Chiến lược điều khiển tốc độ mới dựa trên logic mờ kiểu Pi và PSO cho các động cơ từ trở thay đổi

TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC 
(ISSN: 1859 - 4557) 
Số 14 tháng 12-2017 39 
A NOVEL PSO-BASED PI-TYPE FUZZY LOGIC SPEED CONTROL 
APPROACH FOR SWITCHED RELUCTANCE MOTORS 
CHIẾN LƯỢC ĐIỀU KHIỂN TỐC ĐỘ MỚI DỰA TRÊN LOGIC MỜ KIỂU PI 
VÀ PSO CHO CÁC ĐỘNG CƠ TỪ TRỞ THAY ĐỔI 
Nguyen Ngoc Khoat 
Faculty of Automation Technology, Electric Power University 
Abstract: 
This work concentrates on the design of a novel speed control strategy for a 10/8-type switched 
reluctance motor (SRM) applying particle swarm optimization (PSO) algorithm and fuzzy logic 
technique. Due to the simple operation mechanism and high effectiveness, the PSO technique is 
successful to optimize some crucial parameters of a PI-type Fuzzy Logic (FL) speed controller, i.e. 
membership functions and an output scaling factor. This method will also be employed to determine 
the most effective switching angles of an asymmetrical DC-DC converter which is used to feed power 
to the SRM. Therefore, a total of twelve variables in accordance with a swarm of particles is 
successfully optimized through five integrated steps proposed in this paper. The convergence of this 
optimization process provides optimal parameters for designing the PI-type FL speed controller and 
the determination of two switching angles. Subsequently, numerical simulation processes using 
various load conditions will also be executed to validate the effectiveness and superiority of the 
proposed control strategy compared with those of the conventional PI regulator. It is found from the 
simulation results the control scheme devised is an optimal solution for designing the intelligent 
speed controller of a 10/8-type SRM drive system in practice. 
Key words: 
10/8-type switched reluctance motor, PI-type fuzzy logic controller, particle swarm optimization, 
optimal tuning, membership functions, gain-updating factor, switching angles. 
Tóm tắt: 6 
Bài báo này đề xuất một chiến lược điều khiển tốc độ mới cho các hệ truyền động sử dụng động cơ 
từ trở thay đổi loại 10/8 sử dụng thuật toán tối ưu hóa bầy đàn (PSO) và lý thuyết điều khiển mờ. 
Thuật toán tối ưu hóa PSO với ưu điểm nổi bật như cơ chế làm việc đơn giản và hiệu quả cao sẽ 
được áp dụng để tối ưu hóa một số tham số quan trọng của bộ điều khiển tốc độ mờ kiểu PI như 
các hàm thuộc và hệ số chỉnh định đầu ra. Thuật toán này cũng được sử dụng để xác định các góc 
chuyển mạch tối ưu cho một bộ biến đổi áp DC/DC không đối xứng cấp nguồn cho động cơ từ trở 
thay đổi loại 10/8 nói trên. Giải thuật tối ưu hóa PSO sử dụng trong nghiên cứu này sẽ bao gồm 12 
biến, và quá trình tối ưu hóa được thực hiện thông qua 5 bước được đề xuất chi tiết trong bài báo. 
Sự hội tụ của thuật toán tối ưu PSO đã đưa ra các tham số tối ưu hiệu quả cho thiết kế bộ điều 
khiển mờ kiểu PI cũng như xác định được các góc chuyển mạch van hợp lý nhất. Quá trình mô 
phỏng sử dụng nhiều điều kiện khác nhau của phụ tải được thực hiện để minh chứng cho sự hiệu 
quả và đặc tính vượt trội của giải pháp điều khiển đã đề xuất so với phương pháp điều khiển kinh 
6Ngày nhận bài: 23/11/2017, ngày chấp nhận đăng: 8/12/2017, phản biện: TS. Nguyễn Quốc Minh. 
TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC 
(ISSN: 1859 - 4557) 
40 Số 14 tháng 12-2017 
điển sử dụng bộ điều chỉnh PI truyền thống. Các kết quả mô phỏng khẳng định giải pháp điều khiển 
mới đưa ra trong nghiên cứu này là một phương pháp tối ưu hiệu quả trong việc thiết kế bộ điều 
khiển tốc độ thông minh cho các hệ truyền động sử dụng động cơ từ trở thay đổi loại 10/8 trong 
thực tế. 
Từ khóa: 
động cơ từ trở thay đổi loại 10/8, bộ điều khiển logic mờ loại PI, giải thuật tối ưu hóa bầy đàn, chỉnh 
định tối ưu, các hàm thuộc, hệ số chỉnh định cập nhật, các góc chuyển mạch. 
1. INTRODUCTION 
Switched reluctance motors (SRMs) with 
many attractive features, i.e. high torque 
to weight ratio, simple construction and 
rigged structure have gained much 
attention to researchers as well as 
engineers. The novel categories of the 
SRMs have been continuously 
investigating in order to enrich their SRM 
family [1-4]. Despite the fast widespread 
application, the SRM drive systems have 
still been studied to deal with their 
inherent disadvantages, such as the 
nonlinearity, the torque ripple and the 
difficult control of electronic power 
converters which feeds energy to the 
machines [5-7]. It is found that the 
efficient control strategies need to be 
further investigated to obtain the desired 
control performances, such as the 
stability, efficiency and the optimal 
dynamic responses of the phase current, 
electromagnetic torque as well as the 
angular speed. In general, control 
strategies, which mainly focus on 
designing speed and current controllers, 
have applied both the conventional and 
modern regulators. The conventional 
controllers (i.e., PI, PD and PID 
regulators) have be ... 

 By combining with 
the tuning process of the MFs as 
mentioned earlier, the PSO algorithm is 
run following five steps as: 
Step 1: Initialization 
The initial parameters for the PSO 
algorithm should be set, including particle 
size m, number of swarms n, number of 
iterations N and constraints , .Lb Ub 
Step 2: Determination of the objective 
function 
In this work, the objective (fitness) 
function is determined as expressed in 
(8). This fitness function needs to be 
minimized according to the objective of 
the PSO algorithm. 
Step 3: Design of the FL reasoning 
The FL model is built here using 
Mamdani architecture with symmetric - 
triangular MFs which are parameterized 
as shown in Fig. 4. In addition, the basic 
49 rules base for a classical PI-based FLC 
(as illustrated in [8]) will also be applied 
to the proposed FL model. 
Step 4: Design of the SRM system 
A 10/8 type SRM, which has been 
modeled in Section 2, can be used 
here applying the proposed PI-based FL 
speed controller. In addition, switching 
angles are able to be determined by either 
the experience or applying the PSO 
technique. 
Step 5: Run PSO algorithm and get the 
optimal results 
The PSO algorithm will be run according 
to steps as introduced earlier. Finally, 
results obtained shows the optimal 
parameters of MFs and gain-updating 
factor . 
3.3. Determination of switching angles 
applying PSO method 
This work applies the PSO algorithm to 
determine not only the parameters of a PI-
type FLC but also the switching angles, 
i.e. turn-on angle α (
on ) and turn-off 
angle β ( off ). It is the fact that such two 
switching angles impact significantly on 
the electromagnetic torque generation of 
the SRMs [1,2]. Therefore, control 
performances of the SRM drive system 
will also be affected, leading to the need 
of their optimization. 
In the context of this study, α and β can 
also be optimized by using the PSO 
method. To perform it, two arguments 
need to be added to the variable space of 
the PSO algorithm. Hence, the total of 
variables used in such PSO method is 
twelve (nine for MFs, one for gain 
updating factor  and two for α and β). 
Using the trial and error method, the 
TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC 
(ISSN: 1859 - 4557) 
46 Số 14 tháng 12-2017 
lower and upper bounds of the turn-on 
angle α and the turn-off angle β can be 
determined respectively as: 10 22  
and 39 45 .  The optimization 
process will be carried out through five 
steps as mentioned above. Accordingly, 
the optimal control strategy proposed in 
this study will be represented finally in 
Fig. 5. The effectiveness and feasibility of 
the proposed control strategy will be 
discussed in the following section. 
Δe[i]
u[i]
Inference 
Engine
Data 
Base
1
z
z 
uG 
eG
eG 
e[i]
Δu [i]
1z
z
F
u
zz
if
ic
at
io
n
Rule Base
IF  THEN ...
[ ]Nu i 
C
o
n
tr
o
l 
si
g
n
al
SRM 
DRIVE SYSTEM
PSO Algorithm

Gain-updating factor Fitness function 
evaluation
[ ]ref i
_
[ ]i
[ ]Ne i
[ ]Ne i 
 
PI-type FLC
Switching 
angle controller
D
ef
u
zz
if
ic
at
io
n
Fig. 5. The proposed control strategy of the 10/8-type SRM drive 
4. NUMERICAL SIMULATION RESULTS 
In order to justify the effectiveness and 
the feasibility of the proposed control 
strategy, a simulation configuration 
for the 10/8-type SRM drive is designed 
in Matlab/Simulink environment 
corresponding to the system shown in 
Fig. 5. Here, the PSO algorithm is 
implemented through a m-file written in 
Matlab/Script environment. In this study, 
the PSO algorithm will be applied to 
optimize totally twelve variables as 
mentioned in the previous section. It is 
known that not only the speed FL 
controller with the corresponding MFs 
and output scaling factor but also two 
switching angles are tuned to obtain the 
optimal parameters for the SRM drive 
system. Thus, the variable space in 
accordance with a particle swarm is given 
below: 
( , , , , , , , , , , , ).e e e de de de o o oP m n p m n p m n p   
 (10) 
The PSO technique is initialized with 
parameters indicated in Appendix of this 
paper. To implement the PSO algorithm, 
in the simulation process, a high reference 
speed 3000rpm will be set on the input of 
the SRM drive system. Also, a PI 
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(ISSN: 1859 - 4557) 
Số 14 tháng 12-2017 47 
regulator is employed as the current 
controller of this drive system. Using the 
objective function given in (8) as the cost 
to evaluate the optimization process of the 
PSO algorithm, the optimal results are 
obtained as shown in Fig. 6-8. In Fig. 6, 
the cost functions have been calculated 
and plotted through 100 iterations for the 
local, global and mean optimal variable 
vectors corresponding to a set of 
parameters as expressed in (10). It can be 
seen obviously that these functions are 
converging to the optimal value. The 
details of this convergence evolution 
are represented in Fig. 7(a) and 7(b) 
for the switching angles (α, β) and the 
gain-updating factor (γ), respectively. 
Moreover, based on the PSO method, the 
MFs of two inputs (eN[k], ∆eN[k]) and one 
output (∆uN[k]) are tuned to obtain the 
optimal values as illustrated in Fig. 8(a), 
8(b) and 8(c), respectively. As shown, the 
number of the MFs has been reduced due 
to their overlapping. Concretely, there 
are only three remaining MFs used for 
both the first input eN[k] and the output 
∆uN[k]. Meanwhile, the second input 
∆eN[k] of the PI-FL speed controller only 
employs five instead of seven MFs as the 
basic control strategy [9-10]. Obviously, 
after the PSO method, the FL inference 
has been simplified significantly. This 
will dramatically speed up the simulation 
process of the control system in 
comparison with the basic FLC. The 
optimal parameters obtained is applied to 
design an effective control strategy for the 
10/8-type drive system. 
To evaluate the superiority of the optimal 
PI-type FL speed controller over the 
conventional PI regulator, 3 cases of load 
torques are applied to the SRM drive as: 
(i) Case 1: there is no load TL = 0 (see 
Fig. 9(a)). 
(ii) Case 2: there is only a load torque 
(TL = 100 N.m) which will be appeared at 
1(s) (see Fig. 9(b)). In fact, this can be 
used for a process of the machining 
machinery applying the SRM drive 
system. 
(iii) Case 3: there is a symmetrically 
repeated load torque (see Fig.s 10(a) and 
10(b)). This can be employed practically 
to design the repeated machining 
machinery drive system with highly exact 
quality characteristics. 
Fig. 6. Optimization process of the PSO 
algorithm 
Fig. 7. Convergence of the PSO algorithm 
(a) Switching angles; (b) Gain-updating factor 
0 20 40 60 80 100
6
8
10
12
14
16
18
20
22
Iterations
C
os
t v
al
ue
Local optimal value
Mean optimal value
Global optimal value
0 20 40 60 80 100
0
10
20
30
40
50
Iterations
(a)
D
e
g
re
e
 (
o
)
Alpha
Beta
0 20 40 60 80 100
0
2
4
6
8
Iterations
(b)
-Gain updating factor
Score
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48 Số 14 tháng 12-2017 
Fig. 8. Optimal membership functions 
of the PI-type FLC applying the PSO 
Both of the SRM drive systems (applying 
PI-type FLC and conventional PI 
controller (PIC)) use the optimal 
switching angles taken from the PSO 
mechanism (α = 16.0116 and β =
42.7951 ). It can be seen from Figs. 9 and 
10 the proposed FLC has obtained much 
better results compared with the 
conventional PI regulator. The dynamic 
control performances of the angular speed 
response obtained by using the PI-type 
FLC, such as the overshoot, transient time 
and settling time, are much smaller for all 
of three load cases. 
Fig. 9. The dynamic response of the angular 
speed. (a) Case 1: No load; (b) Case 2: Load 
occurs at 1(s) 
Fig. 10. Load torque and angular speed 
for the third simulation case 
(a) Symmetrically repeated load torque; 
(b) Dynamic response of the angular speed 
Fig. 11. Phase currents and electromagnetic 
torque around 1(s) in the third simulation case 
(a) Phase currents: iA (blue-solid line) and iC 
(magenta-dashed line); (b) Electromagnetic 
torque Te 
Fig. 12. Phase currents and electromagnetic 
torque around 4(s) in the third simulation case 
(a) Phase currents: iB (blue-solid line) and iE 
(red-dashed line); (b) Electromagnetic torque Te 
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
0
0.5
1
e
N
[i]
(a)
M
F
s
NL NM NS ZE PS PM PL
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
0
0.5
1
 e
N
[i]
(b)
M
F
s
NL NM NS ZE PS PM PL
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
0
0.5
1
 u
N
[i]
(c)
M
F
s
NL NM NS ZE PS PM PL
0 0.5 1 1.5 2
0
1000
2000
3000
4000
Time (s)
(a)
S
p
e
e
d
 (
rp
m
)
PI-type FLC
Conventional PIC
0 0.5 1 1.5 2
0
1000
2000
3000
4000
Time (s)
(b)
S
p
e
e
d
 (
rp
m
)
PI-type FLC
Conventional PIC
Load occurrence
0 1 2 3 4 5
0
50
100
150
200
Time (s)
(a)
L
o
a
d
 t
o
rq
u
e
 (
N
.m
)
T
Load
0 1 2 3 4 5
0
1000
2000
3000
4000
Time (s)
(b)
S
p
e
e
d
 (
rp
m
)
PI-type FLC
Conventional PIC
+T
L1
+T
L2
+T
L2
+T
L1
-T
L2
-T
L2
-T
L1
-T
L1
0.99 0.995 1 1.005 1.01
0
100
200
300
Time(s)
(a)
i (
A
)
0.99 0.995 1 1.005 1.01
-100
0
100
200
Time(s)
(b)
T e
 (
N
.m
)
3.99 3.995 4 4.005 4.01
0
200
400
Time(s)
(a)
i (
A
)
3.99 3.995 4 4.005 4.01
-100
0
100
200
Time(s)
(b)
T e
 (
N
.m
)
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In addition, Fig. 11(a) and 11(b) illustrate 
the dynamic responses of current phases 
and electromagnetic torque around 1s 
corresponding to an increase of the load 
torque in the third simulation case. On 
the other hand, when load moment falls in 
the second time (at 4s), such two dynamic 
responses can be affected as shown in 
Fig. 12(a) and 12(b). It is found that 
the oscillations of the current phases are 
kept to be stable even though the 
electromagnetic torque is being affected 
by the change of the load. Indeed, this is 
very meaningful when designing the drive 
systems that require strictly high control 
characteristics, e.g., the electric traction 
drive systems of electric vehicles. 
5. CONCLUSIONS AND DISCUSSIONS 
The efficient application of the PSO 
algorithm has been investigated in this 
paper to design the optimal PI-type FL 
speed controller for a 10/8-type SRM 
drive system. All of the MFs as well as 
the gain-updating factor of this FLC are 
optimized successfully. In addition, the 
PSO method is applied to determine 
the optimal switching angles of the 
asymmetrical DC-DC converter, which 
has been used to feed the power energy to 
the SRM drive. Using five integrated 
steps of the PSO technique proposed in 
this study, the optimization process has 
been implemented in order to design a 
highly feasible and efficient control 
strategy for the 10/8-SRM drive system. 
Through the simulation results obtained 
with various cases of loads, the 
superiority of the proposed control 
scheme has been demonstrated compared 
with the traditional counterpart using the 
PI regulator. It is well known that the 
control process has been outperformed 
efficiently enough to apply to various 
drive systems in reality. 
For future work, the investigation of 
different types of the SRM drive systems 
applying the optimal controllers, e.g., the 
PI-type FLC based on the PSO algorithm, 
should be considered. Moreover, a hybrid 
control strategy using the combination of 
fuzzy logic and neural network techniques 
based on means of the biological-inspired 
optimization will catch more attention to 
design an efficiently practical SRM 
system. 
Appendix 
10/8-type SRM parameters 
2
min max max
0.05 , 0.05 . , 0.02 . . ,
0.67 , 23.6 , 500
k
k k k
R J kg m f N m s
L mH L mH i A
  
PSO parameters 
 
10, 12, 100;
[0,0,0,0,0,0,0,0,0,0,10,39];
1,1,1,1,1,1,1,1,1,1,22,45
n m N
Lb
Ub
ACKNOWLEDGMENT 
The authors wish to thank the editors and 
anonymous reviewers for their valuable 
comments, which will help to improve this 
study. 
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Biography: 
Nguyen Ngoc Khoat received the Msc degree in Automation and Control at 
Hanoi University of Science and Technology, in 2009. He received the PhD 
degree in Electronic Science and Technology at University of Electronic 
Science and Technology of China, in 2015. He is working as a lecturer and 
researcher at Faculty of Automation Technology, Electric Power University in 
Hanoi, Vietnam. His research interests include renewable energy, intelligent 
control, power electronics and smart electric drives systems.