1.1 Introduction
An electric fan is a device used to produce flow of air for the purpose of providing comfortable ventilation. They are designed to create breeze and circulate the air in a region so as to provide the necessary cooling, especially during hot weather condition (Bai et al., 2008). While an electric fan circulates the air around its environment, an air-conditioning system changes the temperature of the air in its environment (Sathishkumar et al., 2015). Older DC fans used mechanical brushes, which can cause increased electromagnetic interference (EMI) along with dust particles due to mechanical wear throughout the system. Over time, the fan would wear and eventually fail. Brushless fans have replaced these mechanical brushes with electronic sensors and switches that now perform the necessary commutation and increase the lifetime and the reliability of these fans (Vats et al., 2015).
As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Limitations of the Study and Definition of technical terms.
1.2 Background of Study
Temperature Control cuts through a variety of Industrial and domestic processes such as temperature controlled heat exchangers, baths, green houses, incubators. A DC motor may be used as Fan in accomplishing temperature control. An Electric Fan is basically a device which comprises of three blades at 120 degrees mounted on the DC motor spindle.An electric fan circulates the air around its environment while an air-conditioning system changes the temperature of the air in it environment. Many system used in our daily life which require controlling are non-linear in nature; hence, a detailed system dynamics is difficult to represent mathematically. Temperature control is stochastic and a slowly changing process which requires heuristic based control. Many authors have proposed Fuzzy Logic Inference System in dealing with temperature control. In households during summer which is analogous to dry season in West Africa, Air conditioners are responsible for 60-70% of summer electricity bill. A window air-conditioning unit uses 500 to 1440 watts,while a 2.5-ton central system uses 3500 watts .However, an electric fan uses only 90 watts, depending upon the speed and size (Ali Newaz Bahar et al, 2012).
The fan creates its cooling effect based on the speed at which its blades rotate; hence, the speed controller is a very important part of a fan. Usually, the speed of the fan blades is controlled manually by turning the knob on the regulator. This non-innovative feature may not be very suitable when certain level of air circulation is to be maintained in various types of buildings and enclosed spaces, and when ensuring efficient use of energy (Saad et al., 2015).
An electric fan is a device that on the long run will on the one hand help in keeping us cool in summer and on the other hand help in saving money as well as protecting the environment by limiting the release of Carbon-monoxide (Lakshya Kumar et al, 2015).
In this project, a Fuzzy logic controller design is proposed for deployment of the process variable due to the fact that temperature is a slowly changing variable. Hence, for precision control of a stochastic process variable Fuzzy logic based control suffices. While all the regularly used systems are defined by mathematical equations. The temperature of the metal plate decides the amount of current that can pass through it. Temperature of the metal plate is measured with the help of temperature sensors (Lakshya Kumar et al, 2015). The amount of energy supplied to the fan is to be controlled by SVM or PWM technique.
The human brain has an unpredictable way of reasoning and thus has a high adaptive approach with recourse to control. It does not reason as computers do. Computers reason in a clear statement that uses true or false (0 or 1) - an element is either a number of a given set or it is not. There are many complex systems which do not fit into the precise categories of conventional set theory. This is because of the fact that there is no way to define a precise threshold to represent their complex boundary, and as such their control system is complex. Fuzzy logic was developed owing to this imprecise nature of solving control problems by computer. In a fuzzy logic-based system, a variable can take any truth value from a close set (0, 1) of real numbers thus generalizing Boolean truth values (1). But the fuzzy facts are true only to some degrees between 0 and 1, and they are false to some degrees (Isizoh A. N., et al, 2012).
The Fuzzy inference is based on Human heuristic reasoning pattern. But computers cannot do so because its logic is based on approximate reasoning in a more familiar Boolean forms of logic used in conventional set theory. Fuzzy logic allows the use of labels like “slightly”, “moderately”, medium, and “very” so that statements may be made with varying degree of precision. This flexibility is useful in coping with the imprecision of real-world situations such as designing precision environmental control systems. In a broad sense, fuzzy logic refers to fuzzy sets - a set with non-sharp boundaries. Fuzzy logic is widely used in machine controls, as it allows for a generalization of conventional logic and provides for terms between “true” and “false”, like “almost true” or partially false”. This makes the logic not to be directly processed on computers but must be emulated by special codes.
A fuzzy logic based design control system offers flexibility in system design and implementation, since its implementation uses “if then” logic instead of sophisticated differential equations. It’s technology that provides room for graphical user interface, which makes it understandable by people who do not have process control backgrounds. Another key significance of a fuzzy logic-based control design is the ability to automatically and smoothly adjust the priorities of a number of controlled variables. Finally, it helps to achieve a process that is stable for a long period of time without a need for intervention. However, because of the rule-based operation of fuzzy systems, any reasonable number of inputs can be processed and numerous outputs generated; although defining the rule-base quickly becomes complex if too many inputs and outputs are chosen for a single implementation, since rules defining their inter-relations must also be defined. There are countless applications of fuzzy logic.
In fact many researchers still claim that fuzzy logic is an encompassing theory over all types of logic .Fuzzy logic can control non-linear systems that would be difficult or impossible to model mathematically. This opens door for control system that would normally be deemed unfeasible for automation. There are many approaches to implement fuzzy logic systems; they can be software only, hardware only or the combination of software and hardware. In recent years, fuzzy logic has been implemented using several technologies to solve real world problems such as image processing, robotics/motion control, pattern recognition, fuzzy database and industrial engineering applications. Fuzzy logic is also spreading applications in the field of telecommunications, particularly in broad band integrated networks, based on ATM Technology.
Therefore, in Nigeria where the research was carried out, the activities that was conducted is to design and construct a Speed Control of a DC Fan using a Fuzzy Controller.
1.3 Statement of Problems
Investigation revealed that the control modes used varies like ON/OFF control, the linear predictive control (LPC) and PID control system. The ON/OFF system regulator is not accurate enough. This control mode is the simplest form of control Low accuracy and quality leads the system to become unstable due to mismatch error generated by inaccurate time delay parameter used in the model. Transient and overshoot are present when the controller is used to control the cooling system because it exceeded the required control for certain period.
The linear predictive control, is capable of controlling the indoor temperature within the required limits most of the time but not all the time, the linear predictive control in the relative short prediction periods are used which do not cause any problem, but it is obvious that with larger prediction periods more computing time is necessary and the linear programming problems require more memory.
The proportional integral derivative (PID) controller structure is mostly widely used thanks to its structural simplicity and applicability in solving practical control problems but is not however almighty in many cases, it provide disturbance ,this disturbance is unknown, making it difficult to attenuate.
1.4 Aim and Objectives of Study
The aim of the study is to design and construct a Speed Control of a DC Fan using a Fuzzy Controller. In achieving this aim, the following specific objectives were laid out as follows:
- To develop a cooling ventilation system integrated with speed controller based on temperature sensor;
- To design a controller using MAT-LAB Fuzzy Logic tool box;
- To evaluate the performance of the controller for both the experimental and Simulated model; and
- To improve the cooling system in room using fan rather than using air conditioner associated with sustainability and environmental friendly using fuzzy control logic.
1.5 Significance of Study
The needs for the design of an automatic room temperature control fan are as follows:
- It helps in controlling fluctuating room temperature by adjusting Fan to achieve the desired set point.
- It eliminates the need for human intervention as opposed to constant watching on the device by set controlling the temperature of the system
- It overcomes the disadvantages of thermostatic analogue system in terms of accuracy.
- It is used where it is important to maintain precise temperature.
- It overcome the limitation of conventional control that how to be operated by physically going near them and switch on the button.
- The designed controller may be deployed for other slowly changing controlled process variable.
This study will be of immense benefit to other researchers who intend to know more on this study and can also be used by non-researchers to build more on their research work. This study contributes to knowledge and could serve as a guide for other study.
1.8 Scope of Study
The scope of the research is focused on the Design and Construction of a Speed Control of a DC Fan using a Fuzzy Controller.
1.9 Limitations of the Study
During the course of this study, many things militated against its completion, some of which are:
- Time Constraint: The time frame given to accomplish this project was very short due to school academic calendar and it was carried out under pressure which made the researcher not to implement some necessary features.
- Research material: availability of research material is a major setback to the scope of the study.
- Frequent power failure: This made the researcher append more money on fuel to ensure sustainable power.
- Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet).
1.10 Definition of Terms
Temperature Controller: It is a closed loop control system which senses the temperature of the environment and compares it with a user-fed threshold temperature value and changes the speed of the fan so as to increase or reduce accordingly between the minimum and maximum speed values (Saad et al., 2014; Vats et al., 2015; Fu et al., 2010).
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