Modelling of Greenhouse Temperature Control using Adaptive … Paper/Volume 52/Issue 03/IJES...

International Journal of Engineering, Applied and Management Sciences Paradigms

Volume 52, Issue 03, Quarter 03 (July-August-September 2018)

An Indexed and Referred Journal

ISSN (Online): 2320-6608

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Modelling of Greenhouse Temperature Control

using Adaptive-Neuro Fuzzy Inference System

Nidhi Rathi1 and Mr. Devender Kumar2

1M.Tech. Student, ECE, Gurgaon Institute of Tech. & Mgmt., Gurgaon, Haryana (India)

[email protected]

2Assistant Professor, ECE, Gurgaon Institute of Tech. & Mgmt., Gurgaon, Haryana (India)

[email protected]

Publishing Date: September 05, 2018

Abstract The greenhouse system is an important and effective

measure, which provides the plants with appropriate

environmental conditions for growing. To get proper

growth of crops, inside temperature and inside

humidity should be maintained at desired level with

respect to climate conditions. This paper presents a

design of a control system for a greenhouse using

geothermal energy as a power source for the plants

in order to reach predefined results for high yield, high

quality and low costs. Four controller techniques;

PI control, fuzzy logic control, artificial neural

network control and adaptive neuro-fuzzy control

are used to adjust the greenhouse indoor

temperature at the required value.

MATLAB/SIMULINK is used to simulate the

different types of controller techniques. Finally a

comparative study between different control strategies

is carried out.

Keywords: Artificial intelligent, Control,

Artificial neural network, Fuzzy logic control,

Adaptive neuro-fuzzy.

1. Introduction

Greenhouse crop production is a form of

protected agriculture where depending on the

technological level implemented, the degree of

environmental (aerial and root zone) control will

be sufficient to induce optimal plant growth. .A

greenhouse production systems usually refers to

fixed structures with a translucent glazing

material and with a varying degree of aerial

environment controllability (Jensen and Malter,

1995)[16].

The objective of greenhouse environment can be

further improved by adding heating, ventilation

and CO2 supply systems, in order to provide the

pre-eminent environmental conditions.

Conventional control techniques are difficult to

implement in greenhouse systems due to their

multi-variable and non-linear nature. Where

interrelations between internal and external

variables are complex (nonlinear physical

phenomena that govern these systems dynamics

are complicated). This provides justification for

the use of intelligent control techniques as a

good alternative. In this way, fuzzy logic as part

of AI techniques is an attractive and well-

established approach to solving control problems

[6]. In the last decade, a number of researchers

addressed the control design of the climatic

conditions in GHs. GH modelling and

identification have been studied by Ferreira et al.

(2002)[11], Cunha (2003)[8], and Bennis et al.

(2003)[4]. GH climate was developed using

intelligent controllers by Arvantis et al.

(2000)[2], Lafont and Balmat (2002)[19],

Sigrimis et al. (2002), Pasgianos et al. (2003),

Bennis et al. (2005) [5], Koutb et al. (2004)[18],

Bennis et al. (2003)[4], and Fourati and

Chtourou(2004)[12]. Numerous strategies and

control techniques have been proposed

(Márquez-Vera et al.[21], 2016; Revathi and

Sivakumaran, 2016; Coelho et al., 2005)[7],

model predictive control (Coelho et al., 2005;

Pin˜on´ et al.)[7], linear quadratic adaptative

control (Arvantis et al., 2000)[2], neural

networks control (Ferreira et al., 2002), fuzzy

control (Lafont and Balmat, 2004)[20]. In this

paper, modelling and control problem of

greenhouse indoor temperature are studied.





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Various control techniques (PI control, fuzzy

logic control (FLC), artificial neural network

control (ANNC) and ANFIS) are presented.

Moreover MATLAB/SIMULINK is used to

validate the proposed types of controller

techniques. Finally a comparison study between

the controllers performance is carried out.

2. Artificial Intelligent

Artificial Intelligence (AI) is the field of

Computer Science that attempts to provide

humanlike abilities in computers. One of the

primary means by which computers are endowed

with humanlike abilities, is a Neural Network

(NN). The human brain consists of a network of

over a hundred billion interconnected neurons

that can process small amounts of information

and then activate other neurons to continue the

process. Neural networks are often not suitable

for problems in which it is necessary to know

precisely how the solution was derived. A NN

can be very useful for solving problems for

which they can be trained (Barrera et al.,

2000)[3]. ANFIS, FLC and ANNs techniques,

are types of AI techniques.Fuzzy logic is an

effective in feedback control system and easier to

implement. The computational structure of fuzzy

logic is composed of Fuzzification, Inference

engine and Defuzzication modules. The control

system computational structure of fuzzy logic is

composed of Fuzzification, Inference engine

implemented here is a single input-single output

system with input as ‘error in temperature’and

‘Ventilation rate’ as output variable (Satyajit

Ramesh Potdar et al., 2017)[26]. FLC is

commonly used in control systems. To overcome

system uncertainty and parameter variations, the

fuzzy logic controller is used to model the

control objective based on human knowledge,

understanding the system responses. A fuzzy

controller is a system which works on numerical

data and converts it into a symbolic form through

a data base (fuzzification). A logic of decision-

making (rule base) is implemented, thus it is

possible to provide a symbolic answer which

must be converted into a numerical data

(defuzzification) (Lafont and Balmat, 2002)[19].

ANN’s can be used to solve complex problems

where noise immunity is important (Gupta et al.,

2003)[14]. Two methods of ANN training which

are supervised training and unsupervised

training. Supervised training needs training set

where the input and the desired output of the

network are provided for several training cases,

while unsupervised training requires only the

input of the network, and ANN is supposed to

classify the data properly (Dadios, 2012)[9].

ANFIS is a kind of ANN that is based on

Takagi–Sugeno fuzzy inference system.

Figure 1: ANFIS Controller Structure

Since it combines both neural networks and

fuzzy logic principles, it has potential to capture

the benefits of both in a single framework. Its

inference system corresponds to a set of fuzzy

IF–THEN rules that have learning capability to

estimate nonlinear functions. Hence, ANFIS is

considered to be a universal estimator (Coelho et

al., 2005)[7]. ANFIS control is a hybrid method

consists of two parts which are gradient method

applied to calculate input membership function

parameters, and least square method is applied to

calculate the parameters of output function.

The structure of ANFIS is shown in Fig. 3 (Mote

and Lokhande, 2012) [10][23].

The individual layers of this ANFIS structure are

described below (Mote and Lokhande,

2012)[10][23]:

Layer 1: Every node i in this layer is adaptive

with a node function

1

Oi = μAi(x) (24)





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(25)

where x is the input to node i, Ai is the

linguistic variable associated with this node

function μAi is the membership function of Ai,

and {ai, bi ,

ci} is

the

premise parameter set.

Layer 2: Each node in this layer is fixed node

which calculates the firing strength Wi of the

rule. i

The output of each node is the product of all

the incoming signals to it and is given by

(Mote and Lokhande, 2012[10][23]):

Oi2 =

wi = μAi (x) × μBi(y), I = 1, 2

Layer 3: Every node in this layer is a fixed

node. Each ith node calculates the ratio of the ith

rule’s firing strength to the sum of firing

strengths of all the rules. The output from the ith

node is the normalized firing strength given by

(Mote and Lokhande, 2012)[10][23]:

(27)

Layer 4: Every node in this layer is an adaptive

node with a node function given by (Mote and

Lokhande, 2012[10][23]):

Oi4=

wifiw (pi x+ qi y+ ri ), i= 1, 2

where wi is the output of Layer 3 and (pi, qi,

ri) is the consequent parameter set.

Layer 5: This layer comprises of only one fixed

node that calculates the overall output as the

summation of all incoming signals, i.e. (Mote

and Lokhande, 2012)[10][23].

(29)

Figure 2: Proposed PI controller using

MATLAB/SIMULINK

Figure 3: MATLAB/SIMULINK model of

fuzzy logic controller

3. Control system design

The effectiveness of the control depends on the

accuracy of the mathematical model which

describes the greenhouse dynamics variables

(Iga, 2008)[16]. AI can be used to control the

greenhouse climate in order to improve the

cultivation growth and to minimize the

production costs.

3.1. System design with PID controller

PID control is the method of feedback control

that uses the Proportional, the Integral and the

Derivative as the main tools (Hensel et al. 2012,

Beshi et al. 2011 and 2012). The purpose of

control is to make the process variable y(t) as a

suitable value named set-point yr(t). To achieve

this purpose, the manipulated variable u(t) is

changed at the command of the controller. In the

present application, the process variable y(t) is

the temperature and the manipulated variable u(t)

is the command of the controller. The

“disturbance” is any factor, other than the

manipulated variable, that influences the process

variable. In some applications, however, a major

disturbance enters the process in a different way,

or plural disturbances need to be considered. The

error e(t) is defined as: e(t) = yr(t) – y(t). Where,

yr(t) is the desired trajectory and y(t) is the

measured variable. The PID is a controller that

takes the present, the past, and the future of the

error into consideration. After digital

implementation was introduced, a certain change

of the control system structure was proposed and

was adopted in many applications. However, that

change doesn’t influence the essential part of the

analysis and design of the PID controllers. The

transfer function C(s) of the PID controller is:





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The proposed PI controller is built in

MATLAB/SIMULINK as indicated in Fig. 4.

The optimal control parameters are obtained

using the trial and error method. The

proportional gain (Kp) and the integral gain (KI)

are equal to 500 and 0.0001 respectively.

Table 1: Rule base of fuzzy logic controller

e Ce

NL NM NS ZE PS PM PL

NL NL NL NL NL NM NS ZE

NM NL NL NL NM NS ZE PS

NS NL NL NM NS ZE PS PM

ZE NL NM NS ZE PS PM PL

PS NM NS ZE PS PM PL PL

PM NS ZE PS PM PL PL PL

PL ZE PS PM PL PL PL PL

Figure 4: Membership functions of pts (e, and

ce) and output

3.2. System design with fuzzy logic

controller

The proposed MATLAB/SIMULINK model

of FLC for greenhouse is presented in Fig. 5.

Input variables of the FLC are the error (e) and

change of error (ce) which are normalized by an

input scaling factor. The design of the fuzzy

controller is related to the choice of knowledge

base, decision making logic, and defuzzification

mechanism. The rule base of the proposed FLC

is shown in Table 1. The inputs and output

variables are partitioned into seven fuzzy subsets.

They are presented by seven membership

functions (triangular shape) which are NL

(Negative Large), NS (Negative Small), ZE

(Zero), PS (Positive Small), and PL (Positive

Large) as indicated in Fig. 6 [10]. The

composition operation is the method by which

the controlled output is generated. The Max–Min

method is used. The output membership function

of each rule is given by the minimum.

Defuzzificaion for this system is the centre of

gravity method which is simple and fast.

3.3. System design with neural network

controller

The construction of the networks affects the

learning method and the efficiency of control

unit meanwhile the approximating error depends

on the number of neurons in the hidden layer and

the network inputs. ANN consists of three layers.

Six neurons (error, air temperature, wind speed,

solar radiation, air humidity and floor

temperature) are in the input layer, and fifteen

nodes are in the hidden layer. The output layer

contains one neuron for control flow rate of bare

tube heating system. The LOGSIGMOID

transfer function is used for hidden layer and

PURELIN for output layer. The learning

algorithm is carried by the back-propagation

technique.





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Figure 5: Mean Square Error of the Neural

Network

Figure 6: MATLAB/SIMULINK of the

Neural Network Control

Figure 7: The Proposed Structure of ANFIS

Controller

Table 2: Configuration of ANFIS controller

Configuration

Type Sugeno

Number of inputs 2

Number of outputs 1

Number of rules 9

And method Prod

Or method Probor

Implication Prod

Aggregation Sum

Defuzzification

Wtaver

Figure 8: ANFIS IF-THEN rule

Fig. 5 shows that the best validation

performance for the neural network which can be

obtained at epoch 1000. Hence the network is

working well. MATLAB/SIMULINK of the

neural network is presented in Fig. 8.[10]





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3.4 System design with ANFIS controller

Selection of the appropriate membership

functions and the rule base affects ANFIS

controller accuracy. In the ANFIS controller,

neural network algorithm is used to select a

proper rule base which is accomplished using the

back propagation algorithm. The proposed

structure of ANFIS controller is shown in Fig. 9.

The configuration of ANFIS controller is

indicated in Table 2[10]. The used fuzzy

inference technique is Sugeno method. A hybrid

training algorithm is used where a combination

of the gradient descent algorithm and a least

squares algorithm is used for an effective search

for the optimal parameters. The main benefit of

ANFIS is that it converges much faster, since it

reduces the search space dimensions of the back

propagation method used in neural networks. The

suggested ANFIS If-Then rules are

Figure 9: Surface viewer of ANFIS controller

Figure 10: MATLAB/SIMULINK model of

the heating system using fuzzy controller

Figure 11: MATLAB/SIMULINK Model of

the Heating System using ANN controller

Figure 12: MATLAB/SIMULINK Model of

the Heating System using ANFIS Controller

Table 3: Meteorological data of Ras Sedr

Time

(h)

Air

temp.

(◦C)

Radiation (w/m2) Wind

speed

(m/s)

Humidity

(%)

1 11 0 5.08 49

2 11 0 4.8 50

3 11 0 4.8 50

4 11 0 4.9 52

5 9 0 5.3 54

6 10 2 5.6 56

7 10 39.48 5.8 58

8 10 195.6 6.2 48

9 13 470.05 6.3 40

10 15 535.93 6.8 31





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11 17 637.22 7.2 33

12 17 680.52 7.5 34

13 17.5 675.56 7.6 36

14 18 606.13 7.7 34

15 18 482.15 7.8 32

16 17.5 317.7 7.6 33

17 17 151.78 7.5 36

18 16 17.26 7.3 38

19 15 0 7 42

20 14 0 6.6 42

21 13 0 6.011 42

22 12 0 5.6 43

23 12 0 5.4 43

presented in Fig. 10. Eight If-Then rules and

three Gaussian bell type of membership function

are used. The obtained surface viewer of ANFIS

controller is shown in Fig. 11.

4. Simulation Results

In order to predict the greenhouse

performance, MATLAB/SIMULINK is used to

model and simulate the heating system. Three

proposed controllers are simulated as shown in

Figs. 10–12 [10]. The simulated model is carried

out to test the system performance under various

climatic conditions. Mathematical modelling of

greenhouse subsystem using

Figure 13: Mathematical modelling of

greenhouse system using

MATLAB/SIMULINK

Table 4: MAE & RMSE of Controller

Techniques

Controller type MAE RMSE

PI 0.5851 1.595

ANN 3.3927 9.8941

FLC 0.0716 0.5998

ANFIS 0.0074 0.1977

MATLAB/SIMULINK is presented in Fig. 13.

Meteorological data (air temperature, wind

speed, and relative humidity) are obtained from

New and Renewable Energy Authority (2005) as

shown in Table 3.

The heat loss due to convection; conduction to

the greenhouse soil; condensation; ventilation;

infiltration and long wave radiation is given in

Fig. 14. It is clear that, the infiltration loss

represents the minimum heat loss component





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Figure 14: Greenhouse energy loss variation

over the day

Figure 15: Greenhouse indoor temperature

variation

Followed by the conduction to greenhouse soil

heat, while the condensation loss represents the

largest percentage of total greenhouse heat loss.

Four controller techniques, PI, neural

network, fuzzy, and ANFIS are used to adjust the

greenhouse desired temperature. The response of

the greenhouse indoor temperature is given by

Fig. 15 for various control techniques. It is clear

that, the response under proposed ANFIS

controller is very smooth and nearly the set point

than that given by PI, neural network, and fuzzy

controller. Table 4 indicates the RMSE and MAE

of the proposed controllers. The RMSE and

MAE of ANFIS controller are very smaller than

given by the other controllers. The simulation

results demonstrated that, the system response of

the ANFIS controller is very acceptable and

faster than others.

5. Conclusion

Due to the physical dynamics involved in a

greenhouse, the synthesis of a climate controller

becomes a sophisticated task using traditional

control techniques. This paper represented an

approach to apply a conventional and AI

techniques in a greenhouse climate control. PI

control, fuzzy logic control (FLC), artificial

neural network (ANN) and adaptive neuro-fuzzy

(ANFIS) control represent useful tools for

solving the nonlinearity problem of greenhouse

modelling. Training data for the present study for

artificial neural network and ANFIS control was

randomly collected from several simulations in

MATLAB/SIMULINK. The simulation results

proved that ANFIS controller can be applied

successfully to control the greenhouse indoor

temperature because of its effectiveness and fast

response time.

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