Effect of fuzzy PID controller on feedback control systems based on wireless sensor network

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 3, June 2020, pp. 2416~2425
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i3.pp2416-2425  2416
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Effect of fuzzy PID controller on feedback control systems
based on wireless sensor network
Anas A. Hussien1
, Mehdi J. Marie2
, Khalaf S. Gaeid3
1
Department of Computer Engineering, Al-Nahrain University, Iraq
2
Ministry of Industry and Minerals, Iraq
3
Department of Electrical Engineering, Tikrit University, Iraq
Article Info ABSTRACT
Article history:
Received Jun 29, 2019
Revised Nov 16, 2019
Accepted Nov 26, 2019
Wireless Networked control system (WNCS) has an important in all aspects
of the life and in the research fields of Engineering. In this article, a real-time
implementation of the wireless feedback control system (WFCS) is
performed. The stability issue in the closed-loop control system still suffer
from noise, disturbances, and need careful considerations to handle it.
Three cases to discover the ability of a Fuzzy PID controller to maintain
better angular position control system (PCS) is addressed and controlled by
a personal computer through a wireless sensor network(WSN) constructed by
ZigBee platforms. The practical issues related with the design and
implementation of the wireless computerized control system (WCCS) is
discussed and analyzed. The simulation results carried out with Matlab/
Simulink 2018b. Different parameters effect such as maximum overshoot,
sampling frequency, distance and delay time have been studied. These effects
on overall system performance would be discussed. Improving the efficient
use of ZigBee platform for WFCS. The simulation and experimental results
prove the proposed algorithm in the field of wireless control system.
Keywords:
Delay time
Fuzzy logic
PID controller
Stability
WFCS
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Anas A. Hussien,
Department of Computer Engineering,
Al-Nahrain University,
Al Jadriyah, Baghdad, Iraq.
Email: anasali78@yahoo.com
1. INTRODUCTION
Wireless networked control system (WNCS) has a vital role in many aspects of engineering since
the late 1990s. The advantages of WNCS such as fewer cables, easy to developed, maintain and resource
sharing, according to nodes ability in the NCS is connected to network directly. Great advances in control
engineering, communication, smart communications telecommunications, and for all digital services [1, 2].
This leads to more sophisticated performance in the life. The system performance depends on the capabilities
of the sensors, switches, relays, and actuators. Smart systems has been developed to yield efficient tools in
wireless feedback control systems (WFCSs) such diagnosis, identification monitoring used for performance
evaluation. Hence the whole life will be enhanced. Another important issue leads to more efficient WCS is
the intersection of system engineering (control and communication), management and application domain
expertise as can shown in Figure 1.
Feedback control systems (FCS) is the control loops in which the feedback system are closed
through a real-time network and called networked control systems (NCS) which is used to reduce both cost
of the device and its weight [3]. Since the WNCS deal with the real-time control system it’s combined with
cyber-physical system (CPS) and Tactile Internet (TI) [4]. The objective of any FCS is to ensure that the FCS
has desirable output response with high accuracy, ability to handle the attenuation, high speed and zero
steady-state response as well [5].

Int J Elec & Comp Eng ISSN: 2088-8708 
Effect of fuzzy PID controller on feedback control systems based on wireless sensor… (Anas A. Hussien)
2417
Figure 1. Systems engineering improvement [6]
For the WNCS systems the developed performance specifications will ensure high connectivity
between the devices at same time, the reliability will be increased to reach better control and monitoring
during the operation period [7]. In the WNCS it’s difficult to send a signal to a single receiver or to avoid
receiving signals from the transmitters that have a power in the working region [8] and its consists of sensor
nodes in the network for collecting, processing, storing and transferring information [9]. FLC and PID
structure for H5 inverter topologies describing are observed in terms of settling time, stability, and harmonic
distortions presented in [10]. Takagi–Sugeno (T–S) fuzzy model with PID controller for stochastic
stabilization problem of uncertain WN control in the random network induced delays presented in [11].
Fuzzy Logic control in the field of Telecommunications at both the network and physical layers,
is presented [12]. Hence, the control is a central circuit for wireless control systems as can be shown in
Figure 2:
Figure 2. Hardware implemintation of the proposed work
Wireless sensor networks (WSNs), can be used in many application fields, such as security and
military, industry 4.0 architecture and the environment. The data connectivity between data delivery source
and the consumed energy circuits with high efficiency is the key factor in the network specifications [13, 14].
WFCSs research area is classified into Control of the network, Control over the network and Multi-agent
systems [15, 16]. The new devices system have to be a multi-layered, machine-(to-user, to–machine,
to cloud) server capability and a vast number of smart nodes have been introduced into WFCS system [17].
In the literature, there are a lot of works have been done in the WFCSs in the last two decades to solve
the problems in this field.

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 3, June 2020 : 2416 - 2425
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The WSN implementation based on ZigBee technology to monitor the history and current
temperature information is carried out by [18]. Robust proportional integral derivative (PID) Controllers for
time delay systems in WN for various uncertainties as a feedback method for controlling a plant with sensors
and actuators used. Communication delay occur due to a various parts of system [19]. WNCS with induced
delays and with modified packet compared to the conventional system to reduce number of transmissions is
presented in [20].
Investigation of the transmission of WCSs with relay of limited power resources system with
discrete-time is introduced into the algorithm for better efficiency in control and transmission [21].
The influence of packet loss on WFCS is important to design controllers to reduce the disturbance between
transmissions and the received information. The results proves that, the power spectrum density (PSD) of
the effect on the controller is not constant [22]. A proportional-integral-derivative (PID) controller for
controlling a set of wireless devices to form a WFCS that can affect the orientation of the system to desired
state is presented in [23]. Stability of WCS under Denial of Service (DoS) attacks has been investigated to
the modern concept of system stability during cyber-attack [24]. A cross-layer design for control and
communication layers for WFCS is analyzed, and an optimized receiver design for state feedback is
presented in [25]. The rest of this article is arranged as: in section II, the materials and methods is presented.
In section III, results and discussion is given. The article is concluded in section IV.
2. MATERIALS AND METHODS
The FCS communication and control algorithm designed through prototype test bed consisting of
a plant position control system (PCS). The plant type is very important in the actuators selection for
the position control of the systems [26]. Required torque response to the DC motor shaft, the actuator control
signal can change the system position precisely. Hence, fast and accurate torque control is obtained which are
the major objectives for the FWPCS [27].
Two kinds of torque control schemes are presented in the literatures either current or speed mode.
The current mode is realized and used due to its simplicity features [28, 29]. The FW motor is DC.
Its inductance is very small and can be negligible. This FWCS is sensitive to any torque disturbances in
the current control scheme. To enhance steady state response, the speed control scheme is used to improve
accuracy and reduce the effect of the disturbances. Most of the applications uses the speed control scheme.
The current scheme controller transfer function will be as in (1):
( )
( ) 1L
o i
i
T s
s s

 


(1)
The output torque response of the flywheel fellow the reference toque precisely when the value of
1i . The transfer function of speed scheme, from output torque (To) to flywheel torque (τL)
will be as in (2).
( )
( ) 1L
oT s
s s



 


(2)
The output torque response of the flywheel fellow the reference toque precisely when the value of
1 . The FLC implemented by the following steps:
 1. Modify the real value into fuzzy values (Fuzzification)
 2. Set of rules in this process (Knowledge base),
 3. Reasons application to compute the fuzzy output (Inference engine)
 4. Changing the fuzzy values into real and single values (Defuzzification)
In the FLC, there are two fuzzy inference system (FIS)”
 1. Sugeno which is obtain the output response according to its input
 2. Mamdani which is obtain the response of the process according to the membership function (MF) are
the main.
The error e (k) is difference between the input and output response and the change of the output
(y (k)-y (k-1)) are used as input signals to the FIS. The fuzzy PID controller is linear as can be
shown in Figure 3.

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Figure 3. Fuzzy PID network control system
According to Figure 3, the combination of the FL rule tables of Kp, Ki, Kd, the fuzzy FC model and
the modified PID parameters gain will be as in (3) :
[ , ]
[ , ]
[ , ]
K K error rateof errorp p p
K K error rateof errori i i
K K error rateof errord d d
 
 
 
(3)
The fuzzy truth table used in this paper as in the Table 1:
Table 1. Fuzzy truth table
Error(E)
Rate of Error(∆E)
NL N Z P
N NH NL Z
Z NL Z PL
P Z PL PH
where N, NL, Z, P, PL, PH is refers to negative, negative low, zero, positive, positive low and positive high
respectively.
The implementation of the control server of WFCS illustrated in Figure 2. The National instrument
(NI) equipment’s has been used in this model as analogue output monitoring. The NI data acquisition system
Dev3-6008 is used as well. Serial transmitter and receiver to send the required information with data
conversion from the int13 and doubled conversion to synchronize the information to ensure better matching
between any two circuits. The position control system is used with wireless controllers to measure the angle
of permanent-magnet DC motor. In the plant side, four wireless sensors and two wireless nodes
have implemented on a single-hop wireless network and another two wireless nodes at the controller side
used as well [30].
3. RESULTS AND ANALYSIS
The performance specifications such as time delay which is the time needed to reach half of
the output response, bandwidth and noises effect on the stability and of the WFCS. “It is well known that
a continuous time LTI system is BIBO stable if and only if the impulse response is absolutely integrable.
Equivalently, the necessary and sufficient condition for continuous time LTI system to be BIBO stable is that
all the poles of rational transfer function lie in the left half the s-plane (i.e. to the left of jω axis)” [31].
Rise time (tr) which is the time needed to reach 100% of the output response and its inversely proportional to
the bandwidth, maximum overshoot (Mp), steady state error (ESS) and settling time (ts) can be used to check
the stability of the system. Figure 4 illustrates the system implementation using Matlab2018b/Simulink [32].

 ISSN: 2088-8708
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Figure 4. Implementation of WFCS using simulink
2nd
order control system inertial plant has been investigated in this work with the transfer
function as in (4)
168
4.1
)( 2


ss
sGplant
(4)
The controller is the central unit of the WFCS as mentioned earlier and its can be found in
the controller (client) side to drive the DC motor to the required angular position. PID controller is used to
make the output fellow directly the reference input. The mathematical model of parallel PID
controller [33] as in (5)
s
KsKsK
sG
ipd 

2
)(
(5)
where K p, Ki, Kd refers to the proportional gain, the integral gain and the differential gain, respectively.
The P-controller where Kp=0.042, (Ki and Kd are equal to zero. This mean the PID transfer
function will be as in (6)
042.0G (6)
The output response of the WFCS system is shown in Figure 5. Figure 6 shows the error signal
which is control signal to the controller. Figure 7 shows the controller efforts of PID controller and FPID
controller. The high level effort related to the PID controller and lower control level due to FPID controller.
Figure 5. System response with P-controller

2421
Figure 6. Error (control) signal and residuals
between FPD and PID
Figure 7. Controller efforts
The PI-controller is used with (Kp=0.05, Ki=0.02 and Kd=0). The PID transfer function in this case
will be as in (7).
s
s
sG
02.005.0
)(


(7)
The output response is shown in Figure 8. It is clear that the SSE is reduced and the output fellow
the reference input but with some repeating overshoot in the transient region. The PID controller is used to
enhance the overall response of the system, PID parameters are tuned according to the Ziegler-Nichols and
particular swarm optimization methods. Figure 9 shows the system response with Kp=0.06, Ki=0.035 and
Kd=0.03.
Figure 8. System response with PI-controller
Figure 9. System response with PID-controller

 ISSN: 2088-8708
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The transfer function of PID controller in this case as in (8).
s
ss
sG
035.006.003.0
)(
2


(8)
The simulations results for final two case studies are summarized in graphical user interface (GUI)1,
as shown in Figure 10. Last case study performed by changing the distance between client and server
suddenly to check the output response. The response of the WFCS is illustrated in the Figure 11.
Figure 10. Performance specifications of the both controllers
Figure 11. Variable distance output response with PD controller
It had been noticed that after the instant k=1700 which corresponds to reaching to 15m, the output
response perturbation with an overshoot occur after this instant with 0.4msec sampling time. Periodogram of
the Welch power spectral density (PSD) calculated as a physical frequency function for given sampling
frequency (fs). To evaluate the performance of Welch method [34], the following expression is used to
estimate the performance of Welch method as in (9)
11
( ) ( )( )
0
kjw jw
P e P i ew m
ik

 
 (9)
where
K is the sequence of covered data points
Pm mean of the performance

2423
sf
f
w
2

(10)
f is the tonal frequency
The position of the maximum power results in the desired frequency.
Figure 12 shows the periodogram response for the output response during the operation for
the WFCS with classical PID controller and fuzzy PID controller (FPID). Its quit evidence to the stability of
the system due to oscillation frequency or decay appeared in the output response for the system with classical
PID controller and stable in the FPID controller system. The delay time (Td) is another important factor to be
investigated. The Td between client and server is checked according to the facilities of delay block putted in
different locations. Figure 13 shows the output response when the path between sensor and controller is
changed with Simulink delay block. It is obvious that, the stability is decreased and an overshoot is occur and
the overall performance is degraded. Finally the validation of 96.1% between the transmitting and receiving
information data of the WFCS for PID and 100% for the fuzzy PID controller (FPID) which ensure better
stability as can be shown in Figure 14.
Figure 12. Power spectral density
Figure 13. PID controller system response with time delay
Figure 14. Transmitting and receiving information

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4. CONCLUSION
WFCS plays important role and considered it as fundamental technology in control systems
applications. In this manuscript a prototype is implemented practically to investigate the main important
related to the stability and main lack parameters effects on the feasibility for applting WFCSs. The plant
control through FPID controller is best solution to ensure the stability compared to the classical PID
controller. Time delay between the client and server is effects the stability. The sufficient selection of the Kp,
Ki and Kd is pay vital role in the maintain the good performance specification of the WFCS. The estimated
PSD maintain the energy of the signal independently of the data type and length. The effect of noise and
distance and direction between the client and the server have been addressed and studied. Data collection
strategies from the plant using Matlab/Simulink blocks is proposed and implemented. High accuracy and fast
speed reaction between the transmitter and receiver in obtained. Finally,the most important literatures in
the field WFCS is highlighted.
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BIOGRAPHIES OF AUTHORS
Anas Ali Hussien received the B.Sc. in Electronics and Communications Engineering in 1999,
M.Sc. in Electronics and Communications Engineering / Electronic Circuits and Systems in 2001
and Ph.D. in Information Engineering in 2007, from Al-Nahrain University. He is an associate
professor since 2012 and head of the computer-engineering department. Dr Anas has published more
than 12 published papers all of them in reputed journals and conferences
Mehdi J. Marie was born in Baghdad, Iraq in 1970. He received his Bachelor's (1993), Master's
(2004) Degrees from University of Technology (Iraq) and Ph. D. from the University of Basrah
(2014). He has been a lecturer of Control Theory I, II, Electronics and Electrical Networks at the Al-
Nahrain University, College of Engineering. He is currently a senior engineer at Al-Zawaraa State
Company, Ministry of Industry and Minerals (Iraq). Dr. Mehdi had achieved over seven Journal
articles and two conference papers in the field of control and systems engineering. His current
research and development interests aremainly in the following areas: self-tuning control and system
identification, controllerdesign for linearand non-linear systems, industrial applications particularly
in the processing industry (for example refining, cement, plastic and food industry).
Khalaf S. Gaeid received the B.Sc. degree in electrical engineering/Control from MEC, Baghdad,
Iraq in 1993 and the M.Sc. degree in Control Engineering from University of Technology, Baghdad,
Iraq in 2004. He graduated from University of Malaya, Malaysia in 2012 with the PhD degree in
control system and machine drives. His doctoral researchwas in the development new fault
tolerant controller based wavelet for induction machines.He is currently assistant professor in
control systems at the Department of Electrical Engineering, College of Engineering, Tikrit
University, Tikrit, Iraq. His research interests include fault tolerant control, wavelet, fault
diagnosis,machine drives and their applications in electrical engineering.He is IEEE member
with a lot of publications in high rank journals and confrences

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