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103 Int. J Latest Trends Agr. Food Sci.

Vol-2 No 2 June 2012

Using Proportional Integral Derivative and Fuzzy Logic with Optimization for Greenhouse Temperature Control Abdul Rahman O. Alghannam Head of Department of Department of Agriculture Systems Engineering, College of Agricultural and Food Sciences, King Faisal University, P.O. Box 420, Al-Hassa 31982, Saudi Arabia. Email: [email protected], [email protected]

Abstract - Several Studies were undertaken to evaluate

Keywords: Greenhouse environment, theoretical modeling,

the efficiency of different greenhouse control systems.

control system, sensible heat modeling,

Since one of the fastest growing modern technologies in

temperature modeling.

control systems used in greenhouses as well as in other

1. Introduction

fields is fuzzy logic, further research is needed to study the accuracy of the system compared to PID control

Generally, control systems with on/off switching

system for the same environment and modeling them

have been satisfactory for greenhouse climate

with the same mathematical model. In this research

management for some growers; however, for research

fuzzy logic controller was tested theoretically and

and commercial purposes, more precise control is

experimentally compared with four PID controllers’

needed

settings namely, P, PI, PD, and PID in terms of their

switching system used in greenhouses environmental

accuracy

control are; they are incapable to maintain set point

and

feasibility

to

control

greenhouse

[1]

.

Some of the drawbacks of the on/off

temperatures accurately and due to the non-linearity

temperature. An optimization procedure was used to determine

of this system, it is hard to design a controller to [2]

the best control parameters for PID and Fuzzy Logic

maintain a fixed temperature

controllers. Five tests were conducted to cool the

advantages of this system are its easiness to use and

greenhouse using evaporative cooling system during

maintain and its affordability [2].

summer season.

.

However, the

On the other hand, fuzzy logic is simple to use if

Results of the research indicated that PI and PD

incorporated with analog-to-digital converters, and 4-

controllers were the most efficient controllers in the

bit or 8-bit one-chip micro controllers. This can

controllers output or ventilation rate behavior with a

easily be upgraded by changing rules to improve

zero overshoot and small maximum and minimum

performance or add new features to the system . In

error from the set point. However, the Fuzzy Logic

many cases, fuzzy control can be used to improve

controller made the closest mean to the set point. No

existing controller systems by adding an extra layer

significant difference was noticed between the control

of intelligence to the current control method.

behavior of fuzzy logic controller and the PID

Furthermore, fuzzy logic control systems are still

controllers.

young in the climate control of greenhouse.

Therefore,

fuzzy

logic

controller

is

considered an equivalent and alternative to PID

research conducted by

controller in greenhouse climate control.

6%

was

achieved

[3]

In a

, an energy saving of about

in

greenhouse

set

point

determination using fuzzy sets. Conducted similar ___________________________________________________________________________________ International Journal of Latest Trends in Agriculture & Food Sciences IJCSET, E-ISSN: 2049-5684 Copyright © ExcelingTech, Pub, UK (http://excelingtech.co.uk/)

research in livestock buildings

[4]

. Recently,

[5]

has

104 Int. J Latest Trends Agr. Food Sci.

Vol-2 No 2 June 2012

developed a theoretical approach for fuzzy logic

control systems, a very simple greenhouse sensible

controller for broilers and greenhouse systems. In

heat

most of the commercial greenhouses, traditional

implemented to reduce other parameters or coupling

control systems may be sufficient for the parameters

effect

control. However, more precise systems are needed

Furthermore, for the scope of this project summer

for other applications such as propagation greenhouse

conditions were implemented and the original heat

systems. Furthermore, fuzzy logic systems could be

balance loads was reduced to the following heat loads

used as an alternative controller for the greenhouse

as shown in Figure 1

system

[5]

.

With

the

fast

growing

balance on

equation

the

developed

precision

of

the

by

[6]

was

controllers.

computer

applications in agriculture, there is a need to test and evaluate more advance controllers to reach the best and most affordable greenhouse climate control

qso + qvi = qw + qvo

(1)

The transient thermal behavior of inside air temperature:

systems. The objective of this research is to study the ρC p V

accuracy of the fuzzy logic system compared to the

dTi = aIA f − UA s(Ti − To)− ρC p V& (Ti − Tc) dt

(2)

PID for the same environment and modeling them with the same mathematical model. The two systems

Where:

were applied theoretically and practically using an analog output signal, which constitutes a requirement for more robust control system.

ρC p V

Since the

temperature is one of the important parameters, this research will be limited to the control of this variable and then the control could be extended to include other parameters.

dTi dt = Change of Storage

Ti = Inside air temperature. To = Ambient air temperature. Tc = Air temperature coming out of the evaporative cooling pad.

qs qw qvo qvi

Figure 1. Sensible heat balance for a greenhouse summer cooling and ventilation.

2. Materials and Methods

ρ = Air density. CP = Specific heat.

1.

Theoretical Modeling

There are various mathematical models that differ in their representation to the actual greenhouse environment, their accuracy, and their contained parameters.

However in this study,. Since the

essence of this research is to study and compare the

V = Volume of greenhouse. a = Solar heating efficiency. I = Solar irradiance. Af = Area of floor. U = Greenhouse overall heat transfer coefficient. As = Area of greenhouse covering. V&

= Ventilation rate.

105 Int. J Latest Trends Agr. Food Sci.

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equations. The outputs of the modeling were the best-

PID Controller The Proportional, Integral, and Derivative are

optimized limits for the input and output.

usually used in a variety of combinations with one another to achieve the right control process.

the greenhouse was the Integral of the Absolute

Theoretical methods were based on an algorithm designed by

[7]

The decided performance or criterion index for

. A greenhouse theoretical PID

magnitude of the Error (IAE) for two reasons simplicity and error size.

controller was developed using MATLAB. The PID parameters were tuned automatically through the program using performance index IAE for two

t

IAE = ∫ e dt 0

reasons simplicity and error size. The theoretical

(3)

model was inclusive to most applicable disturbances associated with greenhouse summer energy balance

Height defuzzification was implemented to

Figure 2. Greenhouse Dimensions loads. The output of this modeling, were PID tuned

get the crisp output.

parameters and the IAE associated with the tuning. The tuned parameters were then implemented in the experimental

PID

controller.

Four

different

optimization PID procedures were implemented theoretically and experimentally and compared

m

∑c w i

y=

i

k =1 m

∑w

i

k=1

(4)

regarding their feasibility and accuracy. These four

2. Materials

procedures are P, PI, PD, and PID.

This research took place in a greenhouse in the Plant Science Farm at the University of Idaho. The

Fuzzy Logic Controller (FLC) A greenhouse theoretical Fuzzy Logic Controller was also developed using MATLAB.

The FLC

parameters were tuned through the program using the same

performance

index

and

energy

balance

greenhouse had galvanized steel frames and double layer air inflated polyethylene cover. The dimensions of the greenhouse are shown in figure 2.

106 Int. J Latest Trends Agr. Food Sci.

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Converter. 37 Pin Screw Terminal accessory with

2.1. Greenhouse Dimensions In order to install a complete cooling /

vertical 37 Pin Male Connector.

ventilation greenhouse control system, the following components were required: 2.2. Evaporative

2.5. Computer Hardware/Software

Cooling

and

A Pentium-100 MHZ PC-computer with 64 MB

Ventilation

RAM and 1 GB Hard Drive. Labtech Notebook

System 20 pieces 0.9144 m x 0.3048 m x 0.1016 m of

control software. MATLAB programming software.

3. Experimental Methods

Munters Kool cells. Stainless steel gutters, drain, sides, top cover. Two 1/3 HP sump pumps. PVC fittings and pipes. Four 0.9144 m x 1.2192 m aluminum shutters operated with an ON/OFF CAM motors. Two 189.27 liters capacity tanks Figure 2. Two exhaust fans with a flow of 2.175 m3/s each. Two exhaust fan motors are Dayton Fractional ½ HP

The greenhouse has galvanized steel frames and double layer air inflated polyethylene cover. One variable frequency Boston Gear ACX drive (inverter) was used for the control of the fans motors’ speed. This inverter provides adjustable speed for control of conventional AC motors.

Motors Sensors’ Calibration 2.3. Control System components The HX11C OMEGA temperature sensors were Four HX11C OMEGA temperature sensors. One variable frequency Boston Gear ACX drive (inverter) for the control of the motors’ speed inverter provides adjustable speed for control of conventional AC motors. The controller converts the fixed frequency and voltage of the AC line power source to a sine coded pulse width modulated adjustable voltage and

designed to give 0-5 volt reading. These sensors were first calibrated using constant temperature media. Afterwards, the calibration outputs were converted to Celsius

temperatures

using

linear

regression.

Thereafter, the regression slope and intercept were placed in the interface icon for the display purpose and control.

frequency output. With the revolution in microcomputers, various Three 120volt Relays for the operation of the back shutters, pumps, and front shutters. Five small (5 volts) relays; three of them are used to operate the bigger relays (120volts) and the other two to operate the drive and the heater.

types of automated control software’s became available. Labtech Notebook is one of those control software programs that provide an interface between the user and the control instrumentation system. This software is an object oriented that consists of icons

Wires, transistors, resistors, and diodes, op-amp chips

that

are

designed

to

work

with

certain

were used in circuits to amplify the computer signal

instrumentation computer boards. Various types of

and protect the computer boards.

input and output icons are available. Input icons enable the user to read the signal of the sensor including

2.4. Data Acquisition CIO-DAS08

ADC

(Analog

to

Digital

Converter). CIO-DAC02 DAC (Digital to Analog Converter). Two back plates with 37 Pin Cable used with the Analog to Digital Converter. One back plate with 25 Pin Cable used with Digital to Analog

resistance

analog,

digital,

measurement,

counter, RTD

frequency,

measurements,

thermocouple reading, and strain gage reading. The output icons are designed to implement control processes such as open loop, closed-loop analog or digital, On/Off icons to control relays or actuators. Furthermore, the open and close loop icon can send a stream of analog output to the control device. The

107 Int. J Latest Trends Agr. Food Sci.

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interface of the system is in Engineering Units;

For the fuzzy logic control procedure, the

however, the true input and output are in 0-10 DC

optimum ventilation rates calculated by FLC were

volts.

converted to voltage using the regression curves. The resulting voltages were then plugged in C-Icon program to apply the fuzzy logic rules and send them

Control Computer

Digital Output

Analog A/D and D/A

Relays

Inverter

Sensors’

Greenhouse

Pad

Fan

to the inverter, which controlled the speed of the fans. Figure 3. Flow chart of the control algorithm. Control algorithm

System Response Measurement and Parameters: The deviation of a system response was evaluated by standard performance measures such as

The control algorithm was designed to do the following: Four On/Off icons were assigned to send

rise time, peak time, overshoot, settling time, and steady state error.

signals to the relays that control pumps, heater, back shutters, and front shutters as shown in Figure 3.

1.

The rise time The rise time is the time that the response of

One analog closed-loop icon was assigned to control the inverter by sending 0-10 volts as decided by the closed-loop control. Through this analog icon,

a system takes to reach the desired set point of the system. 2.

The Peak Time

the PID parameters were tuned until the optimum tuning was reached. Furthermore, the PID values of the theoretical simulation were directly plugged into that icon to compare the two methods.

The peak time is the time that a response of a system takes to rise to the peak of the response.

108 Int. J Latest Trends Agr. Food Sci.

3.

Vol-2 No 2 June 2012

Overshoot

the experimental curve demonstrates a significant

The overshoot is the temperature value with which the response of a system overshoots the

overshoot with good tracking to the set point with a mean of 24.85°C.

Furthermore, the experimental

curve shows ripples that are due to the delay effect

desired set point.

mentioned earlier. 4.

Settling Time The experimental results of the ventilation rate

The settling is the time needed for a system response to settle with set point without fluctuation. 5.

demonstrated less fluctuation and good middle values, which indicates a good control of the fan speed to keep up with the set point as shown in

Steady State error

The steady state error is the error appears after the decay of the transient response and after continuous pattern is achieved.

Figure (6). Proportional Derivative Controller Test (PD) The implementation of the optimized values PD theoretically and experimentally resulted in no

4. Results and Discussion The following tests were conducted at noon or just afternoon to test the cooling system and controller in the extreme summer conditions. All the tests were performed to cool the greenhouse from 40 °C to 25 °C to assure fair comparison.

overshoot for the experimental air temperature drop as shown in Figure (7).

Furthermore, the mean

temperature around the set point was 25.32°C. This test showed a good control of all the curves because of the few ripples existed with the delay effect. Smooth steady theoretical behavior is shown in the

Proportional Controller Test (P)

graph.

Applying the best value of the P theoretically

The experimental ventilation rate shown in

and experimentally, gave the results depicted in

Figure (8) indicates an optimum control among all

Figure (4). The theoretical graph shows a smooth

the experimental control curves. In this curve, there

temperature drop and an overall steadiness, except a

is less fluctuation or on/off effect, whereas most of its

little overshoot below the set point. On the other

ventilation rate was around the middle 2.2 – 2.7 m3/s.

hand, the experimental results of this test show a

Full capacity of ventilation 4.35 m3/s is demonstrated

close overall agreement with the theoretical except

from the beginning of the test to the attainment of the

that the curve is not smooth.

set point in a time exactly as the drop time of

An overshoot of

0.093°C is observed in the system response. A delay existed in the graph that is probably due to the uneven air distribution and delay of the fan response. A mean of 24.88 °C was obtained during this test.

representation

using

Proportional Integral Derivative Controller Test (PID) The behavior of temperature at the optimum PID

Proportional-Integral Controller Test (PI) Temperature

temperature, which is 156 seconds.

values is shown in Figure (9). Smooth theoretical the

PI

temperature behavior is shown in the figure with

optimized parameters values are depicted in Figure

slight

(5). The theoretical simulation and the actual data

Experimentally there was more overshoot and delay

amount

of

overshoot

but

no

ripples.

are showing a very close agreement. However, there

fluctuations around the set point. Little delay existed

is a significant drop time difference between them.

at the beginning of the experiment for about 10

The theoretical simulation curve shows a general

seconds, which might be attributed to the air mixing

steady behavior except that it has a little overshoot

delay. The PID controller made a good tracking to set

below the set point and ripples around it. Whereas,

point with a mean of 24.79°C.

109 Int. J Latest Trends Agr. Food Sci.

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mean temperature to the set point, which are 24.9.

Fuzzy Logic Controller (FLC) The optimized fuzzy logic control parameter was plugged into the theoretical and experimental

However, it made highest settling time and overshoot of 228 seconds and 1.1865°C respectively.

5. Conclusion

controllers and resulted in the two curves depicted in Figure (10). Some wrinkles existed in the theoretical

1.

All controllers were able to control the

curve at the intersection with the set point; however,

greenhouse air temperatures precisely within a

the rest of the curve is smooth. On the other hand,

reasonable range of error.

experimental test of the fuzzy logic control, showed

2.

The PI controller showed the shortest overshoot

good agreement with the theoretical except little

time, which is 126 seconds, with no overshoot,

divergence from the experiment start to about 100

short settling time, and minimum error.

seconds.

Good tracking of the set point is

demonstrated

with

an

average

of

3.

The P controller was able to keep lowest error

4.

The PD controller showed no overshoot and

24.92°C.

Nevertheless, the experimental ventilation rate in

off the set point. minimum error.

Figure (11) shows a better behavior from the start time until the drop time of the temperature, which is

5.

The FL controller was easy to implement and

184 seconds. Yet all of the control was similar to an

was very effective in tracking the set point.

on/off and no in between values of ventilation rate.

Moreover, it made the best average set point.

Comparison between the Five Controllers

6.

The little divergence between the theoretical and experimental results could be attributed to the

All the theoretical curves are within the same

unstable disturbances of the solar radiation, heat

drop time but different wrinkling behavior around the

transfer, and possibly turbulence that resulted

set point. Looking at the experimental curves P and FLC gave similar drop time behavior, whereas, the

from the distribution fan currents. 7.

The delay in the fan response was due to the fact

other controllers namely, PI, PD, and PID were

that the data were collected every two seconds

almost coincident in their drop.

and the fan takes five seconds to reach its full

Table (1) shows the comparison between the five

speed and approximately another two seconds to

control systems theoretically and experimentally. The

redistribute the new temperature wave to the

five essential system response measurements to

entire greenhouse. This delay is what appears as

distinguish between curves are rise time, overshoot,

ripples in the air temperature drop curves.

settling time, steady state error, and mean. The curve

8.

curves can be attributed to the delay in pad

that has short rise time, settling time, minimum

saturation and the air mixing.

overshoot, minimal error, and close mean to set point is considered the best controlled curve. From the

The beginning delay in some of the experimental

9.

Although there was a difference between the

table, PI controller and PD controller showed the best

theoretical and the actual greenhouse models, the

overall behavior in rise time, settling time, overshoot

optimization through the mathematical model

and errors, which respectively 126-156, 132-194, 0-0.

showed good control of the greenhouse climate

This means that they are better to be used in the

and shortened the tuning time, whereas, the

greenhouse control. On the other hand, the P

experiment might last days to get good tuning

controller showed the least maximum and minimum

parameters through manual tuning because of the

error which are -0.812 to 1.18°C from the set point

weather dependency.

whereas the PID showed the highest Maximum and

10. Taking into consideration the simplicity of the

minimum error which are –1.48 to 2.34 from the set

fuzzy logic controller design and adjustment, the

point.

fuzzy logic controller has the advantage that it

The fuzzy logic controller has the closest

110 Int. J Latest Trends Agr. Food Sci.

Vol-2 No 2 June 2012

can be designed and adjusted by some one who

3136. St. Joseph, Michigan, USA.

doesn’t have engineering background. In other

[6] Albright L. D. 1990. Environment Control for

words

it

doesn’t

need

a

background

in

Animals and Plants. The American Society

mathematical modeling to implement it.

of Agricultural Engineers. 2950 Niles Road, St. Joseph, Michigan 49085-9659,

References

USA.

[1] Takakura, T., T. O. Manning, G.A. Giacomelli,

[7] Edwards D. and H. T. Choi. 1997. Use of Fuzzy

And W. J. Roberts.1994. Feedforward

Logic to Calculate the Statistical Properties

Control for A Floor Heat Greenhouse.

of Strange Attractors in Chaotic Systems.

Transactions of the American Society of

Fuzzy Sets and Systems 88(1997) 205-217.

Agricultural Engineers 37(3): 939-945. [2] Chao, K. and R. S. Gates. 1996. Design of Switching Control Systems for Ventilated Greenhouses. Transactions of the American Society

of

Agricultural

Engineers

39(4):1513-1523. [3] Ehrlich, H., M. Kuhne, and J. Jakel. 1996. Development of Fuzzy Control System for Greenhouses. Acta Horticulturae 406:463470. [4] Zhang, Y. and L. Christianson. 1991. An Introduction of Fuzzy Control for moisture Balance Ventilation. American Society of Agricultural Engineers Paper No.91-4077. St. Joseph, Michigan, USA.

[5] Gates, R. S., K. Chao, and N. Sigrimis. 1999. Fuzzy Control Simulation of Plant and Animal Environments. American Society of Agricultural Engineers Paper No.99-

Tables Table 1. Comparison between the five control systems theoretically and experimentally. Controller P PI PD PID FLC

Theoretical Experimental Theoretical Experimental Theoretical Experimental Theoretical Experimental Theoretical Experimental

Rise Time (s) 182 190 182 126 194 156 182 132 192 184

Overshoot (°C) 0.1 0.0935 0.1 0.0 0.1 0.0 0.1 0.3364 0.1 1.1865

Settling Time(s) 192 194 192 132 226 194 192 144 196 228

Mean (°C) 25 24.8 25.1 24.8 25.2 25.3 25 24.7 25 24.9

Set Point Error (°C) Max Min 0.1 -0.1 1.18 -.812 0.2 -0.3 1.55 -1.12 0.0 -0.4 1.42 -1.48 0.2 -0.2 2.34 -1.48 0.1 -0.1 1.42 -1.30

111 Int. J Latest Trends Agr. Food Sci.

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Figures T h e o rit ic a l a n d E x p e rim e n t a l R e s u lt s u s in g P = 4 3 7 . 7 50

45

Temperature (°C)

40

35

30

25

20

0

100

200

300

400

500 T i m e (s )

600

700

800

900

1000

Figure 4. Theoretical and experimental results of air temperature behavior using P controller T h e o r i t i c a l a n d E x p e r i m e n t a l R e s u lt s u s in g P = 2 1 . 9 3 7 5 ; I= . 0 0 0 7 6 5 6 2 ; 50

45

Temperature (°C)

40

35

30

25

20

0

1 00

200

3 00

40 0

500 T im e (s )

60 0

700

800

900

1 000

Figure 5. Theoretical and experimental results of air temperature behavior using PI controller Th e E x p e rim e n t a l V e n t ila t io n R a t e o f th e P I C o n t ro lle r 5 4.5 4

Ventilation Rate m3/s

3.5 3 2.5 2 1.5 1 0.5 0

0

100

200

300

400

500 Tim e (s )

600

700

800

900

1000

Figure 6. Experimental results of ventilation rate using PI controller Th e o rit ic a l a n d E x p e rim e n t a l R e s u lt s u s in g P = 1 0 . 0 6 8 8 , D = 0 . 0 1 8 7 50

45

Temperature (°C)

40

35

30

25

20 0

100

200

300

400

500 Tim e (s )

600

700

800

900

1000

Figure 7. Theoretical and experimental results of air temperature behavior using PD controller

112 Int. J Latest Trends Agr. Food Sci.

Vol-2 No 2 June 2012

Th e E x p e rim e n t a l V e n t ila t io n R a t e o f t h e P D C o n t ro lle r 5 4 .5 4

Ventilation Rate m3/s

3 .5 3 2 .5 2 1 .5 1 0 .5 0 0

100

200

300

400

500 Tim e (s )

600

700

800

900

1000

Figure 8. Experimental results of ventilation rate using PD controller. T h e o ri t i c a l a n d E x p e ri m e n t a l R e s u l t s u s i n g P = 4 8 3 . 0 5 5 6 I= . 0 0 1 1 D = 0 . 0 9 8 3 50

45

Temperature (°C)

40

35

30

25

20

0

100

200

300

400

500 T i m e (s )

600

700

800

900

1000

Figure 9. Theoretical and experimental results of air temperature behavior using PID controller T h e o ri t i c a l a n d E x p e r i m e n t a l F L C R e s u l t s , E r ro r = 0 . 0 0 2 2 , V e n t i l a t i o n R a t e = 4 . 1 7 3 8 50

45

Temperature (°C)

40

35

30

25

20

0

100

200

300

400

500 T i m e (s )

600

700

800

900

1000

Figure 10. Theoretical and experimental results of air temperature behavior using FLC controller. T h e E x p e ri m e n t a l V e n t i la t io n R a t e o f t h e F u z z y L o g ic C o n t ro ll e r 5 4.5 4

Ventilation Rate m3/s

3.5 3 2.5 2 1.5 1 0.5 0

0

100

200

300

400

500 T im e ( s )

600

700

800

900

1000