Fuzzy Logic Control System

Introduction

Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is mainly used with Neural Networks to mimic how a person would make decisions faster. Usually, when a computer processes the information, its result is true and false, which is similar to a person's no and yes. Fuzzy logic was developed in 1965 by Lotfi Zadeh by realizing that people always do not have yes-or-no answers; sometimes, they also have other answers. Large-scale machine control uses fuzzy logic. The term "fuzzy" describes a system that handles concepts that cannot be represented as "true" or "false." Numerous control applications effectively use fuzzy logic. Almost all consumer goods have fuzzy controls.

Example of Fuzzy Logic

We can understand Fuzzy logic through real-life examples. Consider the glass of hot water, and we can find the answer to the question "Is the water hot?" by using Boolean logic and Fuzzy logic.

According to the Boolean logic, its answers are:

1. Water is hot means 'Yes', which is '1'.
2. Water is not hot means 'No', which is '0'.

According to Fuzzy Logic, its answers are:

1. Water is very hot means 0.9.
2. Water is hot means 0.7.
3. Water is cool means 0.1.
4. Water is warm means 0.3.

Control System Design

Fig 1: Block Diagram of closed-loop Control System

There are four steps to create a controller for a sophisticated physical system:

1. Dividing the big system into many smaller systems.
2. Changing the plant dynamics slowly and linearizing the nonlinear plant dynamics.
3. Arranging a group of control variables, state variables, or output features for the particular system.
4. Creating easy P, PD, and PID controllers for the subsystems.

FLC System Architecture and Operations

An FLC system's primary elements are a fuzzy rule base, a fuzzifier, a fuzzy knowledge base, an inference engine, and a defuzzifier. It also has normalization parameters. The system is called a fuzzy logic decision system if the defuzzifier's output is not a control action for the plant.

Fig 2: Architecture of an FLC System

1. Fuzzifier: The fuzzifier transforms crisp values into fuzzy values.
2. Fuzzy Knowledge Base: It stores the details of all the fuzzy input-output relationships. Additionally, it features a membership function that specifies the inputs to the fuzzy rule base and the outputs to the plant under control.
3. Fuzzy Rule Base: It stores information about the domain expertise process.
4. Inference Engine: It serves as the FLC's kernel. Additionally, it can simulate human decisions by applying accurate reasoning to achieve the ideal control method.
5. Defuzzifier: Defuzzifiers' job is to transform fuzzy values into crisp values.

The steps to designing FLC are as follows:

1. Variable identification: 

In this step, the plant's input, output, and state variables are identified.

2. Fuzzy subset configuration:

In this step, the universe of data is split up into several fuzzy subsets, and a linguistic label is given to every subset.

3. Obtaining membership function: 

In this step, the membership function obtains for every fuzzy subset.

4. Fuzzy rule base configuration:

This step involves creating the fuzzy rule base, which establishes a connection between fuzzy input and output.

5. Fuzzification:

In this step, the fuzzification process is started.

6. Combining fuzzy outputs:

In this step, by using fuzzy approximate reasoning, it finds the fuzzy output and combines it.

7. Defuzzification:

This is the last step. In this step, the defuzzification process starts to provide a crisp result.

Advantages of Fuzzy Logic Control System

The advantages of fuzzy logic control are discussed below:

1. This system is adaptable and can support changes.
2. Compared to traditional control systems, FLC is more trustworthy.
3. The fuzzy logic systems are simple to build.
4. FLCs can be modified.
5. FLC is used to develop to mimic human deductive thoughts.
6. FLCs are more powerful than PID controls.
7. These systems offer solutions for challenging problems.

Disadvantages of Fuzzy Logic Control System

1. Lots of data are needed for FLC to be used.

2. FLC is useless for programs that are significantly smaller or larger than historical information.

3. High levels of human skill are required.

4. Rules must be updated frequently.

Applications of Fuzzy Logic Control System

FLC systems are used in various industrial and commercial systems and products. Applications of FLC systems are:

1. Traffic Control
2. Steam Engine
3. Adaptive Control
4. Aircraft Flight Control
5. Liquid-Level Control
6. Process Control
7. Helicopter Model
8. Automobile Speed Controller
9. Braking System Controller
10. Missile Control
11. Robotic Control
12. Air Conditioner Control
13. Elevator (Automatic Lift) control
14. Automatic Running Control
15. Cooling Plant Control
16. Water Treatment
17. Knowledge-Based System
18. Boiler Control
19. Nuclear Reactor Control
20. Biological Processes
21. Fault Detection Control Unit
22. Power Systems Control;
23. Fuzzy Hardware implementation and Fuzzy Computers.