Rockwell Automation RSLogix 5000 User Manual

RSLogix 5000 Fuzzy Designer

User Manual

Important User Information

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Table of Contents

Preface

Get Started with FuzzyDesigner

FuzzyDesigner Component Library

About This Publication. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Who Should Use This Publication. . . . . . . . . . . . . . . . . . . . . 7

Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Chapter 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Understanding FuzzyDesigner . . . . . . . . . . . . . . . . . . . . . . . 9

Fuzzy Logic and Fuzzy Control Essentials . . . . . . . . . . . . 12

Potential Use of Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . 16

Specifications and Features . . . . . . . . . . . . . . . . . . . . . . . 18

Integrated Design Environment (IDE) screen captures . . . . . . 22

Chapter 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Component Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Library of Components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Supported Membership Functions. . . . . . . . . . . . . . . . . . . . . 30

Input Port . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

User Defined Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Butterworth Low Pass Filter . . . . . . . . . . . . . . . . . . . . . . 33

Connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Input Linguistic Variable. . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Output Linguistic Variable . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Defuzzification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Output Takagi-Sugeno Variable . . . . . . . . . . . . . . . . . . . . . . 42

Connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Intermediate Linguistic Variable . . . . . . . . . . . . . . . . . . . . . . 46

Connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Rule Block. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Supported Format of Rules . . . . . . . . . . . . . . . . . . . . . . . 48

Connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

PID Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Output Port . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

FuzzyDesigner Graphical User Interface

FuzzyDesigner Projects

Chapter 3

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Setting Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Tool Bar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

FuzzyDesigner Control Basics. . . . . . . . . . . . . . . . . . . . . . . 59

Main Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Chapter 4

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Working with Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Creating a Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Opening an Existing Project . . . . . . . . . . . . . . . . . . . . . 69

Changing the Active Project . . . . . . . . . . . . . . . . . . . . . 70

Project Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Saving a Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Closing a Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Designing a Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Printing a Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Designing a Fuzzy System . . . . . . . . . . . . . . . . . . . . . . . . . 72

Fuzzy System Project Window. . . . . . . . . . . . . . . . . . . . 73

Working with Blocks . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Working with Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Fuzzy System Components . . . . . . . . . . . . . . . . . . . . . . . . . 75

Input Port. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Input Linguistic Variable . . . . . . . . . . . . . . . . . . . . . . . . 79

Output Port . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

Output Linguistic Variable. . . . . . . . . . . . . . . . . . . . . . . 84

Output Takagi-Sugeno Variable. . . . . . . . . . . . . . . . . . . 88

Intermediate Linguistic Variable. . . . . . . . . . . . . . . . . . . 92

Rule Block . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

PID Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Term Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

Term Properties Dialog . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Rule Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Operations with Rules. . . . . . . . . . . . . . . . . . . . . . . . . . 109

Rule Editor Tool Bar . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

Port Order Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Watch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

History Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

History Graph Control – Context Menu . . . . . . . . . . . . . 119

History Graph Control – Tool Bar . . . . . . . . . . . . . . . . . 121

History Graph Control – Mouse Dragging . . . . . . . . . . . 122

2D Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

2D Graph Control – Context Menu . . . . . . . . . . . . . . . . 124

2D Graph Control – Tool Bar . . . . . . . . . . . . . . . . . . . . 125

3D Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

3D Graph Control – Context Menu . . . . . . . . . . . . . . . . 128

Fuzzy System Simulation

RSLogix 5000 Add-On Instruction

XML Format of a Fuzzy Project

3D Graph Control – Tool Bar . . . . . . . . . . . . . . . . . . . . 130

3D Graph Control – Mouse Dragging . . . . . . . . . . . . . . 130

Chapter 5

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Chapter 6

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Generating a Fuzzy Add-On Instruction . . . . . . . . . . . . . . . 134

Add-On Instruction Parameters . . . . . . . . . . . . . . . . . . . 135

Importing Add-On Instructions to RSLogix 5000 Projects . . . 136

Monitoring and Updating a Project Online . . . . . . . . . . . . . 138

Configuring RSLinx OPC Server Topic. . . . . . . . . . . . . . . . . 144

Modifying Fuzzy System Parameters Online . . . . . . . . . . . . 145

Importing an Add-On Instruction to FuzzyDesigner. . . . . . . 146

Chapter 7

Prolog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Document Element . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Glossary

Chapter 8

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Preface

About This Publication

Who Should Use This Publication

Use this manual to understand how to best use the features in RSLogix 5000 software version 16, FuzzyDesigner.

This manual describes the necessary tasks to:

• build fuzzy systems as block diagrams from components of the

FuzzyDesigner Component Library and use FuzzyDesigner functions to complete the project.

• use, execute, and monitor the designed fuzzy system on

Rockwell Automation Logix5000 controllers.

• understand the fuzzy project, and how you can export it to the

XML format.

This manual is for application and control engineers, to enhance functionality of control and decision making systems.

Conventions

Text that is Identifies

Bold A value that you must enter exactly as shown

Italic A variable that you replace with your own text or value

Courier Example programming code, shown in a monospace font so

you can identify each character and space

Enclosed in brackets A keyboard key

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8 Preface

Notes:

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Introduction

Get Started with FuzzyDesigner

Topic Page

Understanding FuzzyDesigner 9

Fuzzy Logic and Fuzzy Control Essentials 12

Specifications and Features 18

Chapter

Understanding FuzzyDesigner

FuzzyDesigner is a software package for designing a fuzzy system to be implemented as a Hierarchical Fuzzy System (HFS). Fuzzy systems can be used in the following applications:

• Industrial automation and control systems

• Process diagnostics and intelligent monitoring systems

• Artificial intelligence

• Decision-making and forecasting

Hierarchical Fuzzy System

FuzzyDesigner enables application and control engineers to enhance the functionality of control and decision making systems in various branches of industry.

FuzzyDesigner includes a library of components you can use to design a fuzzy system that includes nonlinear input-output mapping. You can use a hierarchical structure to decompose a complex fuzzy system into smaller and simpler parts. This reduces the internal complexity of a fuzzy model and results in fewer fuzzy rules and provides easier insight into the system operation.

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10 Get Started with FuzzyDesigner

FuzzyDesigner is designed to work with Rockwell Automation's Logix5000 family of controllers. A fuzzy system designed in FuzzyDesigner can be exported to an L5X Add-On instruction (AOI) format. You can then import the fuzzy AOI into any of your projects as needed. Fuzzy AOIs can be used by any of the programming languages (Function Block Diagram, Ladder Logic, or Structured Text). With FuzzyDesigner, you can also monitor and update the selected fuzzy AOI online, directly in the running controller. This is made available through the RSLinx OPC Server.

The Intended Use of FuzzyDesigner figure shows the underlying idea and intended use of the FuzzyDesigner software package used in designing Fuzzy Add-On Instructions for Logix applications. You can build smart components, based on the expert knowledge encoded in fuzzy If-Then rules. You can use these components in the many applications listed above.

Intended Use of FuzzyDesigner

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Get Started with FuzzyDesigner 11

A Fuzzy Add-On instruction does not typically compete against standard controls found in Proportional-Integral-Derivative Controllers (PID). Fuzzy logic is a complementary tool, and fills functional gaps not addressed in standard controllers such as PIDs or Model Predictive Controllers.

A development cycle of fuzzy logic solutions for Logix applications consists of multiple steps.

1. Design the fuzzy system in FuzzyDesigner.

2. Generate the fuzzy Add-On Instruction.

3. Integrate (import and instantiate) the fuzzy AOI to your RSLogix

5000 project.

4. Monitor and tune the fuzzy AOI running in Logix online by

using FuzzyDesigner.

Using FuzzyDesigner with RSLogix 5000 Software

If you are unfamiliar with fuzzy logic, the next section introduces fuzzy logic terms and principles you might use in your fuzzy system.

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12 Get Started with FuzzyDesigner

Fuzzy Logic and Fuzzy Control Essentials

This section introduces basic concepts used in a Fuzzy Add-On Instruction. The designer should know how to deal with an instruction’s inputs, outputs, and fuzzy If-Then rules that will be used to define input-output mapping.

There are quite a number of systems or processes that are highly nonlinear, not well understood from the formal description point of view, or for which a mathematical model is not readily available. For these systems or processes, there is often an expert that is capable of supervising or controlling the process in a satisfactory manner. The figure Nonlinear System Example illustrates the difference between linear and nonlinear systems.

Nonlinear System Example

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The decision making the expert uses in control system supervision can be expressed as a set of Fuzzy Logic If-Then rules.

Get Started with FuzzyDesigner 13

An expert may be an operator, a maintenance person, or a control engineer, who knows what adjustments are needed during process instability. These adjustments may include defining setpoints for process variables, defining control action in feedforward or feedback contro,l or setting gains of conventional controllers, and may be as simple as turning a valve or knob.

Rockwell Automation is introducing a tool for building smart instructions that encode If-Then rules and use fuzzy logic internally to describe vague and incomplete knowledge in a natural way. Fuzzy Logic may serve in situations where:

• the process has not been automated and is running in Manual

mode.

• a well-tuned PID controller does not provide the desired

response, however, the expert knowledge is available to define the rules for a fuzzy algorithm.

Let’s look at an example where we will discuss building a Heat, Ventilation and Air Conditioning (HVAC) system that manipulates the compressor speed based on room temperature and humidity. In HVAC systems, room comfort is often associated with vague (fuzzy) values of temperature and humidity that are more suitable for describing the problem than numerical (crisp) values.

Fuzzy rules used in this example might be as follows.

If Then

Temperature is high and humidity is high

Temperature is medium and humidity is very high

Speed is medium

Speed is high

Consider these factors when developing fuzzy rules:

• How do I specify High and other fuzzy values in fuzzy rules?

• How do the rules process numerical inputs provided by tags

associated with sensors?

• How do the rules derive outputs from inputs?

• If the output generated is vague (fuzzy), how do I get the

numerical (crisp) value at the output when needed?

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14 Get Started with FuzzyDesigner

Crisp and Fuzzy

For temperature readings, you can classify a reading into three sets, Low, Medium and High. Each set contains values in a given interval, and the intervals do not overlap. This means that a single reading or value is uniquely classified into one set.

degree of membership

(level of classification)

Classification Result

1.00

Medium

1.00

Medium

0.0

High

0.0

High

0.0

Low

0.0

Low

temperaturetemperaturetemperature

Low Medium

range

range20range

Crisp ValueCrisp Value

High

150

TIP

Degree of membership (DOM) is a value describing how well

the particular value of the variable (in this case, temperature) fits the meaning of the label of the set, Medium. If the DOM is 1, the current temperature is understood as 100% Medium.

However, vague classifications are more realistic as there is usually no sharp border between Low, Medium, or High temperatures. In this situation, however, a single numerical value might fall into multiple categories. For example, it might be partially Medium, and partially High as shown in the following figure. A specification of how much the particular value of temperature fits into the meaning of the label of the category (fuzzy set) is described by the membership function, which becomes a design parameter of the fuzzy controller.

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Get Started with FuzzyDesigner 15

Similar fuzzy terms are designed for the output variables, that is, Low, Medium, and High for compressor speed in our example.

Fuzzy rules

The way in which the classified inputs are treated when passing through rules is shown in the following figure for our compressor control example.

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16 Get Started with FuzzyDesigner

First, the numerical values of Temperature and Humidity get their meaning. In our case, the current setting of the Temperature is such that it is both 85% Medium and 40% High. Humidity is both 80% High and 50% Very High. The first rule is thus 80% true for the current inputs while the second rule is 40% true when using minimum for the and operation. The first rule states that, if 100% satisfied, the compressor should run at Medium speed. Currently, the first rule is only 80% fulfilled, so one method of how to consider that the rule is only 80% fulfilled is to truncate the Medium fuzzy set for the output at the level 0.8.

A similar situation happens with the second rule where High compressor speed is only 40% fulfilled. As both rules are used at the same time, their conclusions must be combined to get a fuzzy value for the output, which is compressor speed. The partially-fulfilled Medium and High fuzzy sets are unified, and a single fuzzy value is assigned to Compressor Speed. As conventional control systems cannot deal with fuzzy values, the fuzzy instruction includes conversion from a fuzzy to a crisp value. For this case, the center of gravity for the green area is computed and used to represent the original fuzzy value.

To summarize, the designer has to:

• define input and output variables.

• cover the interval of the respective variable by fuzzy sets (that is,

membership functions).

• write if-then rules using labels of the fuzzy sets defined

previously.

Potential Use of Fuzzy Logic

FuzzyDesigner enables you to enhance the functionality of existing or new control and decision making systems in various branches of industry.

The fuzzy system designed and generated by FuzzyDesigner can be used in control systems, for example, as a direct nonlinear fuzzy-rule based controller, PID-feedback control system supervisor, or a process model in a Model Predictive Control scheme. Input and output filters are used for signal preprocessing such as filtering, deriving trends, and many other functions that might add dynamics to the static I/O map generated from fuzzy rules. Input filters can also be designed in FuzzyDesigner. Output filtering is an option and contains, for instance, a discrete integrator fed by the output of the Fuzzy Add-On Instruction.

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Get Started with FuzzyDesigner 17

Nonlinear, Fuzzy Rule Based Supervisor of a PID Controller

Plant States

feedf orward

PLANT

FUZZY

SUPERVISOR

PID

gains

PID

CONTROLLER

The great advantage of fuzzy supervision is that it can be applied to existing control and there is little danger of making errors in design. Most frequently used is a supervised PID controller where PID gains, feedforward action, or setpoints are being modified dynamically by rules depending on the process status and external conditions defined through setpoints.

Smart Switching Between Conventional Controllers, Takagi-Sugeno Controller

Plant State

PLANT

Setpoints

CONTROLLER

Process Variables

FUZZY SUPERVISOR

Schedule weights ∈ [0,1]

Another popular control structure with fuzzy logic is smart switching between local controllers. A local controller is an analytical controller designed to work around specific process operation conditions. Once the conditions change, the rule based supervisor decreases the influence of one controller and gives more weight to another controller that has been designed to work in the new conditions.

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18 Get Started with FuzzyDesigner

Feedback Control System with Direct Fuzzy Controller

Control system status Primary controls

Setpoints

FUZZY

CONTROLLER

Input filter

Process Variables

Control

Control Variables

Variables

Output filter

PLANT

A fuzzy controller with the above structure typically handles multiple inputs and generates multiple outputs. This system is recommended for experienced designers since control variables are direct functions of rules. The number of rules increases rapidly with the number of inputs and fuzzy terms for inputs. The problem of dimensionality can, however, be reduced by hierarchical structuring of the rule base of the controller, which is supported by FuzzyDesigner.

Specifications and Features

FuzzyDesigner features and specifications are summarized in the following tables.

For details, refer to the subsequent chapters.

Fuzzy System Components

Components are graphical objects, blocks you work with, to design a fuzzy system.

Component Membership

functions

Type/method if applicable

Input Port

Input Linguistic Variable

Rule Block Min/product

Trapezoidal, S-shape, and their inverses

AND OR Aggregation Inference

t-norms

Defuzzification

(Activation)

Max

Output Linguistic

Trapezoidal, singleton

Variable

Output Port

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Max s-norm Mamdani/ Fuzzy

Arithmetic

CA/MCA/ MOM/SOM/ LOM

Get Started with FuzzyDesigner 19

Component Membership

functions

Type/method if applicable

Intermediate Linguistic Variable

Output T-S Variable

PID Controller

AND OR Aggregation Inference

(Activation)

Max s-norm

Defuzzification

Fuzzy System Analysis Tools

Tool Description

2D/3D mesh plots Visualization of input-output static mappings generated

by the fuzzy system or its specified subsystem

Interactive plot control Color, grid, texture, zoom, and viewpoint management

Tracing fuzzy system evaluation Marks output on the mesh when input is being changed

FuzzyDesigner Mesh Plot

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20 Get Started with FuzzyDesigner

FuzzyDesigner Mesh Plot with Simulated Path

Fuzzy System Monitoring

Feature Description

Numerical and graphical display Monitoring of all internal variables

Archiving Recording specified internal or external variables

History graph Plotting history graph for on-line or off-line monitoring

Fuzzy System Monitoring Through Numerical Displays

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Get Started with FuzzyDesigner 21

Fuzzy System Monitoring Through Plotting Historical Recordings and On-Line Update

FuzzyDesigner Project Formats

File Format Description

XML .FSP – complete project file generated by

FuzzyDesigner, .XML – user-supplied fuzzy system or project file

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22 Get Started with FuzzyDesigner

Direct Support of Logix5000 controllers

FuzzyDesigner, version 16.00 and later, supports Rockwell Automation's Logix5000 family of controllers. The fuzzy system designed using FuzzyDesigner can be exported to an RSLogix 5000 Add-On Instruction (AOI) XML import file. You can then import the fuzzy system into any of your projects as needed. Fuzzy AOI can be used by any of the programming languages (Function Block Diagram, Ladder Logic, or Structured Text). With FuzzyDesigner, you can also monitor and update the selected fuzzy AOI online, directly in the running controller. This is made available through RSLinx OPC Server.

Features Description

Export fuzzy AOI Utility for export of designed fuzzy system into L5X file.

On-line parameter change Changing parameters of a fuzzy system downloaded to the controller

dynamically is enabled.

Real-time fuzzy system monitoring Exact copy of the fuzzy system running on the PLC allows FuzzyDesigner to

monitor all internal variables on the computer when both copies are fed with the identical inputs.

Integrated Design Environment (IDE) screen captures

Some of the FuzzyDesigner features, summarized in the preceding tables, are shown in this section.

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FuzzyDesigner Environment in Brief

Get Started with FuzzyDesigner 23

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24 Get Started with FuzzyDesigner

Project Tree view

Input Linguistics Variable

Input Port

FuzzyDesigner Environment - Component examples

Rule Block Output Liguistics

Variable

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FuzzyDesigner Membership Functions

FuzzyDesigner Rule Base - Rule Editor

Get Started with FuzzyDesigner 25

Term Editor

Degree of Fulfillment window

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26 Get Started with FuzzyDesigner

FuzzyDesigner Rule Interfacing

DOF(negative)

y*y

(MCA) (CA)

FuzzyDesigner Defuzzification Methods

DOF(negative) is maximal

DOF(zero)

* y* y*

(SOM) (MOM) (LOM)

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FuzzyDesigner PID Controller

Get Started with FuzzyDesigner 27

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28 Get Started with FuzzyDesigner

Notes:

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FuzzyDesigner Component Library

Chapter

Introduction

Component Interface

The FuzzyDesigner Component Library offers eight components from which you can efficiently build distributed fuzzy systems.

Topic Page

Component Interface 29

Library of Components 30

Supported Membership Functions 30

Input Port 32

Input Linguistic Variable 34

Output Linguistic Variable 36

Output Takagi-Sugeno Variable 42

Intermediate Linguistic Variable 46

Rule Block 47

PID Controller 52

Output Port 56

The connection between components is called a link. Generally, a Hierarchical Fuzzy System (HFS) computes with data in the form of a crisp (real) value and/or a fuzzy set. Not all components enable both types of data to be transferred over the link. The data type on both ends of a link should match. FuzzyDesigner uses icons to define a link type as follows.

FuzzyDesigner Icons

Icon Description

Crisp value (input or output value link) – input crisp values and crisp values

29 Publication LOGIX-UM004A-EN-P - March 2007

resulting from defuzzification are transferred over the link

Crisp value (input or output value link) – crisp values are transferred over the link

DOF value (input or output logical link) – degrees of fulfillment of fuzzy terms of a fuzzy variable are transferred over the link to a rule block

DOF value (input or output logical link) – degrees of fulfillment of fuzzy terms resulting from rule block evaluation are transferred over the link to a fuzzy variable

30 FuzzyDesigner Component Library

Library of Components

The FuzzyDesigner Component Library offers the following components from which you can assemble fuzzy systems ranging from single input – single output systems to multiple input – multiple output systems with complex hierarchical structure of rules.

FuzzyDesinger Component Library Icons

Icon Name Description

Input Port Preprocesses and stores values of a fuzzy

system’s input variables.

Output Port Stores values of a fuzzy system’s output

variables.

Input Linguistic Variable

Rule Block Stores rules and computes degree of fulfillment

Stores linguistic terms and is used for classification of the actual component input, represented by a crisp value, into the fuzzy sets defined for the respective linguistic terms. In fuzzy control, the process where the input is converted from a crisp value is commonly called

fuzzification.

of rule conditions .

Supported Membership Functions

Intermediate Linguistic Variable

Output Linguistic Variable

Output Takagi-Sugeno Variable

PID Controller Allows intelligent supervision of a built-in PID

Bridges logical chaining of rule blocks.

Stores linguistic terms and computes the output value from degrees of fulfillment of stored terms (defuzzification). It implements the process of activation of output linguistic terms defined as fuzzy sets.

Stores parameters of functional terms and computes the output value from degrees of fulfillment of terms.

controller.

Library blocks let you work with fuzzy sets as defined by membership functions. Let x be the linguistic variable and A(x) be the degree of membership of x to the fuzzy set A defined by the sketched membership function. FuzzyDesigner works with the following types of membership functions.

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