Fuzzy Logic Control And Practical Project

هنقوم بشرح اية هو ال Fuzzy Control بالتفصيل فى المقال ده , وبعدين هنشوف مشروع عملى ومستخدم بالفعل فى الحياة العملية.

طيب محتوى المقال:

1-أولا مقدمة عن ال Fuzzy Logic

2- عرض المشكلة اللى احنا عاوزين نعملها تحكم عن طريق ال Fuzzy logic هى عبارة عن جهاز تكييف

3- حل المشكلة عن طريق برنامج MATLAB Simulation

4- حل المشكلة عن طريق برنامج MATLAB SIMULINK

5- حل المشكلة عن طريق برنامج CAD

6- حل المشكلة Manual بالحل اليدوى

7- تطبيق برنامج MATLAB SIMULNK عن طريق ربط ال Arduino بالبرنامج فى هذة الحالة نستعيض عن التحكم فى المدخالات بمقاومة متغيرة و عن المخرجات بلمبات LED

Abstract
i. Reducing energy consumption and to ensure thermal comfort are two important considerations in designing an air conditioning system.
ii. The control strategy proposed is fuzzy logic controller (FLC).
iii. This report describes the development of an algorithm for air conditioning control system based on fuzzy logic (FL) to provide the conditions necessary for comfort living inside a building.
iv. Simulation of the controlling air conditioning system, on which the strategy is adopted, was carried out based on MATLAB.
v. This system consists of two sensors for feedback control: one to monitor temperature and another one to monitor humidity.
vi. The controller i.e. FLC was developed to control the compressor motor speed, fan speed and required power.
vii. At this report, Methods were used to design the controller are Manual solution, MATLAB, Simulink, Proteus for simulation, and Implementation our controller using Arduino Uno.

Introduction
The idea of fuzzy logic was first advanced by Dr. Lotfi Zedan of the University of California at Berkeley in the 1960s. Fuzzy logic is an approach to computing based on "degrees of truth" rather than usual "true or false" (1 or 0). Fuzzy logic seems closer to the way our brains work. The fuzzy logic, unlike conventional logic system, is able to model inaccurate or imprecise models. The fuzzy logic approach offers a simpler, quicker and more reliable solution that is clear advantages over conventional techniques, but Operator's experience required. Fuzzy logic is able to process incomplete data and provide approximate solutions to problems other methods find difficult to solve. Fuzzy logic provides a more efficient and resourceful way to solve control systems, like Temperature controller in air conditioning will be discussed in our report. Fuzzy control system is based on fuzzy logic. The process of designing fuzzy control system can be described by following steps:
i. Identify the principal input, output and process tasks.
ii. Identify linguistic variables used and define fuzzy sets and memberships accordingly.
iii. Use these fuzzy sets and linguistic variables to form rules.
iv. Determine the defuzzification method. Test the system and modify if necessary.

بكل بساطة عندنا نظام تكييف عاوزين نعملة تحكم بال Fuzzy Logic

Problem statement
For Air conditioning system, design MISO Fuzzy Logic Controller (FLC) using the following Information:

Seven Triangular fuzzy membership functions for inputs (Assume 50% overlapping and the top of trapezoid is half of its base), with Seven Trapezoidal fuzzy membership functions for the output.

Range of Temperature (as 1st Input) is: 5 ºC to 65 ºC
Range of Humidity (as 2nd Input) is: 16 % to 100 %
Range of Power (Rate of Energy, as Output) is: 0.0 kW to 3.0 kW
Then calculate the required power using weighted average defuzzification method, for the following cases:
i) The Temperature is 35 ºC and the Humidity is 100 %.
ii) The Temperature is 45 ºC and the Humidity is 75 %.
iii) The Temperature is 50 ºC and the Humidity is 50 %.

lock diagram of proposed model

Air conditioning system is first developed using mamdani fuzzy model. It consists of two inputs from temperature and humidity sensors providing the temperature and humidity of the room. The system has one output that controls the required power. The temperature and humidity are taken to be in ranges of 5 ºC to 65 ºC and 16% to 100% respectively.

Each of the inputs has seven triangular membership functions. The output i.e. power is taken in percentage in range from 0kw to 3kw and have seven trapezoidal membership functions. The rules included for the air conditioning system are described. The function of fuzzy logic controller is very useful since exact mathematical model of it is not required.As shown in the above block diagram, the fuzzy logic control system can be divided into four main functional blocks namely Knowledge base, Fuzzification, Inference mechanism and Defuzzification, Rule base.

Fuzzy Rules table

Membership functions

Triangular fuzzy membership function (Inputs)

Trapezoidal fuzzy membership function (Output)

MATLAB Simulation

The System (MISO)

Membership functions

Fuzzy based rules (49 rules)

Note:
Weighted average defuzzification method is not included in MATLAB Library, so that centroid defuzzification method will be used.

The Temperature is 35 ºC and the Humidity is 100 %.

Power=2.5KW

The Temperature is 45 ºC and the Humidity is 75 %

Power=2.11KW

The Temperature is 50 ºC and the Humidity is 50 %.

Power=1.75KW

MATLAB SimulinkBlock Diagram

The Temperature is 35 ºC and the Humidity is 100 %

Power=2.5KW

The Temperature is 45 ºC and the Humidity is 75 %.

Power=2.11KW

The Temperature is 50 ºC and the Humidity is 50 %.

Power=1.75KW

Manual solution by using CAD

T = 35 ---------------- μF (T) = 1
H = 100 % ---------------- μVVH (H) = 1

T = 45 ---------------- μH (T) = 1
H = 75% ---------------- μVH (H) = 0.21429 & μH (H) = 0.78571

T = 50 ---------- μH (T) = 0.5 & μVH (T) = 0.5
H = 50 % ---------------- μM (H) = 0.42857 & μL (H) = 0.57143

Implementation on Arduino:

Now, it is desired to implement this fuzzy logic system on Arduino Uno.

Hardware configuration

The Arduino Uno accepts both temperature and humidity as analog signals coming out of two potentiometers and the desired output power is sent through the communication port to the PC to be displayed on the serial monitor.

Arduino Code

Code parts

Practical System

Simulation by Proteus

Results from Implementation by Arduino

The Temperature is 35 ºC and the Humidity is 100 %.

The Temperature is 45 ºC and the Humidity is 75 %.

The Temperature is 50 ºC and the Humidity is 50%

Results conclusion

As shown in the table.1, the results are the same from Manual solution, MATLAB, and Implementation by Arduino. There is one difference between the manual solution and MATLAB due to the different methods of solution.

Conclusion

Hence, the advantages of this Design are:
i. The algorithmic design approach makes the system efficient and absolutely under control.
ii. The analysis clearly maps out advantage of fuzzy logic in dealing with problems that are difficult to study analytically yet are easy to solve intuitively in terms of linguistic variables.
iii. In case of the Air Conditioning system, fuzzy logic helped solve a complex problem without getting involved in intricate relationships between physical variables.
iv. Intuitive knowledge about input and output parameters was enough to design an optimally performing system.
v. The utility of the proposed system in processing plants is being carried out and in future, it will help to design the advanced control system for the various industrial applications in environment monitoring and management systems.

Manual Solution scanned papers