Fuzzy-PID System Control for Tungsten Filament Heater of Thermal

Vacuum Evaporation

Dhidik Prastiyanto, Andi Suhono, Anis Fikri Azhari, Sugeng Purbawanto and Ulfah Mediati Arief

Department of Electrical Engineering, Universitas Negeri Semarang, E11 Building Kampus Sekaran Gunungpati

Semarang Indonesia

Keywords: Thermal vacuum evaporator, Fuzzy-PID controller, Heater control, Material deposition

Abstract: Vacuum coating is a solid material deposition technology that uses vacuum combustion using tungsten

filaments. The quality of material deposition depends on the heat distributed by the tungsten filament to the

material to be deposited. In this work, PID and Fuzzy-PID controller to control the heat of tungsten filament

is simulated. The Tungsten model is represented with transfer function in s-domain. Optimization of Fuzzy-

PID controller is done to obtain optimal heat of tungsten filament. Step and ramp responses of PID and PID-

Fuzzy controlled system were investigated. The result shows that the Fuzzy-PID reach faster steady state

compare to the PID system in the step response. The Fuzzy-PID also shows better performance in the ramp

response.

1 INTRODUCTION

Thermal evaporator is widely used for material

deposition (Khan et al., 2017). The challenge in

designing thermal evaporator is optimizing the heat

control so that uniform distribution of heat transfer

can be obtained. Automatic heater control can be

obtained by implementation of, Proportional Integral

Differential control (Asraf, Dalila, Hakim, & Hon,

2017), Fuzzy (Kobersi, Finaev, Almasani, & Abdo,

2013) (Singhala, Shah, & Patel, 2014), Adaptive

neuro fuzzy inference system ANFIS (Huang et al.,

2018) (Atia & El-madany, 2016) (Premkumar &

Manikandan, 2014), Genetic Algorithm and other

robust control system. PID controller is widely used

in many industrial applications for the effectiveness,

simplicity and clear functionality. However,

conventional PID is very sensitive with system

uncertainties so that the performance can decrease

with this problem. Optimization of PID parameter is

currently still an interesting topic (Kumar & Kumar,

2017) (Jung, Leu, Do, Kim, & Choi, 2015). Many

algorithms have been combined with PID such as

fuzzy and genetic algorithm.

In order to obtain fast and stable PID,

Fuzzy is implemented for optimizing PID parameter

(Ochoa & Forero, 2018) (Liu, Pan, & Xue, 2015)

(Lal, Barisal, & Tripathy, 2018) . The advantage of

Fuzzy that can solve some uncertainty of the system

is tried to be combined with the PID (Sahu, Pati,

Mohanty, & Panda, 2015). Fuzzy PID is also has been

also simulated in control system as in motor control

(Choi, Yun, & Kim, 2013) (Jung et al., 2015)[Choi

2015] and heater control (Ochoa & Forero, 2018)

(Atia & El-madany, 2016) .

Simulation of heater control using adaptive

PID have been done in (Ochoa & Forero, 2018). The

result shows that steady state is reached at arounf 30

s. Conventional structure of PID can be expanded

with the new algorithm (Liu et al., 2015) (Kumar &

Kumar, 2017). Some problem is still arise in this

hibryd PID due to the system uncertainties in the

experiment. Gradient descent algorithm was

implemented in (Kumar & Kumar, 2017). However

implementation in the experimental still need some

improvement.

388

Prastiyanto, D., Suhono, A., Azhari, A., Purbawanto, S. and Arief, U.

Fuzzy-PID System Control for Tungsten Filament Heater of Thermal Vacuum Evaporation.

DOI: 10.5220/0009011903880392

In Proceedings of the 7th Engineering International Conference on Education, Concept and Application on Green Technology (EIC 2018), pages 388-392

ISBN: 978-989-758-411-4

Figure 1: Fuzzy-PID control system.

In this work the Fuzzy PID is optimized for

heating system which is like in (Ochoa & Forero,

2018). The difference is that the characteristic of the

heater will be varied in order to test the adaptability

of the control system. By testing the proposed control

system for different characteristic of heaters, the

parameter will be optimized for different behaviour

of the system. The optimal parameter that works for

different system will overcome the uncertaintes of the

system in experiment. In order to increase the

performance of the control system, The characteristic

of the heater will also be confirm with the experiment.

Some adjusment will be needed in order to overcome

the system uncertanties which is omitted in

simulation.

2 METHODS

The simulation model of system which is controlled

using PID-Fuzzy is shown in the Figure 1. The system

is controlled using PID controller. The parameter of

PID is tuned using fuzzy logic controller. As an input

of the fuzzy are the error and delayed error. The

system is tested using step signal and also ramp signal

to analyse the step and ramp response of the system.

The detail model is explained in the following

subsection:

2.1 Evaporator Model

As we know in the system theory, any plant can be

modelled using transfer function. The transfer

function is got from the Laplace transform of the

differential equation of the system. The model of

evaporator and heater which is used in this work is the

same with the model in (Ochoa & Forero, 2018):













1





(1)

With K, T and transport delay d are 1, 11 and 1.8 then

the equation 1 become:











111



.

(2)

The value of K, T and d depends on the heater or

evaporator that to be modelled.

2.2 PID Controller

PID controller is the most common controller in

industry.











































(3)

The output of PID is driven by the error of reference

compared to the output of the system as stated in

equation 3. The PID parameters will influence the

performance of the control system.

In MATLAB PID can simply applied with

toolbox or can be applied with the following block

diagram as shown in Figure 2.

Figure 2: Block diagram of PID in Simulink.

Fuzzy-PID System Control for Tungsten Filament Heater of Thermal Vacuum Evaporation

389

2.3 Fuzzy Controller

Fuzzy inference system works based on set of rules

which is called as membership function. The

membership function set the relationship between

input and output of the fuzzy controller

(Vasičkaninová, Bakošová, Mészáros, & Oravec,

2015). Membership function in this works consists of

two input which is error and derivative of error. The

output will be Kp, Ki and Kd which are proportional,

integral and differential constants. In (Liu et al., 2015)

the output are PID parameters and additional

parameter od integration order  and derivative order

µ. The membership function of input is shown in

the Figure 3. The input is classified into negative big

(NB), negative medium (NM), negative small (NS),

zero (ZE), positive small (PS) positive medium (PM)

and positive big (PB).

Figure 3: Membership function plot of input.

Membership function of output is shown in Figure 4. The

output is classified as zero (ZE) medium small (MS), small

(S), medium (M), big (B), medium big (MB) and very big

(VB)

Figure 4: Membership function plot of input.

The relationship between input e and D_ and output Kp, Ki

and Kd are presented in Figure 5, Figure 6, and Figure 7

respectively. The error and derivative or error decide the

magnitude of theses PID Parameters as shown in the surface

views.

Figure 5: Surface view of input - output Kp.

The error and derivative of error as input of fuzzy will be

processed with FIS to decide the output of the Fuzzy

controller.

Figure 6: Surface view of input - output Ki.

Figure 7: Surface view of input - output Kd.

The membership function might be optimized in order to

get the fast response and stability of the control system.

EIC 2018 - The 7th Engineering International Conference (EIC), Engineering International Conference on Education, Concept and

Application on Green Technology

390

3 RESULTS AND DISCUSSION

Step response is investigation of the response of the

system with step input. This response is very

important because usually, thermal evaporator is

applied with the constant high temperature depends

on the material. In the Figure 8, step response of the

PID controlled system is presented:

Figure 8: Step response of PID controlled system.

The system needs time to reach desired

temperature. In the figure the step response show that

the rise time is around 6 s and settling time more than

10 s respectively. It is faster compared to the result in

(Ochoa & Forero, 2018) which is reached at around

20 s.

The response of the system with ramp signal

input is presented in Figure 9. The ramp response is

analysed to observe the behaviour of the system if the

user wants to get the increased temperature of the

thermal evaporator

Figure 9: Ramp response of PID controlled system.

The result show that the output is not able to reach the

increased desired temperature. This overshoot might

be overcome by increasing the value of derivative

constant.

Step response of Fuzzy-PID control system is

presented in Figure 10.

Figure 10: Ramp response of PID controlled system.

The figure shows that there is some overshoot before

the system reach steady state. The rise time of the

control system is approximately 0.6 s which is very

fast compared to (Asraf et al., 2017) and (Ochoa &

Forero, 2018). The settling time is around 7 s which

are faster than conventional PID and the settling times

in (Asraf et al., 2017) and (Ochoa & Forero, 2018).

However the overshoot is bigger compare the result

in (Ochoa & Forero, 2018).

The ramp response of Fuzzy-PID control

system is shown in Figure 11.

Figure 11: Ramp response of PID controlled system.

Comparing with PID control, the Fuzzy-PID can

control the system with ramp signal as desired

temperature. In the thermal evaporator, the increasing

temperature to some constant value is important so

that the high temperature value can be reached faster.

012345678910

time (s)

0.2

0.4

0.6

0.8

1.2

temperature (C)

Simulasi PID Dengan Step Respon

reference

output

012345678910

time (s)

temperature (C)

Simulasi PID Dengan Ramp Respon

reference

output

temperature (C)

012345678910

time (s)

-2

temperature (C)

Fuzzy PID Control Simulation

reference

output

Fuzzy-PID System Control for Tungsten Filament Heater of Thermal Vacuum Evaporation

391

4 CONCLUSIONS

Simulation of control system for evaporator have

been done. The result shows that PID-fuzzy reach

faster rise and settling time on the step response

compare to the PID control system. The ramp

response show that the control system is able to adjust

the output in-line with the desired temperature. The

future work is finishing experimental setup which

implement the simulation result and do experiment of

deposition with the thermal evaporator.

ACKNOWLEDGEMENTS

We would like to thanks to Faculty of Engineering

UNNES for providing research grant for this work.

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Application on Green Technology

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