LUẬN VĂN THẠC SỸ KỸ THUẬT NGÀNH TỰ ĐỘNG HÓA - THIẾT KẾ BỘ ĐIỀU KHIỂN MỜ TRƯỢT
Cải tiến bộ điều khiển mờ cấp cao cho bài toán điều khiển độ ấm nhà...
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Hội nghị toàn quốc về Điều khiển và Tự động hoá - VCCA-2011
VCCA-2011
Cải tiến bộ điều khiển mờ cấp cao
cho bài toán điều khiển độ ấm nhà kính
A Development on Advanced Fuzzy Based Controller Design for
Humidity Control of Greenhouse
Minh Duc Nguyen1, Viet Boi Chau Luong
2, Tuong Quan Vo
3
1 Department of Mechatronics, Faculty of Mechanical Engineering, HCMUT, Vietnam
(Email: [email protected]) 2 Department of Mechatronics, Faculty of Mechanical Engineering, HCMUT, Vietnam
(Email: [email protected]) 3 Department of Mechatronics, Faculty of Mechanical Engineering, HCMUT, Vietnam
(Email: [email protected])
Tóm tắt Ngày nay, nhà kính không còn là một khái niệm lạ
trong nông nghiệp. Tuy nhiên, điều hòa khí hậu trong
nhà kính còn gặp nhiều khó khăn. Nhiều thông số cần
được điều khiển chẳng hạn như nhiệt độ, độ ẩm tương
đối, độ chứa hơi, lượng khí carbon dioxide trong
không khí ... Trong đó, khó khăn nhất là điều khiển độ
ẩm tương đối. Trong bài báo này đề cập đến bộ điều
khiển mờ thông thường và bộ điều khiển mờ nâng cao
được sử dụng cho máy tăng ẩm và máy hút ẩm. Bộ
điều khiển mờ nâng cao là tự điều chỉnh các thông số
đầu ra dựa trên sai số và đạo hàm của sai số của các
biến điều khiển.
Abstract Nowadays, greenhouse is not longer a strange
conception in agriculture. The plants in a greenhouse
impose their own needs. However, climate control for
protected crops has many difficulties. There are many
parameters are controlled such as temperature,
relative humidity, humidity ratio, carbon dioxide in
the air… Among protecting all of them, relative
humidity is the hardest parameter to control. In this
paper, Conventional Fuzzy Controller (CFC) and
Self-tuning Fuzzy Logic Controller (STFLC) are used
for the humidifier and dehumidifier. The Self-tuning
Fuzzy Logic Controller is adjusted the output scaling
factor on-line by fuzzy rules according to the current
trend of the controlled process. The rule-base for
tuning the output-scaling factor is defined on error
and change of error of the controlled variable.
Keywords Greenhouse, Conventional Fuzzy Controller,
Advanced Fuzzy Controller, Self-tuning Fuzzy
Controller, humidity.
Nomenclature in % indoor relative humidity
o1 % outside relative humidity type 1
Acc m2 surface area of cooling coils
Agap = 0.02m2 area of gaps of wall
Ahc m2 surface area of heating coils
cp kJ/kg.oC specific heat capacity of moist
air
dac kg/kg humidity ratio of air flow
passing over cooling coils
dh kg/kg humidity ratio of supply air flow
by humidifier
din kg/kg humidity ratio of indoor air
do kg/kg humidity ratio of outdoor air
Fo1 m3/s volume flow rate of air blowing
from outside to inside
G kg mass of dry indoor air
Gd kg/s mass flow rate of supply dry air
flow by dehumidifier
Gh kg/s mass flow rate of supply dry air
flow by humidifier
Go kg/s mass flow rate of air blowing
from outside to inside
Gw kg/s mass flow rate of added water
vapor
hcc kW/m2.oC average convective heat-transfer
coefficient of cooling coils
hhc kW/m2.oC average convective heat-transfer
coefficient of heating coils
ih kJ/kg enthalpy of supply air by
humidifier
iin kJ/kg enthalpy of indoor air
iw kJ/kg enthalpy of added water vapor
p = 1 bar pressure of air
phmax bar saturation vapor partial pressure
at indoor temperature
tac oC temperature of air flow passing
over cooling coils
tah oC temperature of air flow passing
over heating coils
tcc oC surface temperature of cooling
coils
th oC temperature of supply air by
humidifier
thc oC surface temperature of heating
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Hội nghị toàn quốc về Điều khiển và Tự động hoá - VCCA-2011
VCCA-2011
coils
tin oC temperature of indoor air
to oC temperature of outdoor air
to1 oC outside temperature type 1
vwind m/s outside wind speed
1. Introduction We find that the climate in the crop have a great
influence on the growth of plants, capacity growth,
productivity, quality and of tree maintenance
procedures. Environmental control is a central feature
of modern production systems. There is so much
research on the theory and application the related
issues of environmental control Greenhouse has been
done by many researchers (Jones, 1984 [Error!
Reference source not found.]; Gates and Overhuts,
1991 [9]; Stanghellini and van Meurs, 1992 [10];
Taylor, 2000 [11]; Zolnier, 2000 [12]). Most research
has focused on the analysis and control of
environmental conditions inside the glass based on the
concept of energy and material model reasons.
Many kinetic models for greenhouse environmental
developed and documented in the literature, the model
the kinetics of these are non-linear model with
variables mainly air temperature and relative humidity
for (or absolute humidity), concentrations of carbon
dioxide also mentioned. Noise impact on the climate
glass all tastes from solar radiation, outside
temperature (the phenomenon of thermal conductivity
and heat transfer), but the frequency the noise is very
low. Indeed, temperature and humidity are closely
associated with each other through the law of non-
linear thermodynamics. Therefore, we designed a
controller that is strong enough to control system for
greenhouse is difficult.
2. System Modeling Humidifier has two systems: atomizing system and air
handling unit. Water passes through an atomizing
system to become water vapor. This water vapor will
be added to the air.
Following [1], the humidity ratio of supply air flow
by humidifier is
wh in
h
Gd d
G (1)
Following [1, 2], the temperature of supply air flow
by humidifier is
in in w h w h
h in
p
d i i d i it t
c (2)
The air in a dehumidifier first passes over a series of
cooling coils (the evaporator) and then immediately
over a set of heating coils (the condenser) and then
back into the room as dryer air with its temperature
elevated.
Following [1, 2], the humidity ratio of air flow
passing over cooling coils is
1.006
2500.77 1.84
cc cc in cc
in ac
dac
ac
h A t ti t
Gd
t (3)
Following [1, 2], the temperature of air flow passing
over heating coils is
1.006 1.84
hc hc hc ac
ah ac
d ac
h A t tt t
G d (4)
Therefore, the temperature td and the humidity ratio dd
of supply air flow by dehumidifier are
d aht t (5)
d acd d (6)
According law of energy conservation, if we choose
the sampling time is one second, the indoor
temperature and humidity ratio at the step k are
1
h h d d o o
in h d
in
o
t k G k t k G k t k G k
t k G k G k G kt k
G k G k
(7)
1
h h d d o o
in h d
in
o
d k G k d k G k d k G k
d k G k G k G kd k
G k G k
(8)
The indoor relative humidity is
max0.622
inin
h
d p
d pj (9)
3. Controllers Design In Controller, there are two inputs:
The system error is defined as the difference
between the plant output y(k) and the set point
r(k) at the step k is :
e k y k r k (10)
The change rate of error at the step k is
1de k e k e k (11)
And two outputs, as the inputs of the plant:
For humidifier: the mass flow rate of water vapor
Gw (kg/s).
For dehumidifier: the volume flow rate of supply
air Fd (m3/s)
3.1. Conventional Fuzzy Controller (CFC)
The structure of CFC is shown in Fig.1. The input
range for e and de are based on the load disturbance.
The output ranges for Gw and Fd are base on the max
power of humidifier and dehumidifier. The language
variables description of inputs and outputs are shown
in Table 1. The fuzzy membership functions for
inputs and outputs are shown in Fig.2. Table 2 lists 49
language fuzzy rules for the CFC.
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Hội nghị toàn quốc về Điều khiển và Tự động hoá - VCCA-2011
VCCA-2011
Conventional
Fuzzy
Controller
Plant
du/dt
Gw
Fdder
y
e
Fig.1. Structure of Conventional Fuzzy Controller
Table 1. Language variables description
Inputs Output
NB Negative Big ZE Zero 1
NM Negative Medium VS Very Small 2
NS Negative Small S Small 3
ZE Zero SB Small Big 4
PS Positive Small MB Medium Big 5
PM Positive Medium B Big 6
PB Positive Big VB Very Big 7
-20 -10 0 10 20
0
0.5
1
Error
NB NM NS ZE PS PM PB
-0.5 -0.25 0 0.25 0.5
0
0.5
1
Change of Error
NB NM NS ZE PS PM PB
0 0.025
0
0.5
1
Gw
ZE VS S SB MB B VB
0 0.12
0
0.5
1
Fd
ZE VS S SB MB B VB
Fig.2. Membership functions of input and output CFC
Table 2. Rule of Conventional Fuzzy Controller
Gw Fd
Error
NB NM NS ZE PS PM PB
Ch
ang
e o
f E
rro
r NB 7 1 6 1 5 1 2 1 1 2 1 4 1 5
NM 7 1 6 1 4 1 2 1 1 2 1 4 1 5
NS 6 1 5 1 3 1 2 1 1 3 1 5 1 6
ZE 6 1 5 1 3 1 1 1 1 3 1 5 1 6
PS 5 1 4 1 2 1 1 2 1 4 1 6 1 6
PM 5 1 4 1 2 1 1 3 1 5 1 6 1 7
PB 4 1 3 1 2 1 1 4 1 6 1 7 1 7
3.2. Advanced Fuzzy Controller (AFC)
Advanced Fuzzy Controller has two fuzzy controllers.
They are Direct Fuzzy Logic Controller (DFLC) and
Self-tuning Fuzzy Logic Controller (STFLC). The
structure of AFC is shown in Fig.3.
Direct Fuzzy
Logic
Controller
Plant
du/dt
GwN
FdNder
y
eGe
Gde
eN
deN
GuHum
GuDehum
Gw
Fd
Self-tuning
Fuzzy
Logic
Controller
Fig.3. Structure of Advanced Fuzzy Controller
DFLC is most similar to CFC but the ranges of inputs
and outputs are different. In DFLC, the inputs (eN,
deN) and outputs (GwN, FdN) are normalized based on
Eqs.(12-15).
max 20N
e ee
e (12)
max 0.5N
de dede
de (13)
min
max min
0
0.025 0 0.025
w w w wwN
w w
G G G GG
G G (14)
min
max min
0
0.12 0 0.12
d d d ddN
d d
F F F FF
F F (15)
The rule for DFLC is the same with CFC. The fuzzy
membership functions for inputs and outputs are
shown in Fig.4.
-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1
0
0.5
1
Error
NB NM NS ZE PS PM PB
-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1
0
0.5
1
Change of Error
NB NM NS ZE PS PM PB
0 0.15 0.3 0.45 0.6 0.75 0.9 1
0
0.5
1
Gw
ZE VS S SB MB B VB
0 0.15 0.3 0.45 0.6 0.75 0.9 1
0
0.5
1
Fd
ZE VS S SB MB B VB
Fig.4. Membership functions of input and output of DFLC
The rule base STFLC is developed to tune the
DFLC’s inputs and outputs gains. The inputs are e
and de and outputs are Ge, Gde, GuHum, GuDehum. Its
inputs are the same with CFC’s, both range and
membership functions. The ranges for outputs are
based on the ranges for inputs and outputs of CFC.
The membership functions of inputs and outputs are
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Hội nghị toàn quốc về Điều khiển và Tự động hoá - VCCA-2011
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shown in Fig.5. The rule of STFLC is shown in Table
3. Table 3. Rule of STFLC for Ge and Gde
Ge Gde Error
GuHum GuDehum NB NM NS ZE PS PM PB
Chan
ge
of
Err
or
NB 1 4 2 3 5 3 7 7 5 1 4 1 3 1
1 4 2 3 5 3 7 7 5 1 4 1 3 1
NM 2 3 5 2 6 4 6 6 6 1 5 1 4 1
2 3 5 2 6 4 6 6 6 1 5 1 4 1
NS 5 2 6 3 4 5 5 5 4 2 6 1 5 1
5 2 6 3 4 5 5 5 4 2 6 1 5 1
ZE 7 2 6 3 5 5 7 4 5 5 6 3 7 2
7 2 6 3 5 5 7 4 5 5 6 3 7 2
PS 5 1 6 1 4 2 5 5 4 5 6 3 5 3
5 1 6 1 4 2 5 5 4 5 6 3 5 3
PM 4 1 5 1 6 1 6 6 6 4 5 2 2 3
4 1 5 1 6 1 6 6 6 4 5 2 2 3
PB 3 1 4 1 5 1 7 7 5 3 2 3 1 4
3 1 4 1 5 1 7 7 5 3 2 3 1 4
0 0.2
0
0.5
1
Ge
ZE VS S SB MB B VB
0 4
0
0.5
1
Gde
ZE VS S SB MB B VB
0 0.03
0
0.5
1
GuHum
ZE VS S SB MB B VB
0 0.16
0
0.5
1
GuDeHum
ZE VS S SB MB B VB
Fig.5. Membership functions of input and output STFLC
4. Simulation Results Using the mathematical model of the plant our
proposed approaches has been tested. From the initial
condition tin(0) = 27oC, in(0) = 65%. The target is to
follow a control reference set to ref1 = 40% and ref2
= 80%.
We test three fuzzy controllers:
Fuzzy1: Conventional Fuzzy Controller
Fuzzy2: Advanced Fuzzy Controller with self-
tuning Gde
Fuzzy3: Advanced Fuzzy Controller with self-
tuning Ge, Gde, GuHum, GuDehum
First, the controllers are tested without disturbance.
The simulation results for both references are
represented in Fig.6-7. In Fig.6, at the beginning,
error is negative big. So the dehumidifier runs with
max power. But there are some differences when error
is negative small. The settling time of Fuzzy3 is the
least. In Fig.7, the errors of Fuzzy2 and Fuzzy3 are
close to zero. The error of Fuzzy1 is about 1.75.
Second, we consider the response of the system
within disturbance. Disturbance is the effect of
outside air. There are two kinds of disturbances:
normal disturbance and unpredictable disturbance.
Normal disturbance is the change of outside
temperature, relative humidity and wind speed in a
day (following Table 4). The equations of them are
shown in below:
1
1
1 1
27.5 7.5sin
78.5 7.5sin
0.02 2.8 sin
o t
o
o gap wind w
t w t
w t
F A v w t
(16)
0 100 200 300 400 500 600 700 800 900 1000 1100 1200
40
42
44
46
48
50
5254
56
58
60
62
64
6668
Time (s)
Rela
tive H
um
idity(%
)
Fuzzy1
Fuzzy2
Fuzzy3
Fig.6. Response of Ref = 40 % without disturbance
0 100 200 300 400 500 600 700 800 900 1000 1100 120064
66
68
70
72
74
76
78
80
82
84
86
Time (s)
Rela
tive H
um
idity(%
)
Fuzzy1
Fuzzy2
Fuzzy3
Fig.7. Response of Ref = 80 % without disturbance
Unpredictable disturbance is the suddenly change of
temperature to2(oC), relative humidity o2 (%) and
volume flow rate of air Fo2 (m3/s) when someone
opens door or the weather becomes heavy.
Disturbance signals are in the form given in Fig.8.
In Fig.9-10, Fuzzy1 and Fuzzy2 take more time to
response than Fuzzy3.
We evaluate the quality of the controllers through
three parameters: Settling time
Error
Sum of square error
2
1
n
i
real i ref i
SSEn
with n = number of samples
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Hội nghị toàn quốc về Điều khiển và Tự động hoá - VCCA-2011
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0 240 480 720 960 1200
15
20
25
30
Time (s)
t o2(o
C)
0 240 480 720 960 1200
80
95
Time (s)
Phi o
2(%
)
0 50 290 530 770 1010 1200
0
5
10
Time (s)
Fo2(m
3/s
)
Fig.8. Unpredictable Disturbance Signals
Table 4. Relative Humidity and Temperature of Ho Chi
Minh City in year
Month Average RH
(%)
Max Average
Temperature
(oC)
Min Average
Temperature
(oC)
1 73.8 32 21
2 71.1 33 22
3 71 34 23
4 73.7 34 24
5 80.7 33 25
6 83.7 32 24
7 84.2 31 25
8 84.5 32 24
9 86 31 23
10 85.2 31 23
11 81.7 30 22
12 77.8 31 22
0 100 200 300 400 500 600 700 800 900 1000 1100 1200
40
42
44
46
48
50
5254
56
58
60
62
64
6668
Time (s)
Rela
tive H
um
idity(%
)
Fuzzy1
Fuzzy2
Fuzzy3
Fig.9. Response of Ref = 40 % within disturbance
0 100 200 300 400 500 600 700 800 900 1000 1100 120064
66
68
70
72
74
76
78
80
82
84
86
Time (s)
Rela
tive H
um
idity(%
)
Fuzzy1
Fuzzy2
Fuzzy3
Fig.10. Response of Ref = 80 % within disturbance
Table 5. Performance characteristics of system with three
Fuzzy Controllers
Settling time (s)
Error (%)
SSE
Fuzzy1 Fuzzy2 Fuzzy3
Wit
ho
ut
Dis
turb
ance
Ref = 40 %
213 175 107
0.5 0.1 0.15
5.0772 4.7607 4.8755
Ref = 80 %
140 100 62
1.10 0.10 0.03
2.4961 1.3167 1.0541
Dis
turb
ance
Ref = 40 %
264 234 155
1.00 0.52 0.63
26.5230 22.0187 21.1630
Ref = 80 %
130 150 120
1.10 0.45 0.20
3.1697 2.0932 1.3749
5. Conclusion In this paper, we simulated the greenhouse humidity
system and the fuzzy controllers. Conventional Fuzzy
Controller and Advanced Fuzzy Controller are both
satisfactory the requirement (error < 2%). But the
performance (Table 5) of Advanced Fuzzy Controller
is better: the setting time is smaller and the setting
error is less. We can choose Advanced Fuzzy
Controller for the fulfillment of complex task of
adaptive set point tracking and disturbance rejection
of a greenhouse humidity system.
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