Industrial Application of
Fuzzy Logic Control
Tutorial and Workshop
© Constantin von Altrock
Fuzzy Logic Development Methodology
According to ISO/IEC Standards
Inform Software Corporation
2001 Midwest Rd.
Oak Brook, IL 60521, U.S.A.

Relevant Standards for Fuzzy Logic

Future Standards for Fuzzy Logic

General Development Methodology
- Goals
- Phase Plan
- Support with Fuzzy Software Tools

Specific Development Methodology
- Goals
- Design Decisions
- Fuzzy Design Wizards
German Version Available!
Phone 630-268-7550
Fax 630-268-7554
Email: [email protected]
Internet: www.fuzzytech.com
© INFORM 1990-1998
Slide 1
Fuzzy Logic:
- Relevant Standards ISO 9000
International Quality Standard
 Design Documentation
 Design Modifications Documentation
 Testing Procedure Documentation
IEC 1131+ Industrial Automation Standard
 Data Exchange Formats for Portability
Both General and Specific
Standards for Fuzzy Logic
Systems Development Exist !
© INFORM 1990-1998
 Integration with Conventional Techniques
 Development Methodology
Slide 2
Fuzzy Logic:
- Future Standards IEEE Standard
Specific Standards for Fuzzy Logic:
 Terminology and Algorithms
(Different words for the same things and same words for
different things are confusing for practitioners)
 Universal Programming Language for Fuzzy Logic
 Meaningful Benchmarks
(Real-world oriented benchmarks for platform selection
and comparison)
 Further Refined Development Methodology
 Fuzzy-”Plug-Ins” for Standard Applications
 Adaptation Techniques for Fuzzy Logic Systems
Under Construction !
© INFORM 1990-1998
Slide 3
General Fuzzy Logic
Development Methodology
Goals of the General Fuzzy Logic Development Methodology:
 Definition of Non-Ambiguous and Transparent Design Steps
 Definition of Minimum Criteria for Project Structuring, Reporting, and
Documentation (Final System, Revision, and Design Steps)
Validation Complies with ISO 9000 !
The Results of This Are:
 A Complete and Transparent Coverage of the Entire Development Process
 Raised Awareness for Design Step Decision Criteria and Their Consequences
 Non-Ambiguous Mapping of External Services in a Complete Development Project
 Protection from Liability Claims
 Unfortunately, Also More Effort…
© INFORM 1990-1998
Slide 4
General Fuzzy Logic
Development Methodology
Phase Plan:
The General Fuzzy Logic
Development Methodology
Structures the Complete Project !
Preliminary Evaluation
A-Audit
Prototype
B-Audit
Off-Line Optimization
Setup
Optimization
Documentation
A-Report
B-Report
Project Start
Acceptance
Audit = Capture of Process Knowledge and Experience from Operators and Engineers
Report = Protocol of an Audit Result
© INFORM 1990-1998
Slide 5
General Fuzzy Logic
Development Methodology
Preliminary Evaluation
Assessment As to Whether Fuzzy Logic Is Applicable for the Given Application
Problem Analysis Before Project Start !
Evaluation Criteria:
 Has Fuzzy Logic Been Previously Applied to a Similar Application
With Success?
 Is It a Multi-Variable Type Control Problem?
 Do Operators and Engineers Possess Knowledge About Any
Relevant Interdependencies of the Process Variables?
 Can Further Knowledge About the Process Behavior Be Gained By
Observation Or Experiments?
 Is It Difficult to Obtain a Mathematical Model from the Process?
© INFORM 1990-1998
Slide 6
General Fuzzy Logic
Development Methodology
A-Audit
Systematic Representation of the
Available Process Knowledge !
Preparation:
 Auditors: Familiarization with the Domain of the Application, Definition of a Specific
Questionnaire
 Plant Operators: Documentation of the Existing Measurement and Control Systems,
Description of All Relevant Sensors and Actuators of the Process -- Including Min/Max
Values and Tolerances, and Provision of Trend Charts Which Display Typical Behavior
Action:
 Analysis of the Quality Variables and Criteria Variables
 Analysis of the Command Variables
 Analysis of the Current Performance
A-Report:
 Written Summary of the Audit by the Auditors, Review and Acceptance by the Plant Operator
© INFORM 1990-1998
Slide 7
General Fuzzy Logic
Development Methodology
Prototype
Implementation of a Prototype Based on the A-Report:
 As a Demonstration for the B-Audit
 As a Starting Point for the Next Development Step
Prototype Generation According to the Specific Fuzzy Development Methodology:
 Definition of the System Structure
 Creation of the Vocabulary
 Formulation of a First Rule Base
Rapid Application Development !
© INFORM 1990-1998
Slide 8
General Fuzzy Logic
Development Methodology
B-Audit
Revision of the Prototype !
Preparation:
 Auditors: Preparation of the Prototype as a Demonstration, Creation of a
Questionnaire Comprising All Open Issues
Action:
 Joint Discussions About Inconsistencies and Missing Parts of the A-Audit , Plus
Further In-Depth Discussion about all Unclear Issues Still Remaining
 Revision of the A-Report to the B-Report
 Definition of Procedures for a Safe Setup of the Controller with Respect to not
Disturbing the Running Process and Endangering Process Safety
B-Report:
 Written Summary of the Audit by the Auditors, Plus Review and Acceptance by the
Plant Operator
© INFORM 1990-1998
Slide 9
General Fuzzy Logic
Development Methodology
Off-Line Optimization
Extension and Refinement of the Prototype Based on the Results of the B-Report:
 Revision of the Linguistic Variable Definitions
 Revision and Extension of the Rule Base
Verification By:
 Off-Line Analysis of the Partial Transfer Characteristics
 Use of Existing Software Simulations of the Plant
 Use of Existing Recorded Process Data for Testing
Implementation of Operation Knowledge !
© INFORM 1990-1998
Slide 10
General Fuzzy Logic
Development Methodology
Setup
Online Optimization
Setup of the Fuzzy Logic Controller:
 Implementation on the Target Hardware and Online-Link to the Development PC
 Integration with Existing Control System and Implementation of Pre-/Postprocessing
 Creation of Safety Function (Limits, Manual Operation Switch, Safe State,..)
Online Optimization of the Fuzzy Logic Controller:
 “Open-Loop” Operation to Validate the Controller’s Behavior
 “Supervised” Operation for Fine-Tuning of Rules and Membership Functions
 “What-If” Analyses to Optimize Control Performance
Verification with the Running Process !
© INFORM 1990-1998
Slide 11
General Fuzzy Logic
Development Methodology
Documentation
Documentation of the Final Design of the Fuzzy Logic Controller:
 Structure of Fuzzy Logic System
 Vocabulary of Fuzzy Logic System
 Rule Bases of Fuzzy Logic System
Using Fuzzy Logic Development
Tools Drastically Reduces
Paperwork Expenditures !
Documentation of the Development Process:
 Audit Reports
 Final Reports
 Development History of the Fuzzy Logic System
© INFORM 1990-1998
Slide 12
General Fuzzy Logic
Development Methodology
fuzzyTECH Software Development Tools for Fuzzy Logic Systems Support the Process By:
 Including Local Documentation for All Objects of a Fuzzy Logic System
 Automatic Generation of Complete System and History Documentation
 An Embedded Revision Control System
The Inter-Operation of these
Components Reduces the
Documentation Effort by 75 - 95% !
© INFORM 1990-1998
Slide 13
General Fuzzy Logic
Development Methodology
Ability to Include Comments About Any Object of a Design Within the
Development Software:
Definition of Comments:
Transparent View of the Comments
During Development:
The Documentation Evolves
DURING Development !
© INFORM 1990-1998
Slide 14
General Fuzzy Logic
Development Methodology
Automated Generation of Complete System Documentation:
 Export, Modify, and Print
in Word Processor
 Automatic Integration into
Plant Operation Manual
 Documentation in Multiple
Languages
Complete Documentation at
Any Level of Development in
Just Seconds !
© INFORM 1990-1998
Slide 15
General Fuzzy Logic
Development Methodology
Integrated Revision Control System:
 Complete Development
History in a Single File
 Documentation of all
Levels of Development
 Protection Against
Unauthorized Access
Access the Entire Development
History at Any Time !
© INFORM 1990-1998
Slide 16
Specific Fuzzy Logic
Development Methodology
Goals of the Specific Fuzzy Logic Development Methodology:
 Definition of a Clear and Non-Ambiguous Design Approach to all Components and
Objects of a Fuzzy Logic System (Linguistic Variables, Rules, Structure,…)
 Definition of the Involved Criteria for the Design Decisions
Validation Complies with ISO 9000 !
The Results of This Are:
 “Cookbook-Recipe”-Type Definition with Respect to Real-World Needs
 Shorter Initial Training Period for Fuzzy Logic Designers
 Avoidance of Misunderstandings and Errors
 Protection Against Unsound Liability Claims
 Future Expansion or Modification of the System Without Risks
© INFORM 1990-1998
Slide 17
Specific Fuzzy Logic
Development Methodology
Design Steps:
Design Methods
Design Decisions:
 Structural Analysis
 Expert Audit
 Structural Definition
 Definition of
Vocabulary
 Offline Simulation
 Type of Membership
Function
 Definition of
Inter-Dependencies
 Verification
?
 Offline Plausibility
Testing of the Rule
Blocks
 Offline Test Using
Process Data
?
 Online Optimization
 Inference Methods
 Operator Choice
 Choice of
Defuzzification
Method
The Specific Fuzzy Logic
Development Methodology
Structures the Actual
Fuzzy System Design !
© INFORM 1990-1998
Slide 18
Design Step Overview
Structure
Linguistic Variables
 The Individual Design Decisions Are Defined by Their
Design Criteria and Consequences for the Final
System Behavior
 Each Design Step Involves Its Individual
Methodology and Design Decisions
Fuzzy Rules
 “Sanity-Checks” Are Conducted After Each Step
Offline Test
 Compliance with the Procedures Can Be Checked (It
Is “Certifiable”)
Setup
Maintenance
© INFORM 1990-1998
 The Development Path of a Fuzzy Logic System
Developed Is Transparent and Reproducible, Even to
Others
Well-Defined Design
Approach Rather Than
“Trial-and-Error” !
Slide 19
Definition of
Systems Structure
Structure
Output Variables
Linguistic Variables
Input Variables
Fuzzy Rules
Connections
Offline Test
Defuzzification
Setup
 Output Variables: What Types of
Decisions Must the Fuzzy Logic
System Make (0/1, inc/dec, absolute)?
 Input Variables: Which Are Available
from the Process, and Which Shall Be
Used First?
 Connections: Which Input Variables
Influence What Output Variables? Are
Intermediate Aggregations Possible?
 Defuzzification: “Best Compromise” or
“Most Plausible Solution”?
Maintenance
The First Development Step Defines
the Outline of the Fuzzy Logic System !
© INFORM 1990-1998
Slide 20
Systems Structure
- Output Variables Output Variables
What Types of Decisions Shall the Fuzzy Logic System Make:
 Absolute Values to Actuators
Input Variables
Connections
 Absolute Values As Set Points to Underlying Controllers
 Relative Values to Modify the Set Point Value of Underlying
Controllers (increment/decrement)
 Discrete Decisions (on/off, ...)
Defuzzification
Documentation of the Output Variables:
 What Is the Influence of Each Output Variable in the Process?
 What Is the Interval in which the Output Variable Shall Be
Varied, and Which “Typical” Values Exist?
Exact Definition of the
Expected Outputs !
© INFORM 1990-1998
 Are There “Safe State” Values?
Slide 21
Systems Structure
- Input Variables Output Variables
Input Variables
Connections
Documentation of All Available Input Variables:
 Which Aspect of the Process Are Described by the Input
Variable?
 What Is the Value Interval of the Input Variable? Which
“Typical” Values Exist?
 What Are the Tolerances of the Sensors, and How Accurate Is
the Measured Information?
Defuzzification
 What Is the Time Delay of the Input Variable?
Which of the Available Input Variables Shall Be Used:
Inventory of All
Available Input
Variables!
© INFORM 1990-1998
 For Each Output Variable, Define a List of the Its Influencing
Input Variables, Sorted by Relative Importance
 From This List, Identify the Smallest Set of Input Variables That
Suffice to Control All Output Variables
Slide 22
Systems Structure
- Connections Output Variables
Input Variables
Identification of Interdependencies in the Decision Structure:
 For Each Output Variable, Which Input Variables Are
Influencing It?
 Can Meaningful Intermediate Variables that Describe Process
States Be Defined?
Connections
Defuzzification
Simple Structure = Complex Rule Definitions
The More a Decision
Can Be Structured,
the More Transparent the Resulting
System Will Be !
© INFORM 1990-1998
Complex Structure = Simple Rule Definitions
Slide 23
Systems Structure
- Defuzzification Output Variables
Input Variables
Connections
Defuzzification
The Use of the Output
Variable in the Control
System Determines
the Defuzzification
Method !
© INFORM 1990-1998
Definition of the Defuzzification Method for Each Output Variable:
 For Continuous Variables: “Best Compromise“ := CoM
 For Discrete Variables: “Most Plausible Result” := MoM
#1: IF Temp = high OR Press = high THEN CH4 = low
#2: IF Temp = med AND Press = med THEN CH4 = med
v_high
high
med
low
1
µ
(0.6)
(0.2)
CoM
#1
#2
0
#1: IF Temp = high AND Flow = ok THEN Fire = on
#2: IF Temp = med AND Flow = low THEN Fire = off
1 #2
on
µ
off
0
CH4
(0.8)
(0.9)
#1
MoM
Fire
Slide 24
Definition of
System Structure
Output Variables
The Fuzzy Design Wizard in fuzzyTECH:
Input Variables
Connections
Defuzzification
Embedded
“Fuzzy-Expert”!
© INFORM 1990-1998
Slide 25
Definition of
Linguistic Variables
 How Many Terms Should Be Defined
for Each Linguistic Variable?
Structure
Linguistic Variables
Number of Terms
Fuzzy Rules
 Which Type of Membership Functions
Should Be Used for the Variables?
 How Can Plausible Membership
Functions for the Terms Be Defined?
Type of Memb.Fct.
Offline Test
Membership Fct.
Setup
Maintenance
© INFORM 1990-1998
The Second Design Step
Defines the Vocabulary of
the Fuzzy Logic System !
Slide 26
Linguistic Variables
- Number of Terms Number of Terms
Heuristic Method (“Cookbook Recipe”):
 Nearly All Linguistic Variables Have Between 3 and 7 Terms
Type of Memb.Fct.
 Most Often, the Number of Terms Is an Odd Number
 ... Hence the Number of Terms Is Either 3, 5, or 7
Membership Fct.
Practical Approach:
 Initial “Test”-Rules Indicate the Number of Terms Necessary
 A Rule-of-Thumb: Start With 3 Terms for Each Input Variable
and 5 Terms for Each Output Variable
Start With A Minimum Number of
Terms, Since New Terms Can Be
Added As Needed !
© INFORM 1990-1998
Slide 27
Linguistic Variables
- Membership Function Types Number of Terms
Type of Memb.Fct.
Membership Fct.
Empirical Psycho Linguistic Research Has Shown that Membership
Function Definitions Should Obey the Following Axioms:
1. µ(x) continuous over X
2. µ‘(x) continuous over X
3. µ‘‘(x) continuous over X
4. µ: minµ{maxx{µ‘‘(x)}} for all X
Cubic Interpolative Spline Functions Satisfy These Axioms:
For Most Real-World
Applications, a Linear
Approximation
Suffices !
© INFORM 1990-1998
Slide 28
Linguistic Variables
- Membership Functions Number of Terms
Definition in Four Easy Steps:
1. For Each Term, Define a Typical Value/Interval
Type of Memb.Fct.
2. Define µ=1 for This Value/Interval
3. Define µ=0 from Which the Next Neighbor is µ=1
Membership Fct.
4. Join Points With Linear / Cubic Spline Functions
Example of Linguistic Variable “Error”:
1
ONE Typical Value Per
Linguistic Term
Suffices for Definition
of Membership
Functions !
© INFORM 1990-1998
large_p: 10
positive: 3
zero: 0
negative: -3
large_n: -10
µ
0
-10
-5
0
Error
+5
+10
Slide 29
Linguistic Variables
- Membership Functions Number of Terms
Definition in Four Easy Steps:
1. For Each Term, Define a Typical Value/Interval
Type of Memb.Fct.
2. Define µ=1 for This Value/Interval
3. Define µ=0 from Which the Next Neighbor is µ=1
Membership Fct.
4. Join Points With Linear / Cubic Spline Functions
Example of Linguistic Variable “Error”:
1
A “Typical Value”
May Also Be an
Interval !
large_p: 10
positive: 3
zero: [-1;1]
negative: -3
large_n: -10
µ
0
-10
© INFORM 1990-1998
-5
0
Error
+5
+10
Slide 30
Linguistic Variables
- Membership Functions Number of Terms
Structured Definition of Linguistic Variables in fuzzyTECH:
Type of Memb.Fct.
Membership Fct.
Definition of Complete Sets
of Membership Functions
in One Easy Step !
© INFORM 1990-1998
Slide 31
Definition of the
Fuzzy Rules Base
Structure
 Which Fuzzy Logic Operator for the
Rule Premise Aggregation Step?
Linguistic Variables
 Which Fuzzy Logic Operator for the
Rule Result Aggregation Step?
 How Are the Actual Fuzzy Logic Rules
Defined?
Fuzzy Rules
Aggregation Op.
Offline Test
Result Agg. Op.
Setup
Definition of Rules
Maintenance
© INFORM 1990-1998
The Third Design Step
Defines the Actual
Control Strategy !
Slide 32
Definition of the Fuzzy Rules
- Aggregation Operator Aggregation Op.
Elementary Fuzzy Logic Operators:
 AND: µAvB = min{ µA; µB }
Result Agg. Op.
Definition of Rules
 OR: µA+B = max{ µA; µB }
 NOT: µ-A = 1 - µA
...Model Human Evaluation and Reasoning Poorly Sometimes
Example: IF Car=fast AND Car=economical THEN Car=good
The Exclusive Use of
Elementary Fuzzy
Logic Operators Can
Inflate the Rule Base !
© INFORM 1990-1998
 Car 1:
180km/h: µ=0.3
9l/100km: µ=0.4
-> 0.3
 Car 2:
180km/h: µ=0.3
7l/100km: µ=0.6
-> 0.3
 Car 3:
175km/h: µ=0.25
4l/100km: µ=0.9
-> 0.25
Mock-Up Solution: Define More Fuzzy Rules:
pretty_fast
highly_economic pretty_good
IF Car=fast AND Car=economical THEN Car=good
somewhat_fast
mildly_economic
just_ok
Slide 33
Definition of the Fuzzy Rules
- Aggregation Operator Aggregation Op.
Transfer Characeristics of MIN and MAX:
Result Agg. Op.
Definition of Rules
Compensatory Operators Better Represent Human Evaluation and
Reasoning:
In Most Real-World
Applications, MIN and
MAX Are Sufficient !
© INFORM 1990-1998
MIN
MAX
AND
OR
The Gamma-Operator Can Be Tuned:
Slide 34
Definition of the Fuzzy Rules
- Aggregation Operator Aggregation Op.
fuzzyTECH Uses Parametric Fuzzy Operators:
Result Agg. Op.
Definition of Rules
Transparent
Parameterization Through
Instant Visualization of
Transfer Characteristics !
© INFORM 1990-1998
Slide 35
Definition of the Fuzzy Rules
- Result Agg. Operator Aggregation Op.
Two Methods Are Applied in Real-World Applications:
 “The Winner Takes It All” (MAX)
Result Agg. Op.
Definition of Rules
 “One Man, One Vote” (BSUM)
Rules:
MAX:
BSUM:
med (0.6)
med (1.0)
#1: ... => Power = high (0.3)
#2: ... => Power = med (0.1)
#3: ... => Power = med (0.4)
#4: ... => Power = med (0.6)
#5: ... => Power = low (0.0)
If the Rule Base Is Not
Symmetrical, BSUM Can
Yield Wrong Results !
© INFORM 1990-1998
Slide 36
Definition of the Fuzzy Rules
- Definition of Rules Aggregation Op.
Basic Properties of Rule Bases:
 Normalization (all Brackets Resolved)
Result Agg. Op.
 Elementation (only “AND” Operators Used)
Example of a Non-Normalized Non-Elementary Rule:
Definition of Rules
IF (((Press_1 = low AND Press_2 = low) OR (Press_3 = med AND
NOT Temp_2 = high)) AND (Press_1 = low OR Temp_1 = high))
THEN CH4 = med
Different Rule Block Definition Approaches:
 Induction: Define the “THEN”-Part for All Possible Input Term
Combinations (only with 2..3 Input Variable per Rule Block)
The Experience to
Be Implemented
Determines the
Procedure!
© INFORM 1990-1998
 Deduction: Define Rules As Single Pieces of Experience
(Prefer “Thin” Rules)
 Linear Approach: Stepwise Optimization of a “Linear” Fuzzy
Rule Base (Mostly Used with “Direct” Fuzzy Controllers)
Slide 37
Definition of the Fuzzy Rules
- Definition of Rules Aggregation Op.
fuzzyTECH Supports All Three Approaches:
 Automatic Generation of Inductive Fuzzy Rule Bases
Result Agg. Op.
Definition of Rules
Definition of a
Consequence for Every
Possible Situation !
© INFORM 1990-1998
Slide 38
Definition of the Fuzzy Rules
- Definition of Rules Aggregation Op.
Result Agg. Op.
fuzzyTECH Supports All Three Approaches:
 Optimized Editors and Analyzers for Deductive Rule Definition
(Table, Text, and Matrix Type Representation)
Definition of Rules
Each Rule Expresses an
Aspect of the Experience !
© INFORM 1990-1998
Slide 39
Definition of the Fuzzy Rules
- Definition of Rules Aggregation Op.
fuzzyTECH Supports All Three Approaches:
 Fuzzy Rule Wizard for Automated Generation of Linear Rule Blocks
Result Agg. Op.
Definition of Rules
Complete Rule
Block Definition
in One Step !
© INFORM 1990-1998
Slide 40
Off-Line Testing
Structure
Rule Validation
Linguistic Variables
Process Simulation
Fuzzy Rules
 Which Fuzzy Rules Are Missing,
Superfluous, or Conflicting?
 Fine-Tuning the Linguistic Variables
With the Help of Process Simulation
 Optimization With Data From the
Actual Process
Process Data Test
Off-Line Testing
Setup
Maintenance
© INFORM 1990-1998
In Off-Line Testing, the
First Verification of the
Fuzzy System Occurs !
Slide 41
Off-Line Testing
- Rule Validation 1 Rule Validation
Analysis Tools in fuzzyTECH for Rule Validation:
Process Simulation
Process Data Test
Direct Analysis of
the Data Range in
a 3D Graph !
© INFORM 1990-1998
Slide 42
Off-Line Testing
- Rule Validation 2 Rule Validation
Analysis Tools in fuzzyTECH for Rule Validation:
Process Simulation
Process Data Test
Verification of Individual Rule
Blocks With the Statistics Analyzer !
© INFORM 1990-1998
Slide 43
Off-Line Testing
- Process Simulation 1 Rule Validation
Process Simulation
Process Data Test
Dynamic Links in fuzzyTECH to Simulation Tools and
Programming Languages:

Fuzzy Control Blocks in VisSim™, Matlab/SIMULINK™, ...

Standard Links Like DDE, DLL, OLE, ActiveX, Data, ...

You Can Tie in the Editors and Analyzers of fuzzyTECH
With Your Own Software

Either Complete fuzzyTECH (With All Editors and Analyzers)
or Runtime Module (Highest Performance) Can Be Used
Open Links Allow Connection
With Most Any Software !
© INFORM 1990-1998
Slide 44
Off-Line Testing
- Process Simulation 2 Rule Validation
Dynamic Monitoring and Tuning in fuzzyTECH:
Process Simulation
Process Data Test
Asynchronous Coupling of
Simulation and fuzzyTECH !
© INFORM 1990-1998
Slide 45
Off-Line Testing
- Process Data Test Rule Validation
Dynamic Optimization Using Actual Process Data in fuzzyTECH:
Process Simulation
Process Data Test
Verification of the Entire
Fuzzy Controller in Real
Process Situations !
© INFORM 1990-1998
Slide 46
Setup
Structure
 Implementation of the Fuzzy Controller
on the Target Hardware
Linguistic Variables
 Implementation of the Online Process
Link
Implementation
Fuzzy Rules
Warm Operation
Offline Test
Hot Operation
 Warm Operation = Output of the Fuzzy
Controller Is Not Switched Through to
the Process
 Hot Operation = Output of the Fuzzy
System Is Switched Through to the
Process
Setup
Maintenance
© INFORM 1990-1998
Final Validation of the
Complete Fuzzy System
in Online Operation !
Slide 47
Setup
- Implementation on Target Various Implementation Techniques Available in fuzzyTECH:




Embedded Control: Assembly Code Kernels
Industrial Automation: Fuzzy Function Blocks for PLCs
Process Supervisory Control: Fuzzy Modules for DCS, SCADA..
Universal: Common Source Code Output (C, C++, VB, Pascal, ..)
Online Link Between the Process Hardware (Target) and fuzzyTECH:
Online-Link (RS 232, ..)
Online Module
User Software:
(fuzzyTECH)
- Operating System
- Conventional
Knowledge Base
I/O
- Signal Processing
(fuzzyTECH)
fuzzyTECH
© INFORM 1990-1998
Actors
Controller Code
- Filter; Driver
Inference Engine
Sensors /
Target Hardware (SPS, VME, µC, ...)
For Most Industrial Target
Platforms, an Optimized
Implementation
Technique Exists !
Slide 48
Setup: Warm/Hot Operation
Analysis of Behavior
Over Time in
fuzzyTECH:
 Dynamic in All
Editors and
Analyzers
 Special
Behavior Over Time
of Variables, Terms
and Rules in a Time
Plot
Real-Time Remote
Debugging for
Systems Verification !
© INFORM 1990-1998
Slide 49
Operation and Maintenance
 Final Documentation of the Fuzzy
Logic Design and Its Integration
(Comprises All Previous Design Steps
Documentation)
Structure
Linguistic Variables
 Configuration of the Monitor
Component for the Supervision of
Fuzzy Logic System Performance
Fuzzy Rules
Documentation
Offline Test
 Optional Review of Fuzzy Logic Design
and Modifications of the System As
Result of Review
Monitoring
Setup
Review
Maintenance
© INFORM 1990-1998
Development Methodology
Continues During Operation !
Slide 50
Operation and Maintenance
fuzzyTECH Supports These Steps Through:
 Documentation: Documentation Generator and Revision Control System
 Remote Tracing for Online Monitoring
Unsupervised
Monitoring Using
Trigger Conditions !
 Review of the Linguistic Variables and Fuzzy Rules Through “What-If” Analyses
© INFORM 1990-1998
Slide 51
Summary of Specific Fuzzy
Development Methodology
1.
2.
3.
4.
5.
6.
Structure Definition
1.1 Documentation of All Output Variables
1.2 Documentation of All Input Variables
1.3 Structuring of the Decision (“many small rule blocks”)
1.4 Defuzzification Method Selection (“Best Compromise“ or “Most Plausible Solution”?)
Linguistic Variables
2.1 Number of Terms per Variable (start with 3 per input and 5 per output variable)
2.2 Type of Membership Function (start with Standard-MBFs)
2.3 Membership Function Definition by Standard Method (typical values => MBF’s)
Fuzzy Rule Definition
3.1 Fuzzy Operator for Aggregation (start with MIN)
3.2 Fuzzy Operator for Result Aggregation (start with MAX)
3.3 Select Rule Definition Approach Depending on Application (Inductive, Deductive, Linear)
Offline Testing
4.1 Validation of the Rule Blocks (identification of missing and conflicting rules)
4.2 Testing Using Process Simulation (if available)
4.3 Testing Using Real Process Data (if available)
An Easily Reproduced, Certifiable
Setup
Process to Reach Your Solution !
Operation and Maintenance
© INFORM 1990-1998
Slide 52
fuzzyTECH Supports Entire
Fuzzy Design Methodology
Structure
Linguistic Variables
=> Fuzzy Design Wizard
The Optimal Tool for Every
Step of the Design Process !
=> Linguistic Variable Wizard
Fuzzy Rules
=> Fuzzy Rule Wizard, Rule Block Utilities
Offline Test
=> Offline Debug Mode, Analyzer
Setup
=> Online Debug Mode, Analyzer
Maintenance
© INFORM 1990-1998
=> Trace, Documentation Generator, Revision Control System
Slide 53
Further Reading / Literature
“Hands-On” Guides for Fuzzy Logic Systems Development:
"Fuzzy Logic and NeuroFuzzy Applications Explained" by Constantin von Altrock, $39.95,
Prentice Hall, ISBN 0-1336-8465-2, shows how to design technical fuzzy logic applications
and contains a demo of the fuzzyTECH software with 10 control system simulations to
experiment with. It also explains the use of fuzzy and NeuroFuzzy techniques in over 70
case studies.
"[This book] ... is packed with information which is presented in a reader-friendly
fashion. It is a must for anyone who is interested in the analysis and design of
fuzzy logic based systems.”
Lotfi Zadeh, Berkeley
"Fuzzy Logic and NeuroFuzzy Applications in Business and Finance" by Constantin von
Altrock, $39.95, Prentice Hall 1996, ISBN 0-13-591512-0, shows how to design business
and finance fuzzy logic applications and contains a demo of the fuzzyTECH for Business
software with numerous case studies to experiment with. It also explains the use of fuzzy
and NeuroFuzzy techniques in recent successful practical implementations.
"... We owe to Constantin v. Altrock our thanks and congratulations for authoring
an innovative text that is leading the way. Fuzzy Logic and NeuroFuzzy in Business
and Finance is must reading for anyone who has a serious interest in adding new
tools to the armamentarium of decision analysis ..."
Lotfi Zadeh, Berkeley
© INFORM 1990-1998
Slide 54
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Industrial Application of Fuzzy Logic