بسم الله الرحمن الرحيم Islamic University of Gaza Electrical Engineering Department

# بسم الله الرحمن الرحيم Islamic University of Gaza Electrical Engineering Department

Télécharger la présentation

## بسم الله الرحمن الرحيم Islamic University of Gaza Electrical Engineering Department

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
##### Presentation Transcript

1. بسم الله الرحمن الرحيمIslamic 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

3. Course Syllabus Islamic University of Gaza Faculty of Engineering Department of Electrical and Computer Engineering Fuzzy Logic Control EELE 6321 Prerequisite: EELE 3360 or consent of instructor Instructor : Basil Hamed, Ph.D. Control Systems Engineering Office : B329 e-mail : bhamed@ iugaza.edu bahamed@hotmail.com WebSite : http://site.iugaza.edu.ps/bhamed/Phone : 2860700 Ext. 2875 Meeting :Monday 2:00-5:00 (I 614)

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, 3rdEd. 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 Control 2-       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 Syllabus Grading System: Homework 10 % Project & Presentation 20 % Mid term Exam (Paper) 20 % ( 6/11/ 2017 ) Final Exam 50 % ( 25/12/ 2017 ) Office Hours:: Saturday, Monday, Wednesday (11:00-1:00) Sunday (9:00-11:00) 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 are encouraged to use material related to their research activities.  Projects must be approved by the instructor.

13. Computer Usage Extensive use of MATLAB or LabVIEW for computer aided analysis and simulation. Students are required to use these tools and equipment for special project and homework development.

14. Signals LTI System + H(z) G(z)

15. Output Controller Process 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 Desired Output Required Input

16. Input Output System What is a System? System: Block box that takes input signal(s) and converts to output signal(s). • Continuous-Time System:

17. Process Dynamics Controller/ Amplifier Desired Output Input Output + - Measurement Closed Loop Control • Open-loop control is ‘blind’ to actual output • Closed-loop control takes account of actual output and compares this to desired output

18. Model of Control System Desired System Performance Control Noise Signal Capture Actuators Sensors Mechanical System Disturbances Environment

19. Digital Control System Configuration

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

21. 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

22. 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. See You next Monday