this information, a multiplexer selects the

membership degree that corresponds to the defined

subset, generating the premises. The same procedure

is followed for the subsets associated to the output

variable to define the consequents. Figure 7

illustrates the multiplexation process for the rule 1,

were, µS1(V1) is the membership degree to the first

subset of the first variable.

3.1.3 Operations

The fuzzy logic operations can be chosen between

the intersection (AND) and the union (OR).

FuzzyControllerbasedonPLCS7-1200-ApplicationtoaServomotor

159

Figure 6: Flowchart for fuzzification of input variables.

Furthermore, there are two options to select the

method for the AND operation: Minimum and

Product. In the event of OR operation, the applied

method is Maximum. These selections are defined

using a bit position.

The procedures for performing these operations

are briefly described. In the case of AND operation

using minimum criterion, successive comparisons

between the membership degrees are performed

until obtaining the minimum value. When the

product method is chosen, the membership degrees

are multiplied (Figure 8). For the OR operation, the

maximum criterion is performed by means of

successive comparisons until reaching the maximum

membership degree.

3.1.4 Defuzzification

The programmed defuzzification method is the

centroid, also called center of gravity.

The defuzzified output signal is obtained

applying the following equation:



∑





∗









∑









(1)

where p

is the centre of the membership function of

the consequent of each rule, and A

(Ri)

is the surface

of such subset truncated by the membership degree

result of the premises of such rule. This last

procedure corresponds to the implication method

Minimum. This area is calculated as the surface of a

trapezoid, resulting very simple from the point of

view of computational resources. Figure 9 shows the

flow diagram of the defuzzification process.

Figure 7: Multiplexation for premises configuration.

4 FUZZY CONTROLLER FOR

SERVO SPEED CONTROL

The servomotor is a well-known first order system

so its speed control is considered an illustrative

process to test and validate the developed fuzzy

module.

It is necessary to clarify that the aim of this work

is not to design or optimize a controller for the

servomotor, but test and validate the FLC.

The membership functions and rules were

adjusted during trials with different input signals.

Simulink and Fuzzy Logic Toolbox of Matlab were

used at this stage. Once the FLC was tuned, it was

coded in the PLC program language.

The input signals are the voltages of reference or

set point speed and the error of the actual speed. The

output signal is generated by the FLC for the

servomotor to reach the desired speed.

The structure of the FLC has been made as

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simple as possible. The fuzzy controller is of

Mamdani type, the And method is Min, the

implication operator is Min, the Aggregation is Max

and the defuzzification strategy is the Centroid of

area. Triangular membership functions are adequate

for this application, so they have been used for input

and output variables. Membership functions for

speed set point, error signal and output variable are

presented in Figure 10. These variables have been

defined by means of 3 fuzzy subsets despite the fact

that the module is able to manage 5 subsets.

Input ranges goes from 0 to 8 V for the speed set

point. Although the interval where the servomotor

behaviour is lineal goes from 3 to 8 V. In the case of

the error signal, the input range is -8 to 8 V. The

range of output signal is 3.5 to 9 V. The fuzzy rules

that define the FLC behaviour are represented by

means of a matrix as can be seen in Table 1.

Figure 8: Flowchart of AND operation.

Table 1: Rules of the FLC.

Set point/Error

S1 S2 S3

S1 S1 S1 S2

S2 S1 S2 S3

S3 S2 S3 S3

Figure 9: Flowchart of defuzzification process.

Figure 10: Membership functions for: a) Speed set point,

b) Error signal, c) Output signal.

5 EXPERIMENTAL RESULTS

With the aim of evaluating fuzzy module behaviour,

a comparison with the FLC designed using the

Fuzzy Logic Toolbox of Matlab is carried out. This

way, a set of simulations has been developed in

Simulink using the scheme shown in Figure 11. The

FuzzyControllerbasedonPLCS7-1200-ApplicationtoaServomotor

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transfer function for the servomotor (Equation 2)

was experimentally obtained.

Figure 11: Simulink scheme to simulate a fuzzy controller

applied to the servomotor.

FDT

0.92

0.44s1

(2)

Figure 12 shows the servomotor response for

three steps with amplitudes of 4 and 6 V. In this

figure the set point speed is in black, while the

servomotor speed corresponds to the red line.

Figure 12: Servomotor speed response simulation applying

step input with amplitude: a) 4 V, b) 6 V.

Once the proposed FLC for controlling the

servomotor has been simulated, the next step

consists in testing the fuzzy module under real

conditions. To this aim, the FLC implemented in the

PLC has been applied to the servomotor for several

trials. Figure 13 contains a photograph of the whole

system connected in the laboratory.

Figure 13: Components of the system in the laboratory.

The HMI allows visualizing in real-time the

numerical and graphical evolution of the servomotor

speed as can be seen in Figure 14. The

corresponding colours to the variables are the same

of Figure 12. The step amplitudes are the same as

shown for the simulations, 4, 5.5 and 6 V.

Table 2 contains the steady-state error expressed

as a percentage for the most representative trials in

both of the situations, the simulation and the PLC

implementation.

Figure 14: Screens of the HMI showing the servomotor

speed response to step input with amplitude: a) 4 V, b) 6

As can be appreciated, the output signal

generated by the FLC leads the servo speed to the

desired value with an acceptable steady-state error

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and high stability. In both of the cases the errors

reached are small and the difference between them is

slight. The main cause of such difference is the

higher resolution and computational resources of

Simulink.

Table 2: Steady-state error comparison.

Vsetpoint

Steady-state error (%)

Simulink PLC

4 -3.5 -1.7

5 4.2 6.2

5.5 4.7 4.7

6 -1.1 2.8

On the view of these results we can conclude

two facts. On the one hand, it has been demonstrated

the ability of the developed controller to adjust the

servomotor speed to the required set point. On the

other hand, these data validate the module developed

to implement fuzzy controllers in the PLC s7-1200.

6 CONCLUSIONS

A software module to implement fuzzy controllers in

a Siemens PLC s7-1200 has been presented. A

servomotor has been used as test platform to validate

the developed PLC-Fuzzy Controller.

The results under real operating conditions

constitute a proof-of-concept of the feasibility of the

proposed system.

A positive feature of the developed work is the

utilization of a PLC of recent market release and,

hence, progressive introduction in industrial plants

and research teams. This device belongs to Siemens

low-end performance range, providing automation

solutions with minor costs.

This work has contributed to a better

understanding of the abilities and procedures to

implement fuzzy controllers in PLC.

Future works focus on the application of the

controller to more complex systems such as a

hydrogen generator integrated in a hybrid renewable

energy system. Also, its integration with software

applications using OPC protocol and the

programming of more options such as fuzzy PID

structure are under study.

ACKNOWLEDGEMENTS

Authors are grateful to the University of

Extremadura and to the Gobierno de Extremadura

for their financial support by grant GR10157 and

FEDER (Fondo Europeo de Desarrollo Regional:

Una Manera de Hacer Europa).

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