







































M. Safarishaal and M. Sarvi /Future Technology                                                               February 2024| Volume 03 | Issue 01 | Pages 
32-39 

32 

 

 

 

Article 

Optimizing weighted voltage mode control for 

enhanced output cross-regulation in multi-output 

DC/DC converters 
Masoud Safarishaal1*, Mohammad Sarvi2 

1Department of Electrical and Computer Engineering, University of Oklahoma, Norman, USA 
2Electrical Engineering Department, Iran University of Science and Technology, Tehran, Iran 

A R T I C L E   I N F O 
 

Article history: 
Received 18 April 2023  
Received in revised form 
20 May 2023 
Accepted 28 May 2023 
 
Keywords:  
Multiple outputs forward DC-DC converters, 
Imperialist competitive algorithm, 
Particle swarm optimization, 
Ant colony optimization, Weighting factor method 
  
*Corresponding author 
Email address: 
masoud.safari@ou.edu  
 
 
DOI: 10.55670/fpll.futech.3.1.4 
 

A B S T R A C T 
 

Weighted voltage mode control is a widely used method for regulating multiple 
output DC-DC converters, but inconsistent outcomes are often observed in 
design due to the complexity of the weighting variable optimization. This study 
proposes an optimization-based approach to accurately estimate the optimal 
weighting factors for improved output voltage regulation in multiple output 
forward DC-DC converters. Three optimization algorithms, the Imperialist 
Competitive Algorithm (ICA), Particle Swarm Optimization (PSO), and Ant 
Colony Optimization (ACO) are compared for their speed and accuracy in 
estimating the weighting factors. Additionally, a Fuzzy Logic Controller (FLC) is 
used to further reduce the overall steady-state error and improve transient 
characteristics. Simulations are performed using the MATLAB/Simulink 
software, and the results show that the proposed strategy significantly 
enhances output cross-regulation in multiple output forward DC-DC converters. 
The ICA-based weighting factor estimator is found to be the most effective 
algorithm among the three optimization algorithms tested. The main 
contribution of this study is to provide a more efficient and accurate method for 
estimating the weighting factors in multiple output forward DC-DC converters, 
which can lead to improved performance and reliability in various applications. 

 

1. Introduction 

Multiple-output DC-DC converters are widely used in 
various applications due to their higher efficiency than 
several separate single-output power supplies [1]. Among 
different topologies, multiple output forward DC-DC 
converters are commonly used, especially in renewable 
energy systems [2]. However, poor regulation is one of their 
main limitations, which can be addressed by using the 
weighted voltage mode control method [3-5]. In this method, 
the regulation error is modified by changing the weighting 
factors used in the control, which redistributes the error 
among the outputs of the converter [7-8]. Although this 
method has been employed in some research, the weighting 
factors have typically been determined by trial and error, 
resulting in inconsistent outcomes [9-10]. To overcome this 
limitation, this paper proposes the use of optimization 
algorithms to determine the optimal weighting factors for 
efficient control of the output voltages of multi-output DC-DC 
converters. The Imperialist Competitive Algorithm (ICA), 
Particle Swarm Optimization (PSO), and Ant Colony 
Optimization (ACO) algorithms are employed to concurrently 

determine the best weight values and ideal duty cycle 
arrangement for enhanced regulation of all outputs [11-17]. 
The effectiveness and capability of the proposed algorithm in 
determining the ideal design are demonstrated in this paper. 
The proposed approach is not limited to the forward 
converter but can be applied to any isolated or non-isolated 
converter or any inverter. Moreover, to improve the steady-
state inaccuracy and transient characteristics, the duty cycle 
is controlled by a fuzzy logic controller (FLC) [18-19]. The 
proposed approach is described in detail in Section 2, 
followed by a description of the evolutionary algorithm-based 
weighting factor in Section 3. The results and discussions are 
presented in Section 4, and the conclusions are provided in 
Section 5. In summary, this paper presents a general, quick, 
and accurate approach to determining the weighting factors 
required for efficient control of the output voltages of multi-
output DC-DC converters. The proposed approach is expected 
to enhance the dc cross-regulation and dynamic 
characteristics of the outputs, leading to improved 
performance and efficiency in various applications. 

 

 

 

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M. Safarishaal and M. Sarvi /Future Technology                                                               February 2024| Volume 03 | Issue 01 | Pages 32-39 

33 

 

2. The proposed control method description 

In this research, we consider a forward topology with 
three outputs and a weighted error voltage mode control 
strategy. The reference voltages of all three output voltages 
are measured and compared, and the error is multiplied by 
three weight factors to achieve the desired output voltage 
levels. To optimize the system's performance during 
operation, we utilize computational techniques such as 
Imperialist Competitive Algorithm (ICA), Particle Swarm 
Optimization (PSO), and Ant Colony Optimization (ACO) to 
determine the best suitable parameters for the system. These 
algorithms repeatedly attempt to increase the quality of a 
potential solution, resulting in significantly improved 
performance of the suggested tuned controller compared to 
the use of fixed factors for effective regulation. The 
optimization algorithms are used to determine the optimal 
weighting factors for the weighted error voltage mode 
control. The described techniques are iterated by the 
optimization algorithms until they achieve the best result for 
the objective function's minimization. In each of the cases, the 
population under control is equal to 50. To further improve 
the system's transient properties and reduce overall steady-
state inaccuracy, a fuzzy logic controller is also employed. The 
proposed approach and the system block diagram are shown 
in Figure 1. The power unit, control unit, and weighting factor 
estimation unit are the three main components of the 
proposed system's topology. Following are full introductions 
of the power unit and control unit. The section 3 presentation 
also includes the unit for estimating the weighting factor. 

+

+

+

-

-

-

Vref = -15

Vref = +15

Vref = +5

Evolutionary 

Algorithm

K1

K2

K3

Power Unit

Fuzzy 

Controller 
SUM 

V1

V2

V3

PWM 

Control Unit

Weighting Factor Estimation Unit

 

Figure 1. The proposed control strategy 

 
2.1 Power unit 

Switching transistors (which include two switching 
MOSFETs), a high-frequency transformer, output rectifiers, 
and output filters make up the power stage block. Using an 
input voltage range of 18V to 40V dc and a switching 
frequency of 50 kHz, this study considers a converter with 
three outputs (5V/50W, 15V/45W, and -15V/15W). By 
linking the output filter inductors, the cross-regulated 
outputs' transient characteristics can be improved. Fig.2 
shows these multiple outputs forward DC-DC converters.  
This circuit uses a single DC input (nominally 28 Volt) and 
converts to three simultaneous output voltages. 

Rw1 and Rwi are the primary and secondary winding 
resistances, respectively, in Figure 2. rd is the source 
resistance. The resistance of D1 in its ON state is Rf. The ON 
state resistance of switching MOSFETS is represented by 

RONQ1 and RONQ2, respectively. The primary and secondary 
leakage inductances are L11 and L22, respectively. The 
MOSFETS leakage inductances are LQ1 and LQ2, respectively. 
The output capacitor's Equivalent Series Resistor (ESR) is 
designated as rci [20]. 

L2

R2N3N1

+15V
Rw1Ll1

Rm Lm

+

-

V3

+

-

V1

Vf

Rf

Rw2
Ll2 RL2

C2

rc2

T
L1

R1N2

+5V+

-

V2

Vf

Rf

Rw1 Ll1 RL1

C1

rc1

L3

R3N4

-15V+

-

V4

Rf

Rw3 Ll3 RL3

C3

rc3

Vf

D1

D2

Vd

rd

Da

Ideal switch

LQ2
Vf

Rf

RONQ2

Db

Vf

Rf

Ideal switch

RONQ1

LQ1

D3

 

Figure 2. Multiple outputs forward DC-DC converter 

 
2.2 Control unit 

The control unit consists of a Fuzzy Logic Controller and 
Pulse-Width Modulation (PWM) generator. The PWM 
approach is used to regulate the power switches, and fuzzy 
logic is used to generate the pulses. 

2.2.1 Fuzzy logic controller 
  A dc-dc forward converter has been controlled by 

employing fuzzy control. Nonlinear time-variant systems are 
well suited for fuzzy controllers, which do not require an 
exact mathematical model of the system to be controlled. 
They are typically created using the knowledge of the 
converters obtained from experts [20]. Error signal and 
differential error signal are the inputs of the FLC. The 
switching signal's duty cycle is the output. 

E(t)=Vref - Vo(t)                                                     (1) 

dE= E(t)- E(t-1)                                                        (2) 

dD= D(t) – D(t-1)                    (3) 

where Vref is reference voltage, Vo(t) is output voltage at tth 

instant, E(t) and E(t-1) are error signal, at tth and (t-1)th 
instant, respectively. D(t) and D(t-1) are duty cycle at tth and 
(t-1)th instant, respectively. dE and dD are changed in error 
and duty cycle, respectively. 
For this application, a controller of the Mamdani type is used. 
The max-min inference approach is employed to determine 
the control decision. In order to get a clear result from the 
linguistic values produced using the rule basis, the center of 
gravity approach to defuzzification is also utilized. The basic 
rule of this type of controller is: IF E is A and dE is B THEN d 
(t) is C, where A and B are fuzzy subsets, C is a fuzzy singleton. 
According to Figure 3, the input error, change in error, and 
output all have triangle membership functions. Table 1 serves 
as an illustration of the 7*7 inference system that was utilized 
to generate the fuzzy controller. 
N stands for negative, P for positive, and Z for zero in Table 1. 
B stands for big, M for medium, and S for small. NB, for 
instance, stands for negative big. 

 

 



M. Safarishaal and M. Sarvi /Future Technology                                                               February 2024| Volume 03 | Issue 01 | Pages 32-39 

34 

 

Table 1.  Rule base used in the fuzzy controller 

E\dE NB NM NS ZE PS PM PB 

NB NB NB NB NB NM NS ZE 

NM NB NB NB NM NS ZE PS 

NS NB NB NM NS ZE PS PM 

ZE NB NM NS ZE PS PM PB 

PS NM NS ZE PS PM PB PB 

PM NS ZE PS PM PB PB PB 

PB ZE PS PM PB PB PB PB 

 

 

Figure 3. Membership functions of (a) input E; (b) input CE; 
(c) output dD 

2.2.2 Pulse width modulation (PWM) 
In a DC-DC converter with fuzzy logic control, the 

desired duty cycle is computed by the fuzzy logic controller 
using the total steady-state error. The Pulse Width 
Modulation (PWM) then generates the pulse signals for the 
converter based on the desired duty cycle. The duty cycle 
serves as the control input and is a logic signal that regulates 
the power stage's pattern, which in turn controls the output 
voltage. By adjusting the duty cycle, the converter can 
maintain the desired output voltage, even in the presence of 
disturbances or changes in the input voltage. 

3. Evolutionary algorithm-based weighting factor 

estimator   

 To achieve effective regulation of each output in a 
multiple-output DC-DC converter with weighted voltage 
mode control, the weighting factor (Ki) plays a critical role in 
addition to the circuit parameters. In this paper, we propose 
to use optimization algorithms, namely ICA, PSO, and ACO, to 
estimate the optimal weighting factors for all three output 
voltages. These algorithms iterate through the steps outlined 
until they find the optimal solution to minimize the fitness 
function and improve the regulation performance of each 

output. The algorithm fitness function is assumed to 
correspond to the output errors. The following is how it is 
expressed as the sum of the absolute terms of the relative 
errors: 

𝑭𝒊𝒕𝒏𝒆𝒔𝒔𝑭𝒖𝒏𝒄𝒕𝒊𝒐𝒏 = |𝒆+𝟏𝟓| + |𝒆+𝟓| + |𝒆−𝟏𝟓|         (4) 

Where  e+5 is the +5-v output error, e+15 is the +15-v output 
error, and  e-15 is the -15 v output error. An evolutionary 
algorithm is a subset of evolutionary computation, a 
population-based metaheuristic optimization method used in 
artificial intelligence. 

3.1 Imperialist competitive algorithm (ICA) 
The ICA algorithm used in this study is inspired by 

human socio-political evolution and is based on the idea of 
colonial competition. In the algorithm, a group of imperialist 
countries, along with their colonies, competes to find the 
general optimal solution for the optimization problem. In this 
study, the number of initial imperialist countries is set to 
Np=20, and the number of established colonies is set to 
Nc=50. The stopping criterion is defined as reaching the 
maximum number of iterations (Max Iteration=100) and 
having only one imperialist left in the search space. The ICA 
flowchart is shown in Figure 4. 

Start

 Revolve some 

colonies

Assimilate colonies

Initialize empires

Is there a colony in an empire which 

has lower cost than of the imperialist

Imperialistic Competition

Compute the total 

cost of all empires

Exchange the position of 

that imperialist and colony

Is there an empire 

with no colonies

Eliminate this empire

Unite Similar Empires

Stop condition 

satisfied

END

Yes

No

Yes

Yes

No

No

 

Figure 4. Flowchart of ICA 

3.2 Particle swarm optimization (PSO) 
PSO can be applied to problems whose answer is a point 

or surface in the next n space. It is in this space that 
hypotheses are formed, given an initial velocity and given 
channels of particle communication. Then, when these 
particles travel in the answer space, the outcomes are 
computed each time based on a "goal function." Particles 
gradually move faster in the direction of other particles that 
belong to the same communication group and have a higher 
competency standard. Each agent is aware of its current best 
value (pbest) and its XY position. Additionally, each agent is 
aware of the group's best value (gbest) among all the pbest s. 
By initializing the swarm from the solution space, the velocity 
and position of all particles are randomly set to within 
determined ranges. The velocities of every particle are 
updated after every iteration. The following equation 
provides for the modification of each agent's velocity [12, 13]: 

𝑣𝑖+1 = 𝑣𝑖 + 𝑐1𝑅1(𝑝𝑖,𝑏𝑒𝑠𝑡 − 𝑥𝑖) + 𝑐2𝑅2(𝑔𝑖,𝑏𝑒𝑠𝑡 − 𝑥𝑖)                (5) 

where xi and vi represent a particle's current position and 
speed, respectively. The uniformly distributed random 



M. Safarishaal and M. Sarvi /Future Technology                                                               February 2024| Volume 03 | Issue 01 | Pages 32-39 

35 

 

numbers [0-1] that introduce the stochastic component are 
R1 and R2.  Weight is controlled by the variables c1 and c2. The 
equation can be used to determine a specific velocity that 
gradually approaches the pbest and gbest values that have 
been determined by all of the particles in the swarm. The 
position update equation is given by: 

𝒑𝒊𝒏𝒆𝒘 = 𝒑𝒊 + 𝒗𝒊               (6) 

After updating, pi should be checked and limited to the 
allowed range. Afterwards, when the condition is met Update 
𝑝𝑖,𝑏𝑒𝑠𝑡  and 𝑔𝑖,𝑏𝑒𝑠𝑡  as follows: 

𝒑𝒊,𝒃𝒆𝒔𝒕 = 𝒑𝒊    𝒊𝒇 𝒇(𝒑𝒊) > 𝒇(𝒑𝒊,𝒃𝒆𝒔𝒕)  

 𝑔𝑖,𝑏𝑒𝑠𝑡 = 𝑔𝑖     𝑖𝑓 𝑓(𝑔𝑖) > 𝑓(𝑔𝑖,𝑏𝑒𝑠𝑡)                            

           (7) 

where f(x) is the objective function to be optimized. When 
stop conditions were met, algorithm reports the values of 
𝑔𝑖,𝑏𝑒𝑠𝑡  and f (𝑔𝑖,𝑏𝑒𝑠𝑡) as its solution. The method continues 

until a successful outcome is achieved or the specified 
number of iterations has been reached.  

3.3 Ant colony optimization (ACO) 
One of the most current methods for approximate 

optimization is ACO [14-17]. In this method (ACo), artificial 
ants by moving on the problem diagram and by leaving marks 
on the diagram, like real ants that leave marks in their path, 
make the next artificial ants can provide better solutions to 
the problem. Also in this method, the best path in a diagram 
can be found by computational-numerical problems based on 
probability science. An ant encountering an already 
established route can identify it and decide to follow it with a 
high probability, exploitation, and as a result, reinforces the 
track with its own pheromone. In contrast, an isolated ant 
virtually walks at random, exploring. 

4. Results and discussions 

In this section, several conditions were taken into 
consideration and simulated in order to assess the accuracy 
and validity of the suggested method. The simulation results 
of three optimization algorithms and conventional constant 
weighting factor control are presented. The following 
conditions are used to run the simulations in the 
MATLAB/SIMULINK environment: 

• Variation of DC-DC converter output load (at +5 V output) 
• Variation of DC-DC converter output load (at +15 V output) 
• Variation of DC-DC converter input voltage 

It should be noted that the fuzzy logic controller has been 
used for all approaches including constant weighting factors 
(which were calculated by conventional mathematical 
method) and all three proposed methods at three mentioned 
conditions. Also, for the same system, a PID controller is 
designed. When the PID coefficients are established for the 
investigated DC-DC converter by trial and error, the total 
error is then sent through a PID controller to reduce the 
steady-state error as well as a fuzzy logic controller in Section 
4.5. Under the following circumstances, simulations are 
carried out in the MATLAB/SIMULINK environment. Also in 
this section, the performances of the three used algorithms 
are compared with each other. 

4.1 Variation of DC-DC converter output load (At +5 V 
output) from 100% to 50% 
In this case, the load on +5 v output of the converter 

changes from 100% to 50% at t=10 ms when the output 

voltages are in steady state. Figure 5 (a) shows three output 
voltages when ICA is used as a weighting factors estimator. 
Figures 5 (b) and (c) show these output voltages for ACO and 
PSO weighting factors estimator, respectively. Accordingly, 
Figure 5 (d) shows these voltages for constant weighting 
factor are used for voltage mode weighting factor control. 
These results show that the use of an evolutionary algorithm 
can improve the percent of cross-regulation significantly. 

(a) 

(b) 

(c) 



M. Safarishaal and M. Sarvi /Future Technology                                                               February 2024| Volume 03 | Issue 01 | Pages 32-39 

36 

 

(d) 

Figure 5. Output voltages while load on +5 V output changes 
from 100% to 50% with a) ICA estimator of weighting factors, 
b) ACO estimator of weighting factors c) PSO estimator of 
weighting factors, d) constant weighting factors (Continue) 

 
Table 2 shows the regulation of the presented methods 

before and after load changing. In a constant weighting factor 
method, the weighting factors are determined by trial and 
error at the system condition before load changing (as t=6 
ms) for optimum regulation. Thus, the regulation can be good 
in this condition, but after changing conditions, regulation 
may be reduced, whereas three other methods have lower 
regulation. Also, because of its higher accuracy and speed, ICA 
based weighting factor estimator is more effective in 
comparison with ACO and PSO-based weighting factor 
estimators. 

Table 2. Output voltage regulation while load changes at +5 v 
output from 100% TO 50% 

Method Regulation (%) at t=6 

ms 

Regulation (%) at 

t=16 ms 

+15V  +5V  -15V +15V +5V -15V 

ICA 0.33 0.50 0.26 0.46 0.58 0.40 

ACO 0.40 0.72 0.46 0.46 0.76 0.46 

PSO 0.46 0.78 0.53 0.46 0.76 0.46 

Constant 

Weighting 

Factor 

0.53 0.86 0.60 2 1.76 1.93 

 

4.2 Variation Of DC-DC converter output load (At +15 V 
output) from 100% To 120% 
In the third case, the load on +15 v output of the 

converter changes from 100% to 120% at t=10 ms when the 
output voltages are in a steady state. Figures 6 (a), (b), (c), and 
(d) show the three output voltages for the ICA, ACO, and PSO-
based estimator of the weighting factor as well as the constant 
weighting factor method, respectively. Table 3 shows the 
output voltage regulation. This outcome demonstrates that 
the suggested approach can enhance cross-regulation in all 

three outcomes. Results show that the ICA can be more 
accurate than the two other algorithms.  

(a) 

(b) 

 

(c) 

 



M. Safarishaal and M. Sarvi /Future Technology                                                               February 2024| Volume 03 | Issue 01 | Pages 32-39 

37 

 

(d) 

Figure 6. Output voltages while the load on +15 V output 
changes from 100% to 120% with c) PSO estimator of 
weighting factors, d) constant weighting factors (Continue) 

 
Table 3.  Output voltage regulation while load changes at +15 
v output from 100% to 200% 

Method Regulation (%) at t=6 

ms 

Regulation (%) at 

t=16 ms 

+15V  +5V  -15V +15V +5V -15V 

ICA 0.00 0.05 0.00 0.06 0.14 0.13 

ACO 0.13 0.18 0.60 0.06 0.18 0.53 

PSO 0.60 0.94 0.33 0.60 0.92 0.46 

Constant 

Weighting 

Factor 

1.26 5.46 0.73 1.26 5.38 0.80 

 

4.3 DC-DC Converter Input Voltage Changing From 30 V 
to 35 V 
In this case, the input DC voltage of the DC-DC converter 

changes from 30V to 35V. Figures 7 (a), (b), (c), and (d) show 
the three output voltages for the ICA, ACO, and PSO-based 
estimator of the weighting factor as well as the constant 
weighting factor method, respectively. The results of this case 
show that the evolutionary algorithms are very effective in 
improving cross-regulation in all outputs. Results after 
changing at t=16 ms show that the constant weighting factor 
method is not a suitable method for all conditions.  The results 
of regulation for comparison of all methods are presented in 
Table 4. 

4.4 Dynamic Response Comparison 
The +5 V output voltage results of the mathematical-

based weighting factors estimator, together with a set of 
optimal weighting factors that is obtained from one of the 
best-used evolutionary algorithms (ICA), are presented in 
Figure 8 when both PID and fuzzy logic controllers are 
compared, their performances have been assessed through 

simulations. The time response parameters percent 
overshoot (%), settling time (ms), and percent steady-state 
error (%) for PID controller and fuzzy logic controller when 
ICA and conventional mathematical method have been used 
for both as weight factor estimator, are presented in Table 5.  

(a) 

(b) 

 

(c) 



M. Safarishaal and M. Sarvi /Future Technology                                                               February 2024| Volume 03 | Issue 01 | Pages 32-39 

38 

 

 

(d) 

Figure 7. Output voltages while input voltage changes from 
30 V to 35 V with a) ICA estimator of weighting factors, b) ACO 
estimator of weighting factors, c) PSO estimator of weighting 
factors, d) constant weighting factors (Continue) 

 
Table 4. Output voltage regulation while input voltage 
changes from 30v to 35v 

Method Regulation (%) at t=6 

ms 

Regulation (%) at t=16 

ms 

+15V  +5V  -15V +15V +5V -15V 

ICA 0.93 0.26 1.00 0.33 0.22 0.20 

ACO 0.73 0.54 0.53 0.40 0.52 0.53 

PSO 0.26 0.90 0.20 0.66 0.80 0.80 

Constant 

Weighting 

Factor 

0.13 1.22 0.66 2.26 3.04 3.13 

 

As shown in this Figure 8, the choice of appropriate 
weighting factors, in addition to improving voltage regulation, 
can positively affect the dynamic behavior of the system. 
Results show that when the optimal weighting factors are 
used, the overshot value and settling time have reduced 
significantly. 

4.5 Comparison of the Presented Algorithms 
Performances 
In this section, the comparative performance analysis of 

the three optimization algorithms used in this study is 
presented. Table 6 summarizes the performance of the 
algorithms under different conditions, with total regulation 
being the sum of regulation at all three output voltages. 
Results show that ICA outperforms PSO and ACO in terms of 
overall regulation, execution time, and convergence rate. 
Moreover, ICA is found to be more effective in achieving 
cross-regulation than PSO and ACO. It should be noted that 
while all optimization techniques improve cross-regulation, 

ICA shows better results. The simulations were performed 
using MATLAB on a Pentium 2.4GHz computer with a 
population size of 50 in all scenarios and a maximum iteration 
of 50 for all algorithms. The findings suggest that ICA is a 
suitable optimization algorithm for this work and could be 
explored further for other applications. 

 
Figure 8. The +5 V output voltage results for Fuzzy ICA, PID 
ICA, Fuzzy mathematical, and PID mathematical methods 

 
Table 5. Time response parameters 

                     Item 

 

Method 

Overshoot 

(%) 

Settling 

time (ms) 

Steady 

state 

error (%) 

Fuzzy & ICA 2.38 1.9 0.88 

PID & ICA 1.78 1.9 0.90 

Fuzzy Mathematical 7.8 2.5 1.64 

PID & Mathematical 12.78 3.5 0.92 

 

Table 6. Algorithms perform at different conditions 

 
Conditions 

Convergence 
Iteration 

Total regulation % at 
t=16ms 

ICA ACO PSO ICA ACO PSO 
Load 
variation 
from 100% 
to 50% 

75 85 100 1.44 1.68 1.68 

Voltage 
variation 
from 30V to 
35V 

75 80 110 0.75 1.45 2.26 

Load 
variation 
from 100% 
to 120% 

70 85 110 0.33 0.77 1.98 

 

 

 



M. Safarishaal and M. Sarvi /Future Technology                                                               February 2024| Volume 03 | Issue 01 | Pages 32-39 

39 

 

5. Conclusion 

In summary, this paper presents a novel approach for the 
estimation of weighting factors in weighted voltage mode 
control of multiple outputs forward DC-DC converter. The 
approach uses evolutionary algorithms, including Imperialist 
Competitive Algorithms, Particle Swarm Optimization, and 
Ant Colony Optimization, to find the optimal weighting factor 
for voltage mode control. The fuzzy logic controller is also 
utilized to improve the dynamic response of the converter. 
The proposed method improves the cross-regulation and 
dynamic characteristics of the outputs significantly compared 
to the constant weighting factor method. The ICA-based 
weighting factor estimator is shown to have higher speed and 
accuracy compared to the other presented evolutionary 
algorithms, making it more effective. Overall, the results 
demonstrate that the proposed method can be used for other 
types of multiple-output DC-DC converters and can 
considerably enhance the control and dynamic features of the 
detected outputs if the weighting factors are properly 
designed. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
Datasets analyzed during the current study are available and 

can be given following a reasonable request from the 

corresponding author. 

Conflict of interest 

The authors declare no potential conflict of interest. 

References 

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Multi-Output Converters”, IPEMC. Int. Conf., Xi'an, 

China, Aug 2004, pp. 482-486. 

[2] Khazeiynasab, S. R., & Batarseh, I. “Measurement-

Based Parameter Identification of DC-DC Converters 

with Adaptive Approximate Bayesian Computation”. 

(2021). arXiv preprint arXiv:2106.15538.Matsuo 

[3]  Wilson, T., “Cross Regulation in an Energy-Storage DC 

to DC Converter with Two Regulated Outputs”, IEEE. 

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