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Fuzzy Control for Temperature and Humidity in Model of Chicken room using Lab VIEW

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คำ�สำ�คัญ : ฟซัซีค่อนโทรล : ก�รควบคมุระบบอัฉรยิะสำ�หรบัควบคมุอุณหภมูแิละคว�มชื้นในโรงเรื่อนเล้ียงไก่Fuzzy Control for Temperature and Humidity

in Model of Chicken room using LabVIEW

Chaiyos Commee Siripun ThongchaiGraduate Student, Teacher Training in ElectricalFaculty of Technical Education, Engineering Dept. King Mongkut’sKing Mongkut’s University of University of Technology Technology North Bangkok. North Bangkok,Tel : 02913-2500, Thailand Srp@Kmutnb.acEmail : chaiyos8683@hotmail.com

Abstract

by analyzing the difference of an air conditioning control system used in chicken room’s farm it is showed that the current method used in the air is not appropriate for temperature and humidity control in chicken room’ farm. The intelligent control method is

given based on the improved Fuzzy control Techniques. The detailed design of The fuzzy control of temperature and humidity controller are discussed in this paper. The Fuzzy control Techniques does not need the object’s Mathematic Model but applies control based on Fuzzy Rule and Member ship function. On basic fuzzy control algorithm. The application results Proved that control system has good performance in the fast response and robustness to disturbance.

key word Fuzzy control : intelligent temperature – humidity control chicken room’ farm.

1. Introduction I Chicken Farm The conditioning system used in ordinary building is sample, and it always applies pre – evaluation control. (1) and it was used for chicken house for get the best performance which Chicken farms are establishments that operate integrated poultry farms. Are the production of salted eggs, poultry processing. Preliminary studies showed that egg production process. Farm raised in chicken eggs is closed. Evaporative Cooling System (Evaporative Cooling System) is a party in a closed system has helped prevent outbreaks of avian influenza. To chickens, however, has

intensified over the past actions. Chicken farms have problems with the integration of air quality in buildings as housing. 1. Use control system. The equipment from abroad. Which has been designed. The program of work to work well in cold area. When used in Thailand, thus not available as to the target. Often the devices to work properly in some conditions the power consumption also list trade Brett maintenance and replacement is difficult to buy time from overseas vendors often lack expertise in environment. culture of Thailand 2. The current house there is a difference of temperature and humidity

environment. By the end of temperatures higher than 6 Celsius end of the chicken under conditions at higher Affecting the quality and quantity of poultry products from the inferior end of and a higher mortality rate page chicken stall 3. The accumulation of ammonia from the chicken until the end of because improper ventilation. The head end of with different air quality. Will result in stress and sick chicken easy Need to import drugs from abroad, often more expensive. 4. The changes in the external environment because of seasonal housing and environment of the world. Result in housing that does not properly controlled. Can not maintain consistency in the proper environment. Housing environment has changed in the proper environment. Housing environment has changed the environment under the conditions of the above issues affect the number of chicken deaths in the high culture. Kai Lounge likely will end production at less than

chicken page fold And quality of eggs Weight and yield decreased egg production. To resolve the above. Experts. Proposed to be constructed in a controlled environment such as housing. Division and distribution equipment for air into the house as appropriate. And control systems controlled by microcomputer Can be programmed to create the appropriate environment for the chickens. Raised in an environment of Thailand Chest quickly Easy to make chicken and strain the patient mortality rate decreased productivity.

2. Fuzzy Logic Controller. Most of the real world are nonlinear. A Linear classic controller can be tuned to give good Performance for these Processes. at the Particular operating point or for a limited period of Time. The controller must be periodically ventured if the operation point changes 1. An interesting Approach is to used Fuzzy Logic controller (FLC) to control these processes 2. Fuzzy

Logic Enables us to incorporate human intelligence in to Automatic control 3. by means or fuzzy Algorithms based on intuition and experience of operator 1. The nonlinear system or system which mathematical models are unknown or too complex to be treated analytically. Can be easily controlled.

There are two ways to design fuzzy logic controllers by using mathematic models or linguistic models which emulate the behavior of a skilled operator. The experience of operators working with the process Allows to find the fuzzy controller structure and the linguistic rule base when the fuzzy controller parameters can be tuned by an on line optimization to obtain a better behavior of plant 1

To design a fuzzy controller it is necessary to decide for the following aspects :

- forms of membership functions:

- logic operators;- inference method:

- defuzzification method.

A fuzzy logic controller contains a number of sets of parameters that can be altered to modify the controller performance. These parameters are:

- the scaling factors each variable:

- the fuzzy set representing the meaning of linguistic values:

- the fuzzy rules.For a successfully

work number of parameters to be tuned should be as small as possible.

There are some drawbacks to fuzzy controllers design mentioned above. Sometimes a reliable linguistic model of the operator’s control strategy can not be obtainable and at othcrtimes the operator experience may not allow him to control some significant process changes.

An approach to solve these problems is provided by an adaptive fuzzy controller that modifies the fuzzy rules. Such controller is called a self – organizer one

It can cither modify an existing set of rules

or it can start without any rules and based on a learning algorithm it is capable to generate and to modify the control rules evaluating the system’s performance (4).

The adaptation algorithm is based on a credit value or a delay in reward parameter to be added in the past control output in order to improve the present system performance.

Both approaches have been applied to control the temperature of the following processes: an electrical heating battery and an electric oven

3. Temperature – humidity control systems Description.Thermal processes are classified in two classes : Thermal processes with heat elimination and without heat elimination 1 This paper present an elimination heat with show in fig. 1.

Fig 1. The temperature and humidity control systems.

Form fig 1. has a 1000 watt electrical power heating resistant, a two speed for introducing cold air and device for modifying the exit of heated air and pump used to spray water into the room.

4. Studies of temperature and Humidity coupling. The fig 2. show that cold store temperature (T) and relative humidity () after disturbance and changes of coding and fan power The simulation model studies of the dynamic behavior of the cold store under The simulation model allows, e.g. studies of the dynamic behavior of the cold store under the

influence of disturbances or after changes of set points. In Fig. 2, results of such simulations are given in a plot of the cold store temperature T versus the relative humidity The curves show results of heating (a), humidification (b), increased cooling power (c) and increased fan power (d) starting in each case from the same stationary operating point. Fig. 2. demonstrates two important effects:

* Change in temperature directly leads to change in relative humidity (curve a), while humidification only causes small changes in temperature (curved).

* Changes in compressor power of refrigerator (curve c) and fan power (curve d) have an appreciable influence both on temperature and relative humidity. Hence, it is possible to effectively change relative humidity in cold store solely by actions of the evaporator fan.Studies of this kind allow the development of strategies for temperature and humidity control, which consider the physical coupling between

these variables. These control strategies were implemented as a fuzzy controller.

Fig. 2 Cold store temperature T and relative humidity O after disturbances and changes of cooling or fan power [5].

5) Design of Fuzzy controller for temperature and Humidity or chicken farm.

+ +

Fig. 3. illustration or P.C- based Fuzzy controller for temperature and humidity or chicken farm.

5.1 Structure of control system

Fig. 4 shows the feedback control system with the fuzzy controller and the process. The fuzzy controller consists of a fuzzy temperature controller and a fuzzy humidity controller. The inputs of the fuzzy temperature controller are the temperature error eT and the change in temperature error. The inputs of the fuzzy humidity controller are the humidity error eø and the change in its error. The outputs of the fuzzy controller are the change in refrigerator power dP R and the change in evaporator power dP F. The strong influence of temperature on humidity is taken into account by using the temperature error as additional input of the fuzzy humidity controller.

Fig.4 Control system with fuzzy control

Inside the fuzzy controller six fuzzy variables (four inputs, two outputs) and

defined:* TE – temperature

error (defined as the difference between the value of set point and the present temperature value)

* HE – change in temperature error, defined as the difference between present value of TE and its last value

* CHE – change humidity error (defined as before)

* PRC – change in refrigerator power

* PFC – change in fun powerThe values of the input variables are converted by fuzzification to the linguistic variables in terms of five respectively three linear membership functions. The resulting. Output values after defuzzification deliver the change in electrical power of the refrigerator dP R respectively of the evaporator fan dP F. For both controllers the MAX – MIN inference method and the Centre – of – Area (CoA) – defuzzifocation method are used. The controller was designed

by using the tool TIL Shell (3) and connected to the simulation environment of SIMORES.

5.2 Fuzzy temperature controller

The fuzzy temperature controller is designed like a PI fuzzy controller. This means that the inputs and outputs are equivalent to traditional PI controller (4,) (5),Fig. 5 shows the membership functions for the two inputs (TE, CTE) and the output (PRC). The values of the input variables and the output variable are scales to the interval (-1,1). Table 1 shows the fuzzy rules of the rule base. The control law for the temperature controller requires, e.g., a big positive change in refrigerator output (PRC) to counteract a negative temperature error (TE) with a big positive change in temperature (CTE).

Fig. 5 Membership function definitions for the fuzzy temperature

controller.

Table 1 Fuzzy rules for temperature controller

PRC TEnb ns zo ps pb

nb

ns CTE zo

Pspb

zo ns ns nb nbps zo ns ns nbps ps zo ns nspb ps ps zo nspb pb ps ps zo

5.3 Fuzzy humidity controller

Fig. 6 shows the membership functions for the fuzzy humidity controller. Besides the humidity error (HE) and its change (CHE) this controller has the temperature error (TE) as additional input.

Fig. 6 shows the three rule bases for the humidity controller depending on the temperature error TE (zero, negative or positive).

Some of the rules for taking into account the influence of temperature on humidity will be explained for the example that relative humidity is slightly above its set point and does not change (‘HE is negative small’ and ‘CHE is zero’)

If, in this case, the temperature is at its set point, the fan power is decreased slightly to reduce relative humidity of the cold store air.

IF TE is zero AND HE is negative small AND CHE is zero THEN PFC is negative small.IF the temperature is below its set point, the power of the fan is not changed, as the increasing temperature will automatically lead to a lower value of relative humidity:IF TE is positive AND HE is negative small AND

CHE is zero THEN PFC is negative big. The physical background of these rules is only shortly discussed here:Due to the continuous action of the compressor, the refrigerator works permanently and the refrigeration power is lower than in the case of an intermittent refrigerator action, This lower refrigeration power leads to a surfaceTemperature of the ice on the evaporator closer to the cold store temperature. The partial pressure of water at this surface is often above that in the cold store. Hence, contrary to the situation normally encountered in cold store operations, the fan action leads to an increase of the relative humidity of the cold store air.

Table 2 Fuzzy rules for temperature controller TE = zero

PRC TEnb ns zo ps pb

n CHE zo p

ns zo ps pb pbnb ns zo ps pbnb nb ns

zo ps

TE = positivePRC TE

nb ns zo ps pb

n CHE zo p

zo ps pb pb pbns zo ps pb pbnb ns zo ps pb

TE = negative

PRC TEnb ns zo ps pb

n CHE zo p

ns ns zo ps pbnb nb ns zo psnb nb nb ns zo

5.4 Model of Fuzzy control for Temperature and Humidity of chicken room using LabVIEW.

Fig.7 Structure of Chicken farm model

Fig.8 Chicken farm model

Fig.9 Block diagram of Chicken farm model

6. The experimental Results.

Fig.10 Font panel of temperature control

Fig.11 Font panel of humidity control

The experimental result of the Fuzzy control for Temperature and Humidity of chicken room suing Lab VIEW. Which fasted in two cases as follows. 1. A set Temperature at 28 OC while room temperature at 25 OC then RUN program and Result show in fig. 12

Fig.12 2. A set temperature at 25O C while room temperature at 28O C then run

program and Result show in fig. 13.

Fig.13

6. ConclusionsIn this paper, a

Fuzzy controller for temperature and relative humidity in chicken farm is described in the control design the thermodynamic coupling of temperature on relative humidity is considered by using the temperature error as additional input for the Fuzzy humidity controller and reimplementation in model of chicken farm which base on Lab VIEW Program. The controller does, not need the object’s mathematic model, but applies face partly serializing base on the basic fuzzy control algorithm. The application results proved that control system has good performance in rapid response and robustness to disturbances.

7. References [1] C.C Lee, “Fuzzy logic control systems;

Fuzzy logic controller – part I and II”.IEEE Transaction on Systems, Man, and Cybernetics, 20(2), March/April 1990, pp.404-435.[2] E.H. Mandeni, Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis. IEEE Transaction, on computers, C-26 (12), December 1977, pp. 1181-1191.][3 ] Lucian Mast can, “Fuzzy logic controller design: A case study” IEEE Transaction on Systems; 1994:PP233-238.[4] M.Beeker, D.Oestreieh, H.Hasse, L, Litz “Fuzzy control for Temperature and Humidity in Refrigeration Systems” IEEE, 1994, p.p. 1607-1612[5] Yinjun guo, Don Fang Cao, Guang Zheng, “Application of Intelligent Control Techniques for Temperature and Humidity Control in Industrial Workshops” IEEE 2009, pp, 192-194