Fuzzy Controller

Fuzzy Control System

Fuzzy Controller

Fuzzy control system or Fuzzy controller is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).

Chapter A Introduction

About the dimming controller, fuzzy logic and components of the controller. Give us an overview of the content of the next chapter.

Chapter B Fuzzy Sets

In this chapter we will be reminded of the basic concept. In addition, the fuzzy set is also a fairly new concept for new people to approach.

Chapter C Fuzzy Sets – Math

After learning the concept of “Fuzzy set” in the previous chapter, the next chapter will talk about the mathematics used with fuzzy sets. This is quite important chapter as these calculations will relate to the knowledge of the next chapter.

Chapter D Language Variable – Value

The characteristic of natural language is that it contains vague, uncertain information, but the blurry set also contains vaguely uncertain information so it is clear that we immediately see that it is possible to use blurred sets to express natural language. In this section, we will examine the language and its value.

Chapter E Fuzzy Rule

The Fuzzy rule, also known as the Fuzzy rule system, can be considered as a mathematical model that represents knowledge, human experience in solving problems in the form of language statements. The Fuzzy rule is also the deciding component for the design of the controller.

Chapter F Fuzzy Inference

Fuzzy inference or understandable inference is how we identify or construct a blur from one or more values from the blur. In the control, there are many common mechanisms of expression such as MAX – MIN, MAX – PROD, SUM – MIN, and SUM – PROD. In this chapter, we will find out all of this content.

Chapter G De-Fuzzy

De-fuzzy is the process of converting the blur set (language value) to a clear value (physical value at the output). There are two methods of openness mainly the method of maximum dependence and the focus point method.

Chapter H Fuzzy System

The Fuzzy system is a fuzzy inference system of three basic components: Fuzzification, Fuzzy Rule and Method of Inference, De-fuzzy. Two types of rules were used, the Mamdani rule and the Sugeno rule.

Chapter I Fuzzy Control System Design

This chapter will cover how to design a fuzzy control system. Introduce the steps taken and the considerations needed when implementing the design.

Chapter K Fuzzy Logic Designer – Matlab

Fuzzy Logic Toolbox™ provides MATLAB^® functions, apps, and a Simulink^® block for analyzing, designing, and simulating systems based on fuzzy logic. The product guides you through the steps of designing fuzzy inference systems. Functions are provided for many common methods, including fuzzy clustering and adaptive neurofuzzy learning.

Chapter L SISO for DC Motor

To better understand how to build a fuzzy controller. In this article, we will explore the process of designing the Fuzzy controller for the DC motor model on MATLAB – Simulink as an example for the theoretical application from previous chapters.

Chapter M MISO for DC Motor

Next, we will explore the process of designing the Fuzzy – MISO controller for the DC motor model on MATLAB – Simulink as an example for the theoretical application from previous chapters.