بسم الله الرحمن الرحيم Islamic University of Gaza Electrical Engineering Department

الرحيم الرحمن الله بسم

Islamic University of GazaElectrical Engineering

Department

2

Fuzzy Logic Control EELE 6306

By

Basil Hamed, Ph. D.

Control Systems Engineering

http://site.iugaza.edu.ps/bhamed/

E-Mail: bhamed@iugaza.edu

By

Basil Hamed, Ph. D.

Control Systems Engineering

http://site.iugaza.edu.ps/bhamed/

E-Mail: bhamed@iugaza.edu

3

Islamic University of GazaFaculty of Engineering

Department of Electrical and Computer Engineering

Fuzzy Logic Control EELE 6306Prerequisite: EELE 3360 or consent of instructor

Instructor : Basil Hamed, Ph.D. Control Systems Engineering

Office : B251e-mail : bhamed@ iugaza.edu

bahamed@hotmail.comWebSite : http://site.iugaza.edu.ps/bhamed/Phone

: 2860700 Ext. 2894Meeting : Sunday 2:00-5:00 (I 108)

Course Syllabus

4

Course Syllabus

Course Description: Fuzzy logic is a design method that can be effectively applied to problems that, because of complex, nonlinear or ambiguous system models, cannot be easily solved using traditional analytical control techniques.  This course discusses the types of applications for which fuzzy control is useful and introduces basic concepts of fuzzy set theory, fuzzy logic operations, fuzzification and de-fuzzification. Several types of fuzzy control design.

5

Course Syllabus

Text Book: Fuzzy Logic with Engineering Applications, 3rd Ed. John-Wiley, 2004, T.J. Ross,

References:• L. X. Wang, "A Course in Fuzzy Systems and Control",

Prentice-Hall, 1997. • K. M. Passino, "Fuzzy Control", Addison-Wesley, 1998.• Fuzzy Set Teory, 1997, G.Klir et al. Prentice Hall• Fuzzy Sets and Fuzzy Logic 1995, G Klir et al. Prentice Hall• Foundation of Fuzzy Control ,Jan Jantzen 2007

6

Course Objectives

The objectives of this course are to:

•Help students to be familiar with the fundamental concepts of fuzzy set theory and fuzzy logic; •Foster competence in recognizing the feasibility and applicability of the design and implementation of intelligent systems (that employ fuzzy logic) for specific application areas; and •Help students develop a sufficient understanding of fuzzy system design methodology and how it impacts system design and performance

7

Materials Covered:1-       Introduction, Definitions and Concepts Intelligent Control Fuzzy Logic Fuzzy Control Applications Rule Base Fuzzy Sets Classic versus Fuzzy Control System Design An Example of Fuzzy Control2-       Fuzzy Mathematics Fuzzy Sets and Membership Functions Mathematical Operations on Fuzzy Sets Fuzzy Relations Linguistic Variables Fuzzy Rules Approximate Reasoning

8

Materials Covered:3-       Fuzzy Systems•Fuzzy Rule Base•Fuzzy Inference Engine•Fuzzification•Defuzzification•Mathematical Representations of Fuzzy Systems•The Approximation Properties of Fuzzy Systems

4-       Design of Fuzzy Controllers•Trial and Error Approach•Control surface of a fuzzy controller•Stable Fuzzy Controllers•Fuzzy Supervisory Control•Fuzzy Gain Scheduling•TSK Fuzzy Systems

9

Course Intended Learning Outcomes:

Upon successful completion of the course, students should be able to: •Utilize the state of the art topics of fuzzy control in their research activities.•Design fuzzy systems and fuzzy controllers. exhibit familiarity with the fundamental concepts of fuzzy set theory and fuzzy logic; •Recognize the feasibility and applicability of the design and implementation of intelligent systems (that employ fuzzy logic) for specific application areas; and•Understand fuzzy system design methodology and how it impacts system design and performance.

10

Course SyllabusGrading System:

Homework 10 %

Project & Presentation 25 % Mid term Exam 25 % (10/11/2013)Final Exam 40 % ( 22/12/2013)

Office Hours:: Saturday, Monday, Wednesday (10:00-11:00, 1:00-2:00)Sunday (12:00-2:00)

Open-door policy, by appointment or as posted.

11

Homework There will be several homework

assignments/computer projects during the term. These assignments will require students to program in MATLAB or a similar language. Students can seek help and work together on homework and projects, but each student must turn in his/her own write-up. 

12

Projects Students will be required to develop an

application of fuzzy logic to a problem of their choice. They will prepare a paper, including a survey of current literature, and make a presentation to the class.  Students may work alone or in groups of 2 and are encouraged to use material related to their research activities.  Projects must be approved by the instructor.  

Computer Usage

Extensive use of MATLAB or LabVIEW for computer aided analysis and simulation. The digital computers used are IBM PC Pentium compatible, these are available in the Department of Electrical Engineering. Students are required to use these tools and equipment for special project and homework development.

13

14

Signals

LTI System

H(z)

G(z)

+

15

What is a Control System

• A Process that needs to be controlled:– To achieve a desired output– By regulating inputs

• A Controller: a mechanism, circuit or algorithm– Provides required input – For a desired output

RequiredInput

DesiredOutput

ProcessOutput

Controller

16

What is a System?

System: Block box that takes input signal(s) and converts to output signal(s).

• Continuous-Time System:

SystemInput Output

17

Closed Loop Control

• Closed-loop control takes account of actual output and compares this to desired output

Measurement

DesiredOutput

+-

ProcessDynamics

Controller/Amplifier

OutputInput

• Open-loop control is ‘blind’ to actual output

18

Model of Control System

Environment

Sensors

ActuatorsSignal Capture

Control

Mechanical System

Disturbances

Noise

Desired SystemPerformance

19

Digital Control SystemConfiguration

20

Control

• Control: Mapping sensor readings to actuators– Essentially a reactive system

• Traditionally, controllers utilize plant model– A model of the system to be controlled– Given in differential equations

• Control theory has proven methods using such models– Can show optimality, stability, etc.– Common term: PID (proportional-integral-derivative)

control

Fuzzy Control

• When plant model unavailable• A set of rules associating sensor readings with

action

IF Close-by(right-sensor) THEN Left

IF Clear(forward-sensor) THEN Forward

IF Right(goal-location) THEN Right • All rules fire in parallel, and their results are

combined

Fuzzy Logic

• Fuzzy logic: – A way to represent variation or imprecision in logic– A way to make use of natural language in logic– Approximate reasoning

• Humans say things like "If it is sunny and warm today, I will drive fast"

• Linguistic variables:– Temp: {freezing, cool, warm, hot}– Cloud Cover: {overcast, partly cloudy, sunny}– Speed: {slow, fast}

23

Fuzzy Controller

24

25

See You next Sunday