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Al-Khwarizmi 

Engineering   
Journal 

 

Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, September, (2018) 

P.P. 129- 140 

 

 

Modified Elman Neural-PID Controller Design for DC-DC Buck 

Converter System Based on Dolphin Echolocation Optimization 
 

Khulood E. Dagher 

Al-Khwarizmi Collage of Engineering /University of Baghdad/ Baghdad/ Iraq 

dagherkhulood@kecbu.uobagdad.edu.iq :Email  

 
(Received 25 September 2017; accepted 21 January 2018) 

https://doi.org/10.22153/kej.2018.01.009 

 

 

Abstract 

This paper describes a new proposed structure of the Proportional Integral Derivative (PID) controller based on 

modified Elman neural network for the DC-DC buck converter system which is used in battery operation of the portable 

devices. The Dolphin Echolocation Optimization (DEO) algorithm is considered as a perfect on-line tuning technique 

therefore, it was used for tuning and obtaining the parameters of the modified Elman neural-PID controller to avoid the 

local minimum problem during learning the proposed controller. Simulation results show that the best weight 

parameters of the proposed controller, which are taken from the DEO, lead to find the best action and unsaturated state 

that will stabilize the Buck converter system performance and achieve the desired output. In addition, there is a 

minimization for the tracking voltage error to zero value of the Buck converter output, especially when changing a load 

resistance by 10%.  

 

Keywords: Buck Converter, Dolphin Echolocation, Modified Elman Neural PID Controller, On-Line-Tuning 

Optimization.   
 

 

1. Introduction 
 

DC-DC buck convertor is a very versatile 

electronic circuit due to its essential use in battery 

operations for different applications such as 

computer systems, portable devices, office 

equipment and any other devices that need to get 

an output voltage more or less than the input 

voltage. Buck convertors acquired their name 

from the reality of their work, where the 

amplitude of input voltage is bucked/chopped or 

decreased to get the output voltage [1]. In fact, the 

optimal or near optimal control of the attenuation 

operation is not easy and this is because of the 

load variation thus, we can find various researches 

that solve this problem and the backbone for these 

researches is the famous PID controller which is 

well known by its structure simplicity, time 

domain regulation and good closed loop response 

[2]. Hence, many optimization methods are 

developed to find the control gain parameters for 

the PID controller that is used to control the buck 

convertor, and some of these methods are: [3] 

presented a fuzzy PID controller with no 

defuzzification module. In [4] the authors 

proposed a new adaptive method based on 

simulated annealing algorithm to control the 

output voltage of the convertor.  

In [5] the authors proposed an on-line tuning 

method based on particle swarm optimization and 

they show high control performance. In addition 

to that, the genetic algorithm as a tuning control 

algorithm for buck converter is used as in [6]. 

This paper presents a new structure of the 

controller that uses a modified Elman neural 

network in the form of the PID controller equation 

and the control parameters are tuned by an 

optimization algorithm named as Dolphin 

Echolocation Optimization DEO algorithm in 

order to get fast, accurate and robust control 

action for the DC buck converter. 



Khulood E. Dagher                       Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 129- 140 (2018) 

 

 

130 

DEO was presented by Ali in 2013 [7] and proved 

as the most powerful optimization meta-huristic 

method as compared with other evolutionary 

algorithms in terms of the number of function 

evaluations and in convergence toward reaching 

the optimal solution. 

The description of this paper is as follows: 

Section two describes the mathematical model of 

the DC-DC Buck converter. Section three 

explains the design of the proposed modified 

Elman Neural-PID controller. In section four, the 

Dolphin echolocation optimization algorithm is 

explained. Section five presents the simulation 

results of the proposed controller. Finally, the 

conclusions are explained in section six. 

 

 

2. Buck Converter Circuit Model 
 

In this work, an asynchronous Buck convertor 

model is used with two n-channel MOSFETs as 

shown in Figure (1) to make the output voltage 

Vout always less than the supply voltage Vs. 

This converter uses two controllable switches 

to achieve its operation rather than using one n-

channel MOSFET power switch and one power 

diode rectifier, thus it has a maximize conversion 

efficiency and fast switching transient  [4, 8]. 

 

 
 

Fig. 1. Schematic diagram of Synchronous Buck 

power stage [8]. 

 

 

In general, the operation of the Buck converter 

depends on Q1 of the MOS switch and Q2 of the 

MOS synchronous rectifier. The parameter values 

of the buck convertor taken from [3, 4] is shown 

in Table 1.  

Now to drive and analyze the mathematical model 

of the buck convertor two operation time have to 

be considered depending on two MOSFETs (Q1 

and Q2) [3, 4] as follows:  

• At period of time t<T1, Switching ON Q1 and 
switching OFF Q2: 

The circuit analysis can be illustrated by applying 

Kirshoff’s voltage law as follows: 

0=−⋅−−⋅−
OUTLL

L
onLS

Vri
dt

di
LrsiV

       
…(1) 

Where VS: is the supply voltage; iL: is the 

inductor current; rson: is the on n-channel 

resistance of the MOSFET; rL: is inductor 

effective series resistance; Vout: is the output 

voltage of the circuit. 

At period of time T1<t<T2, Switching OFF Q1 

and switching ON Q2: 

The circuit analysis can be illustrated by applying 

Kirshoff’s voltage law as follows: 

0=−⋅−−⋅−
OUTLL

L
onL

Vri
dt

di
Lrsi        …(2) 

By defining ∆ as Eq. (3), as a switching function 
to turn VSupply ON and OFF: 

21

1
0

0

1

TtT

Tt

〈〈

〈〈





=∆            …(3) 

So the mathematical model (Eq. (1) and Eq. 

(2)) can be written in state space representation as 

follows: 

Let:  

x1= iL,  ��� =
dt

di
L  , x2=VOUT ,  ��� =

dt

dV
OUT

     
…(4) 

��� = �� + 	
��� 
                                                                           … (5) � = ���� ������ ���� =
� �(�������)� ����(�������)(��� ��� )��(�����) �(������ )��(����� ) �                                   …(6) 
	 =  !�!�" = �

∆�∆(������ )��(�����)
�                                  …(7) 

� = $0 1'                                                                                     …(8) 
In this work, the Buck converter power circuit 

specifications at a frequency of 80 kHz, which are 

taken from [3, 4], are listed in Table 1. 

 
Table 1, 

The parameter values of the Buck converter model 

[3, 4]. 

Description Symbol Value Unit 

Inductance  L 47 µ H 

Capacitor C 68 µ F 

Load Resistance LoadR 2.345 Ω 
Inductor Effective 

Series Resistance 
Lr 0.13 Ω 

Capacitor Effective 

Series Resistance  
Cr 55 mΩ 

The ON-n-Channel 

Resistance of MOS 

Transistors 
onrs 2.1 Ω 

Supply Voltage SupplyV 3.75 V 

 

 

L

0

Vout

Rloa d
Q2

1

2
3

Vsupply

rl

Q1

1

2 3

rc

0

C

 

rL 



Khulood E. Dagher                       Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 129- 140 (2018) 

 

 

131 

3. Modified Elman Neural PID Controller 
Design 

 
The proposed structure of the closed loop Buck 

converter PID controller based on a modified 

Elman neural network technique is shown in 

Figure (2). 
 

 

 

 

 

 

 

 

 

 

 

 

 

 
Fig. 2. The structure of MENN-PID controller for 

Buck converter equation model. 
 

 

The on-line tune control action of MENN-PID 

is very essential for stabilizing the error voltage 

signal of the output of the Buck converter system 

when the actual output voltage is drifting from the 

desired voltage and to give a high performance 

evaluation with disturbance rejection. The PID 

control equation in the time domain form is given 

by Eq. (9) [8, 9]. 

dt

tde
ktdtektektu

d

t

ip

)(
)()()()(

0

++= ∫                  …(9) 

where: kp is the proportional gain; ki is the integral 

gain; kd is the derivative gain; u(t) is the control 

action and e(t) is the error signal. 

Figure 3 shows the new structure of the MENN-

PID controller which is constructed from four 

layers each of which has its own operation as 

explained below [10]: 

• Input Layer: it works as a buffer i.e. pass the 
data without any modification. 

• Hidden layer: it is the active layer with the 
non-linear activation functions.  

• Context layer: it is a memory layer without 
activation functions. 

• Output layer: it represents a linear collector 
unit which adds all fed signals. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

 

 

 

 

 

 

Fig. 3. The new structure of MENN-PID controller. 

 

 

The network weights notation are: kp, ki, and 

kd hidden layers weights.Vc :Context layers 

weight. 

Li : Linear node as scaling factor and it is equal 

to 1. H : Sigmoid nonlinear node. )(kh
o

c
: 

Context unit output. )(kh : Hidden unit. α : Self-

connections feedback gain, which is represented 

randomly between (0 to 1). β : Connection 

weight from the hidden layer to the context layer 

and it is represented randomly between (0 and 1). 

e(k): Input error signal. u(k): Control action 

signal.  

The proposed control law of the MENN-PID 

controller for the Buck converter system is as 

follows:  

)()( −×= hLiku
                    …

(10) 

For the output h(-) of the neural network and it 

uses a sigmoid activation function as in equation 

(11) [9]: 

1
1

2
)(

)(
−

+
=−

−− net
e

h                       …(11) 

)( −net is calculated from equations (12) and 

(13):  

))(())1()((

)1()(()((()(

khVckekekd

kekekikekpnet

o

c
×+−−×+

−−×+×=−
  …(12) 

Where: 

)1()1()( −+−= kukhkh
o

c

o

c
βα

          …
(13) 

The new proposed MENN-PID has considerable 

properties which can be summarized by : 

• Fast learning, high adaptation performance, 
and high order control performance due to the 

context units in MENN which memorize the 

previous activations of the hidden units. 

Desired 

Voltage 

MENN-

PID 

Controller 

Control     Parameters 

e(k) 

 

 +      
  

- 

 

u(k)  Buck 
Converter 

Eq. Model 

(k)outV   

DEO Tuning 

Controller  

 

Vc

  

c
o

h

  

)(ku

  
h

  

α 

  

Hidden Layer 

 

H 

Input Layer  

)(ke  

 

Context layer  

β

 

L

Output Layer 

)1()( −− keke

 

)1()( −+ keke  

kp 

ki 

kd 

)1( −ku

  
Z-

1 



Khulood E. Dagher                       Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 129- 140 (2018) 

 

 

132 

• Good dynamic characteristic, no output 
oscillation and strong robustness performance due 

to the self-connection in the context units which 

increase the order of the controller model. 

The near optimal weights of the MENN-PID 

controller kp, ki, kd, and Vc will be updated on-

line by using Dolphin Echolocation Optimization 

algorithm as explained in the next section. 

 

 

4. Dolphin Echolocation Optimization 
Algorithm 

 
Dolphin echo optimization is a recent discrete 

meta-heuristic optimization algorithm developed 

by Ali Kaveh [7]. They inspired it from the fact 

that the dolphins take the advantages of 

echolocation to discover the environment and 

hunt prey in nature the probability of dolphin 

hunting is increased every time till it gets the 

prey. The DEO simulates the process of hunting 

thus, the algorithm proportionally limits 

(decreases) the exploration space with respect to 

the target’s distance by controlling the index of 

convergence factor CF which is defined as the 

average probability of the best answer. Simply the 

DEO algorithm divides it work into two phases: in 

the first phase the algorithm performs a global 

search and this means that the DEO explores all 

around the search space by exploring some 

random locations and in the second phase, the 

DEO concentrates the search around better results 

achieved in the first phase i.e. the algorithm goes 

gradually to the local search in a user defined 

curve. It is very important to mention here that the 

probability of getting the near optimal solution is 

increased in each step ahead till reaching the 

target [7, 11].  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

Fig. 4. The flowchart of DEO. 
 

 

The typical flowchart of the DEO algorithm is 

shown in figure (4) and the steps of the tuning 

algorithm are as follows: 
 

Step One “Initialization” 

This step contains the initialization for the 

following: 

• Random Location Matrix L with dimension 
L[-]NLxNV where: NL :number of location; NV 

number of variable which are kp, ki, kd and Vd in 

the proposed controller design. 

Calculate the Accumulative 

Fitness AF for the alternatives and 

its neighborhood according to the 

DEO rule. 

Yes 

No 
Stop 

criteria  

stop  

Initialize the following:  

Probability equation. 

Dolphins random location. 

Calculate the fitness 

Use the Probability to choose the 

next loop location 

 

Find the best location  

Calculate the Probability based on AF 

Set the following: 

Best location probability=Predefined  

Probability in the current loop 

Distribute the other probability between the 

other alternatives 



Khulood E. Dagher                       Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 129- 140 (2018) 

 

 

133 

• Alternative matrix with dimension [MA×NV] 
where MA is maximum Alternative number in the 

search space with ascending order form. 

• Maximum loops number N:number of loops ith 

in which the algorithm should reach to the target. 
 

Step Two “CF predefining and finding” 
Set PP1 =0.1which represent the convergence 

factor of the randomly selected location in the 

first loop then use it to find the predefined 

probability PP according to the equation (14) [7]: 

PP(Loopi)=PP1+0.1((Loopi-1)                      …(14) 

Step Three  “Fitness calculation”  

Fitness(Li)=
�()*(+,)�-                                   …(15) 

where . > 0, 
MSE: Mean Square Error of location is 

MSE(Li)=
�0 ∑ (
234 − 
678)�09:�                …(16) 

 

Step Four “Accumulated Fitness AF 

computation” 
In this step, the search space (alternative matrix) 

has to be divided into two regions, including an 

affected region within the affected radius Re and 

the not affected region. The recommendation says 

that Re is a quadric search space. 

Now:  

• Find the position of the location L(i,j) in the jth  
column of the alternative and put it in a vector 

named A. 

note: In Re, the accumulative fitness of the 

alternative A’s neighbors is affected from its 

fitness then calculate the AF for each jth variable 

in L(i,j) location by using the dolphin equation 

given below [7]: �;(� + �)9�� = �;(� + �)9 +

<=>
=? 0 � < A6B3C 
�E73F9GHI��(�9)�I A6B3C 
�E73 < � ≤ A(K, M)F9GHI��(�9)�I0 A(K, M) < � ≤ NOO3C 
�E73� > NOO3C 
�E73

…(17) 

Where: NOO3C 
�E73 = A(K, M) + P3                     …(18) A6B3C 
�E73 = A(K, M) − P3                     …(19) 
A+x<0and A+x>L(Aj) (length of Aj ) are not 

valid. 
 

Step Five   “Best location finding” 
Best location will have best AF thus, here 

terminate algorithm if termination criteria e ≤ 
esmall is satisfied, else: 

• Find the alternative assigned to the variable of 
best location 

• Let AF for best location alternative=0 
 

Step Six” probability determination and 

allocation” 

• Determine the probability as equation (20). 

P(i,j) =
QR,S∑ QR,STUVWXY                                               …(20) 

• Assign probability equals to PP for all 
variables of the best location (P(i,j)=PP) and 

P(i,j)=(1-PPloopi)P(i,j) 
 

Step Seven “Next loop location selection” 
Update location value with respect to the allocated 

probability of its alternative. 
 

Step Eight “Repetition” 

Repeat the steps two to seven till the maximum 

number of iterations is satisfied. 

In this work, the steps of the on-line DEO 

algorithm for finding and tuning control 

parameters of the proposed MENN-PID controller 

are repeated at 3.6 µ second (sampling time) for 

each sample based on Shannon theorem. 

 

 

5. Matlab Simulation Results 
 

The on-line Dolphin Echolocation 

optimization tuning control algorithm for the 

proposed MENN-PID controller is carried out by 

using the MATLAB simulation package in order 

to achieve the reference output voltage for the DC 

Buck converter system. By applying the Shanon 

theorem, the sampling time of the Buck converter 

system is equal to 3.6 µ sec based on the time 

constant of the system which is equal to 

τ=36.5µsec depending on the natural frequency 
wn=2.44×10+4 rad/sec and the damping ratio ζ= 
1.12 of the Buck converter system that have been 

calculated from Eq. (6). 

Figure 5 shows the unit step change open loop 

response for the output voltage of  Buck converter 

system which has stable response. 
 

 
Fig. 5. Open loop response for output voltage of 

Buck converter system. 



Khulood E. Dagher                       Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 129- 140 (2018) 

 

 

134 

In this work, to investigate the MENN-PID 

controller as shown in Figure 2 with DEO tuning 

control methodology which has a capability of 

generating optimal voltage control action and 

minimizing the voltage error with minimum 

number of fitness evaluation, the parameters of 

the control methodology based on Dolphin 

algorithm are defined in Table 2. 

The number of locations, variables and values of 

kp, ki, kd, Vc are selected by using try and error 

method based on best results that have been 

obtained. 

   
Table 2, 

The parameters of DEO algorithm. 

The number of locations NL=25 

The number of variables NV=4 

The best range values of 

kp weight 

kp=[0.1, 0.2, 0.3, 0.4, 

0.5, 0.6  to 4] 

The best range values of 

ki weight 

ki=[0.025, 0.05, 0.075, 

0.1 to 1] 

The best range values of 

kd weight 

kd=[0.0125, 0.025, 

0.0375 to 0.5] 

The best range values of 

Vc weight 

Vc=[0.075, 0.15, 0.225, 

0.3 to 3] 

The number of 

alternative matrix length 
MA=40 

The number of loop N= 10 

The affected radius Re=±10 

The minimum error value eSmall=0.001 

 

 

The simulation results of the closed loop 

voltage control system with the variable step 

change as (2, 2.5 and 3) volt in the Buck 

converter’s reference output voltage with on-line 

tuning MENN-PID controller with initial output 

voltage of the system equals to zero volt can be 

shown in Figures (6-a, b, c). 

The output voltage response of the Buck 

converter system in the variable step change was 

very small over-shoot in the transient state while 

at steady-state, the error was equal to zero value 

in each step change, as shown in Figure (6-a) 

when we compared with another controller results 

as in [4]. 

 In Figure (6-b), the voltage error signal of the 

closed loop Buck converter controller system was 

a small value in the transient and it has become 

zero at the steady state. The action response of the 

MENN-PID controller was smooth without 

oscillation response, no spikes behavior and the 

action did not reach to the saturation state of 3.75 

volt depending on the supply voltage. Figure (7) 

clearly shows the improved performance index of 

the Buck system based on the Mean Square Error 

(MSE) for the on-line tuning Dolphin control 

methodology at 300 samples. 

Figures (8-a, b, c, d) show the parameters of the 

MENN-PID controller kp, ki, kd and Vc which 

have been tuned on-line based on Dolphin 

algorithm at each sample. 

In this on-line DEO algorithm, at sample 80 for 

example, the Accumulative fitness of the kp, ki, 

kd, Vc control gains in the 10th loop or (final loop) 

of the algorithm can be shown in Figure (9-

a,b,c,d) respectively. 

 

(a)  

 
(b) 

 
(c) 

 
 

Fig. 6. Simulation results of the adaptive PID 

controller: (a) Output voltage for Buck converter 

model; (b) Voltage error; (c) Voltage control action. 

 

50 100 150 200 250 300
0

0.5

1

1.5

2

2.5

3

3.5

Samples

B
u

c
k

 C
o

n
v

e
r

te
r

 O
u

tp
u

t 
V

o
lt

a
g

e
 (

v
o

lt
)

 

 

Desired Output Actual Output Voltage

50 100 150 200 250 300
-0.5

0

0.5

1

1.5

2

Samples

E
r

r
o

r
 V

o
lt

a
g

e
 S

ig
n

a
l 

(v
o

lt
)

50 100 150 200 250 300
0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Samples

V
o

lt
a

g
e

 C
o

n
tr

o
l 

A
c

ti
o

n
 (

v
o

lt
)



Khulood E. Dagher                       Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 129- 140 (2018) 

 

 

135 

 
 

Fig. 7. On-line performance index (MSE). 

 

 
To verify that the closed loop control Buck 

converter system has a robust performance for 

boundary disturbance through adding variable 

load resistance by decreasing 10% from its value 

at sample 100 (0.36 msec), the output voltage 

response of the Buck converter system to a step 

change 1.75 volt has followed the reference 

voltage at sample 50 (0.18 msec), the output 

voltage response of the Buck converter system to 

a step change 1.75 volt is followed the reference 

voltage at sample 50 (0.18 msec) with no 

overshoot at transit response and robustness 

response during adding the disturbance as shown 

in Figure (10-a), the output voltage of the Buck 

converter system did not accessed ±0.05 volt from 

the reference output voltage and very small over-

shoot in the transit response then at steady-state 

response the error voltage signal was equal to zero 

value. 

 

(a)  

 
 

 

 

 

 

 

 
 

(b)  
  

 
(c) 

 
(d) 

 
 

Fig. 8. Simulation results of the control parameters: 

(a) kp; (b) ki; (c) kd and (d) Vc. 

 

 
The small value of the error voltage signal 

between the reference voltage and the output 

voltage of the Buck converter system is shown in 

Figure (10-b). Figure (10-c) shows the response of 

the control action which has a fast and strong 

adaptability with robustness performance because 

the control parameters are learned and tuned by a 

powerful on-line Dolphin algorithm especially 

when adding boundary disturbance to show the 

control action has a capability to track the error 

voltage signal of the Buck converter system to 

follow the reference voltage as step change and 

reduce the effect of the load resistance 

disturbance. 

 

50 100 150 200 250 300
0

1

2

3

4

5

6

7

8
x 10

-3

Samples 

O
n

-L
in

e
 C

o
st

 F
u

n
c

ti
o

n
 M

S
E

50 100 150 200 250 300
0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

Samples

k
p

 C
o

n
tr

o
l 

G
a

in

0 50 100 150 200 250 300
0.05

0.1

0.15

0.2

0.25

Samples

k
i 

C
o

n
tr

o
l 

G
a

in

50 100 150 200 250 300

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Samples

k
d

 C
o

n
tr

o
l 

G
a

in

50 100 150 200 250 300
1.15

1.2

1.25

1.3

1.35

1.4

1.45

1.5

1.55

1.6

Samples

V
c

 C
o

n
tr

o
l 

G
a

in



Khulood E. Dagher                       Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 129- 140 (2018) 

 

 

136 

(a)  

 

(b) 

 

(c) 

 
 

 

 (d)  
 

 
 

Fig. 9. The Accumulative fitness of the control 

gains: a) kp, b)  ki, c)  kd, d) Vc. 

 

 

Figure 11 shows the cost function of the on-

line tuning Dolphin control algorithm is clearly 

improved the performance of the proposed 

MENN-PID controller for the Buck converter 

system especially at sample 100 during adding 

disturbance. 

The on-line tuning the parameters of the 

MENN-PID controller kp, ki, kd and Vc are 

shown in Figures (12-a, b,c, d) respectively which 

are found and tuned by using DEO algorithm in 

order to find the best voltage control action that 

depends on the initial parameters of the DEO 

algorithm. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.5 1 1.5 2 2.5 3 3.5 4
0

100

200

300

400

500

600

700

800

900

Alternative of kp Control Gain

A
c

c
u

m
u

la
ti

v
e

 F
it

n
e

ss
 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0

100

200

300

400

500

600

700

800

900

1000

Alternative of ki Control Gain

A
c

c
u

m
u

la
ti

v
e

 F
it

n
e

ss

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
0

100

200

300

400

500

600

700

800

900

1000

Alternative of kd Control Gain

A
c

c
u

m
u

la
ti

v
e

 F
it

n
e

ss

0 0.5 1 1.5 2 2.5 3
0

100

200

300

400

500

600

700

800

900

1000

Alternative of Vc Control Gain

A
c

c
u

m
u

la
ti

v
e

 F
it

n
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ss



Khulood E. Dagher                       Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 129- 140 (2018) 

 

137 

 

  

 (a) 
 

 
 

(b)  

 
 (c)  

 
 
Fig. 10. Simulation results of the MENN-PID controller with disturbance effect: (a) Output voltage for Buck 

converter model; (b) Voltage error; (c) Voltage control action. 

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Khulood E. Dagher                       Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 129- 140 (2018) 

 

138 

 

  

 
 

Fig. 11. On-line performance index (MSE) with 

disturbance effect.  

  

(a)  
  

 
(b)  

 
(c)  

 
 

 

 

 

(d)  

 
 

Fig. 12. Simulation results of the control 

parameters: (a) kp; (b) ki; (c) kd; (d) Vc. 

 

 

6. Conclusions 
 

The numerical simulation results of the 

proposed MENN-PID controller based on the 

DEO algorithm is presented in this paper for the 

DC-DC Buck converter system showing the 

following capabilities: 

• An on-line optimization was achieved for the 
control gains of the proposed controller. 

• A suitable voltage control action was obtained 
without a saturation voltage state. 

• Tracking an excellent reference voltage with a 
minimum voltage error was achieved. 

• Strong adaptability performance through 
changing the desired voltage level output. 

• High robustness performance when changing 
the load resistance as the disturbances effect to 

the Back converter system. 

 

 

7. References 
 
[1] S. Chander, P. Agarwal, I. Gupta, “ASIC and 

FPGA based DPWM Architectures for 

Single-Phase and Single-Output DC-DC 

Converter: A Review”. Central European  

Journal of Engineering. Vol. 19, No. 4, pp. 

620-643, (2013). 

[2] A. Emami, M. Poudeh, S. Eshtehardiha, 

“Particle Swarm Optimization for Improved 

Performance of PID Controller on Buck 

Converter”. The International Conference of  

IEEE on Mechatronics and  Automation. pp. 

520-524, (2008). 

[3] M. Garg, Y. Hote, M. Pathak, “Design and 

Performance Analysis of a PWM DC-DC 

Buck Converter Using PI-lead Compensator”. 

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1

2

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-3

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-L
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e
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S
E

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1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

Samples

k
p

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o

n
tr

o
l 

G
a

in

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0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

Samples

k
i 

C
o

n
tr

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l 

G
a

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0.04

0.05

0.06

0.07

0.08

0.09

0.1

0.11

Samples

k
d

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tr

o
l 

G
a

in

0 50 100 150 200 250 300

1.4

1.6

1.8

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2.2

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2.6

2.8

3

Samples

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Khulood E. Dagher                       Al-Khwarizmi Engineering Journal, Vol. 14, No. 3, P.P. 129- 140 (2018) 

 

139 

 

  

Arabian Journal for Science and Engineering . 

Vol. 40, No. 12, pp. 3607-3626, (2015). 

[4] M. Tapou, H. Al-Raweshidy, M. Abbod, M. 

Al-Kindi, “A Buck Converter for DVS 

Compatible Processors in Mobile Computing 

Applications Using Fuzzy Logic Implemented 

in a RISC Based Microcontroller”. The 2nd 

Internation Conference on Circuit and System 

Control Signal. Prague, Czech. pp. 135-139, 

(2011). 

[5] K-H Kim, B-S Kong, Y-H Jun, “Adaptive 

Frequency-Controlled Ultra-Fast Hysteretic 

Buck Converter for Portable Devices”. 

International Conference of IEEE on  SoC 

Design. pp. 5-8, (2012). 

[6] J. Chuang, H. Chou, “An Efficient Fast 

Response Hysteresis Buck Converter with 

Adaptive Synthetic Ripple Modulator”. The 

8th International Conference of IEEE on 

Power Electronics. pp. 620-627, (2011) 

[7] A. Kaveh and N. Farhoudi,”A New 

Optimization Method: Dolphin 

Echolocation”. Advances in Engineering 

Software. Vol. 59, pp. 53-70, (2013). 

[8] A. S. Al-Araji, “Development of an On-Line 

Self-Tuning FPGA-PID-PWM Control 

Algorithm Design for DC-DC Buck 

Converter in Mobile Applications”. 

Engineering Journal. Vol.  23, No. 8, pp. 84-

106, (2017). 

[9] K. E. Dagher and A. S.  Al-Araji, ”Design of 

a Nonlinear PID Neural Trajectory Tracking 

Controller for Mobile Robot based on 

Optimization Algorithm”. Engineering  & 

Technology Journal Vol. 32, No. 4, pp. 973-

986, (2014). 

[10] A. S. Al-Araji, M. F. Abbod and H. S. Al-

Raweshidy, “Design of a Neural Predictive 

Controller for Nonholonomic Mobile Robot 

Based on Posture Identifier”. Proceedings of 

the IASTED International Conference 

Intelligent Systems and Control (ISC 2011), 

Cambridge, United Kingdom, pp.198-207, 

(2011). 

[11] A. Kaveh and N. Farhoudi,”Dolphin 

Echolocation Optimization: Continuous 

Search Space”. Advances in Computational 

Design. Vol. 1, No. 2, pp. 175-194, (2016).  

 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

  

  

  

  

  

  

  

  

  



 )2018( 123-128، صفحة 3، العدد14دجلة الخوارزمي الهندسية المجلم                                          اسكندر داغرخلود 
 

140 

 

  

  

  

 ستمرالمتصميم مسيطر تناسبي تكاملي تفاضلي عصبي ايلمن المعدل لنظام محول خافض التيار 
  مبنيا على أساس أمثلية الدولفين لتحديد الموقع بالصدى

 

  خلود اسكندر داغر
  جامعة بغداد /هندسة الخوارزمي الكلية 

  dagherkhulood@kecbu.uobagdad.edu.iq :البريد االلكتروني
 

 
  

  الخالصة
  

فض التيار المحول الخا من لنظامباالعتماد على الشبكة العصبية المعدلة ايلإن هذا البحث يصف مقترح جديد لهيكلية المسيطر التناسبي التكاملي التفاضلي 
بشكل حي  بوطة للتنغيمقنية مضتهزة المتنقلة. إن الخوارزمية الدولفين االمثلية لتحديد الموقع بالصدى تعتبر المستمر والذي يستخدم في عمل البطارية لألج

ى هائيات الصغرع في النعناصر المسيطر التناسبي التكاملي التفاضلي العصبي ايلمن المعدل بدون الوقوومتصل لذلك تم استخدامها لتنغيم والحصول على 
لدولفين التي قادت إلى إيجاد أفضل عناصر للمسيطر المقترح تم أخذها من خوارزمية ا إن نتائج  المحاكاة أظهرت ان المحلية أثناء تعليم المسيطر المقترح.

 ،كإضافة إلى ذل لمرغوب.اإلشباع وان هذا الفعل أدى إلى استقرار أداء النظام المحول الخافض للتيار المستمر وتحقيق اإلخراج أفضل فعل وبدون حالة ا
       .%١٠ب  هنالك تقليل لتتابع خطأ إخراج فولتية الى قيمة الصفر لنظام المحول الخافض للتيار المستمر خصوصا عند تغيير ممانعة الحمل