Author Topic: Autonomous Room Air Cooler Using Fuzzy Logic Control System  (Read 2536 times)

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Autonomous Room Air Cooler Using Fuzzy Logic Control System
« on: August 20, 2011, 05:34:07 am »
Author : M. Abbas, M. Saleem Khan, Fareeha Zafar
International Journal of Scientific & Engineering Research, Volume 2, Issue 5, May-2011
ISSN 2229-5518
Download Full Paper : PDF

Abstractó This research paper describes the design and implementation of an autonomous room air cooler using fuzzy rule based control system. The rule base receives two crisp input values from temperature and humidity sensors, divides the universe of discourse into regions with each region containing two fuzzy variables, fires the rules, and gives the output singleton values corresponding to each output variable. Three defuzzifiers are used to control the actuators; cooler fan, water pump and room exhaust fan. The results obtained from the simulation were found correct according to the design model. This research work will increase the capability of fuzzy logic control systems in process automation with potential benefits. MATLAB-simulation is used to achieve the designed goal.
Index Termsó Fuzzy  Logic Control, Inference Engine, MATLAB simulation and Rule Selection.

1   INTRODUCTION                                                                     
MODERN processing systems are heavily dependent on automatic control systems. The control automation has become essential for machines and processes to run successfully for the achievement of consistent operation, better quality, reduced operating costs, and greater safety.
The control system design, development and im-plementation need the specification of plants, machines or processes to be controlled. A control system consists of controller and plant, and requires an actuator to interface the plant and controller. The behaviour and performance of a control system depend on the interaction of all the elements. The dynamical control systems design, modeling and simulation in local and distributed environment need to express the behaviour of quantitative control system of multi-input and multi-output variables control environment to establish the relation between actions and consequences of the control strategies [1].Computational Intelligence (CI) is a field of intelli-gent information processing related with different branches of computer sciences and engineering. The fuzzy systems are one paradigm of CI. The contemporary technologies in the area of control and autonomous processing are benefited using fuzzy sets [2].The user based processing capability is an impor-tant aspect of fuzzy systems taken into account in any design consideration of human centric computing sys-tems. The human centricity plays a vital role in the areas of intelligent data analysis and system modeling [3]. The elements of fuzzy sets belong to varying degrees of membership or belongingness. Fuzzy sets offer an important and unique feature of information granules. A membership function quantifies different degrees of membership. The higher the degree of membership A (x), the stronger is the level of belongingness of this element to A. Fuzzy sets provide an ultimate mechanism of communication between humans and computing environment [4].
The fuzzy logic and fuzzy set theory deal with non-probabilistic uncertainties issues. The fuzzy control system is based on the theory of fuzzy sets and fuzzy logic [5]. Previously a large number of fuzzy inference systems and defuzzification techniques were reported. These systems/techniques with less computational overhead are useful to obtain crisp output. The crisp output values are based on linguistic rules applied in inference engine and defuzzification techniques [6]-[7].
The efficient industrial control with new techniques of fuzzy algorithm based on active rule selection mechanism to achieve less sampling time ranging from milliseconds in pressure control, and higher sampling time in case of temperature control of larger installations of industrial furnaces has been proposed [8].
The development of an air condition control system based on fuzzy logic with two inputs and one output using temperature and humidity sensors for feedback control, and three control elements for heating, cooling, and humidity, and formulated fuzzy rules for temperature and humidity has been achieved. To control the room temperature, the controller reads the room temperature after every sampling period [9].
   This proposed design work of Autonomous Room Cooling System is the application of fuzzy logic control system consisting of two input variables: Temperature and Humidity, and three output variables: Cooler fan speed, Water pump speed and Exhaust fan speed, used in a processing plant of room cooler to maintain the required cooling environment. The basic structure of the proposed model is described in Section 2. Section 3 gives the simplified design algorithm of fuzzy logic for room air cooler system. Section 4 describes the simulation results of this system. Conclusion and future work is given in Section 5.

The basic structure of the proposed model of auto-nomous water room cooler consists of room air cooler with fuzzy logic control system. The room cooler mounted in a room has cooler fan, a water pump to spread water on its boundary walls of grass roots or wooden shreds. A room exhaust fan, humidity and tem-perature sensors used to monitor the environment of room are mounted in the room. The sensors with amplification and voltage adjustment unit are connected with the two fuzzifiers of the fuzzy logic control system. Three outputs of defuzzifiers: cooler fan speed control, water pump speed control and room exhaust fan speed control are connected through actuators.

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