Rockwell Automation RSLogix 5000 User Manual

RSLogix 5000

Fuzzy Designer

User Manual

Important User Information Solid state equipment has operational characteristics differing from those of electromechanical equipment. Safety Guidelines for the Application,

Installation and Maintenance of Solid State Controls (publication SGI-1.1 available from your local Rockwell Automation sales office or online at http://literature.rockwellautomation.com) describes some important differences between solid state equipment and hard-wired electromechanical devices. Because of this difference, and also because of the wide variety of uses for solid state equipment, all persons responsible for applying this equipment must satisfy themselves that each intended application of this equipment is acceptable.

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	Table of Contents
Preface	About This Publication. . . . . . . . . . . . . . . . . . . . . . . . . . . . .	7
	Who Should Use This Publication. . . . . . . . . . . . . . . . . . . . .	7
	Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .	7
	Chapter 1
Get Started with FuzzyDesigner	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
FuzzyDesigner Component Library	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

4

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

5

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

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

	Chapter 5
Fuzzy System Simulation	Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .	131
	Chapter 6
RSLogix 5000 Add-On Instruction	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
XML Format of a Fuzzy Project	Prolog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .	147
	Document Element . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .	147
	Chapter 8
Glossary	Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .	149

6

Preface

About This Publication

Who Should Use This

Publication

Conventions

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.

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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Chapter 1

Introduction

Understanding

FuzzyDesigner

Get Started with FuzzyDesigner

Topic	Page
Understanding FuzzyDesigner	9
Fuzzy Logic and Fuzzy Control Essentials	12
Specifications and Features	18

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
n	p

o

q

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

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

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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	Speed is medium
high
Temperature is medium and humidity	Speed is high
is very 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
Low	Medium	High	Result
1			1.00 Medium
0			0.0 High
	range		0.0 Low
			temperature
20		150
	Crisp Value

		Degree of membership (DOM) is a value describing how well
	TIP
		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
		FUZZY	feedforward
		FUZZY
		SUPERVISOR
		SUPERVISOR
		PID
		gains	CV
	SP	PID
			PLANT
		PID
		CONTROLLER	PLANT
		CONTROLLER

PV

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

	FUZZY SUPERVISOR	Plant State
	FUZZY SUPERVISOR

Schedule weights [0,1]

	CONTROLLER	×
	1
	CONTROLLER
	1			+ CV
Setpoints	CONTROLLER
				PLANT
	CONTROLLER	×
	2		+	PLANT
	2			+
	CONTROLLER
	CONTROLLER		×
	3
	3

Process Variables

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	Control	PLANT
CONTROLLER	Variables	PLANT
Input filter	Output filter
Process Variables

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	AND	OR	Aggregation	Inference	Defuzzification
	functions				(Activation)
	Type/method if applicable
Input Port
Input Linguistic	Trapezoidal,
Variable	S-shape, and their
	inverses
Rule Block		Min/product	Max
		t-norms
Output	Trapezoidal,			Max s-norm	Mamdani/ Fuzzy	CA/MCA/
Linguistic	singleton				Arithmetic	MOM/SOM/ LOM
Variable
Output Port

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Get Started with FuzzyDesigner	19

Component	Membership	AND	OR	Aggregation	Inference	Defuzzification
	functions				(Activation)
	Type/method if applicable
Intermediate				Max s-norm
Linguistic
Variable
Output T-S				Max s-norm
Variable
PID Controller

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

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Project Tree view

FuzzyDesigner Environment - Component examples

Rule Block	Output Liguistics
	Variable

Input Linguistics

Variable

Input Port

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FuzzyDesigner Membership Functions

Term Editor

Degree of Fulfillment window

FuzzyDesigner Rule Base - Rule Editor

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FuzzyDesigner Rule Interfacing

				FuzzyDesigner Defuzzification Methods
DOF(negative)
							DOF(negative) is maximal
				DOF(zero)
	y*	y*
					y* y* y*
	(MCA)	(CA)			(SOM) (MOM) (LOM)

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FuzzyDesigner PID Controller

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Notes:

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Chapter 2

FuzzyDesigner Component Library

Introduction

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

Component Interface

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 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

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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	Stores linguistic terms and is used for
	Variable	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.
	Rule Block	Stores rules and computes degree of fulfillment
		of rule conditions .
	Intermediate	Bridges logical chaining of rule blocks.
	Linguistic
	Variable
	Output Linguistic	Stores linguistic terms and computes the output
	Variable	value from degrees of fulfillment of stored terms
		(defuzzification). It implements the process of
		activation of output linguistic terms defined as
		fuzzy sets.
	Output	Stores parameters of functional terms and
	Takagi-Sugeno	computes the output value from degrees of
	Variable	fulfillment of terms.
	PID Controller	Allows intelligent supervision of a built-in PID
		controller.

Supported Membership

Functions

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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