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Course detail

Control Theory III

FSI-VVFAcad. year: 2009/2010

The course is focused on modern methods of the analysis and design of control systems.The centre of the interest are adaptive systems, expert control, optimal control,systems with applied artificial intelligence and algebraic theory of control systems.

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

prof. Ing. Pavel Ošmera, CSc.

Department

Institute of Automation and Computer Science (ÚAI)

Learning outcomes of the course unit

...To prepare students for solving complicated tasks of automatic control.
...Analysis and design of modern feedback control systems. Students will obtain the basic knowledge of optimal control, adaptive control and fuzzy control.

Prerequisites

...Fundamental concepts of the methods used in the analysis and design of linear continuous feedback control systems. Essential principles of automatic control, logical control and PLC systems. The differential equations of control systems, transient response, frequency analysis, stability of systems. Fundamental concepts of the methods used in the analysis and design of nonlinear continuous feedback control systems and discrete control systems.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

...In order to be awarded the course-unit credit students must prove 100% active participation in laboratory exercises and elaborate a paper on the presented themes. The exam is written and oral. In the written part a student compiles two main themes which were presented during the lectures and solves three examples. The oral part of the exam will contain discussion of tasks and possible supplementary questions.

Course curriculum

1. An introduction to fuzzy sets and fuzzy logic
2. The mathematics of fuzzy Systems
3. Fuzzy relations
4. Representing a set of rules
5. Adaptive Fuzzy control
6. Neural network - introduction, the biological paradigm
7. Single-lear neural networks
8. The multiple-layer Perceptron
9. Radial basis function
10. Recurrent networks
11. Unsupervised learning and clustering algorithms
12. The backpropagation algorithm
13. The Hopfield model
14. Kohonen networks
15. Hardware for neural networks
16. Genetic algorithms

Work placements

Not applicable.

Aims

...Goals of the course: The basic aim of the course is to provide students with the knowledge
of optimal control, adaptive control and fuzzy control.

Specification of controlled education, way of implementation and compensation for absences

...Attendance and activity at the seminars are required. One absence can be compensated for by attending a seminar with another group in the same week, or by the elaboration of substitute tasks. Longer absence can be compensated for by the elaboration of compensatory tasks assigned by the tutor.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Vegte, V.D.J.: Feedback Control Systems, Prentice-Hall, New Jersey 1990, ISBN 0-13-313651-5
Levine, W.S. (1996) : The Control Handbook, CRC Press, Inc., Boca Raton, Florida 1996 , ISBN 0-8493-8570-9
Morris,K.: Introduction to Feedback Control, Academic Press, San Diego, California 2002.

Recommended reading

Švarc,I.:: Automatizace-Automatické řízení, skriptum VUT FSI Brno, CERM 2002, ISBN 80-214-2087-1

Classification of course in study plans

  • Programme M3I-P Master's

    branch M-AIŘ-3 , 2. year of study, winter semester, compulsory

  • Programme M2I-P Master's

    branch M-AIŘ , 2. year of study, winter semester, compulsory
    branch M-AIŘ , 2. year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

prof. Ing. Radomil Matoušek, Ph.D.
doc. Ing. Ivan Švarc, CSc.

Syllabus

Week 1: Polynomial Equations Approach to Control Systems.
Week 2: Introduction to algebraic theory of control.
Week 3: Use of algebraic theory for design of controllers.
Week 4: Controllers with preliminary constrains and conditions.
Week 5: Adaptive systems. Approaches to adaptive control.
Week 6: Self-tuning control.
Week 7: Model reference adaptive control (MRAC)
Week 8: Optimal control. Introduction.
Week 9: Quadratic optimal control.
Week 10: Time and energy optimality.
Week 11: Fuzzy control.
Week 12: Neural control.
Week 13: Modern trends in automatic control.

Exercise

14 hours, compulsory

Teacher / Lecturer

Ing. Olga Davidová, Ph.D.
Ing. Vladimír Tůma

Syllabus

Seminars to the previous lecture:
--------------------------------------
Week 1: Polynomial Equations Approach to Control Systems. Introduction to algebraic theory of control.
Week 3: Use of algebraic theory for design of controllers.Controllers with preliminary constrains and conditions.
Week 5: Adaptive systems. Approaches to adaptive control.Self-tuning control.
Week 7: Model reference adaptive control (MRAC). Optimal control.
Week 9: Quadratic optimal control.Time and energy optimality
Week 11: Fuzzy control.Neural control
Week 13: Modern trends in automatic control.

Computer-assisted exercise

12 hours, compulsory

Teacher / Lecturer

Ing. Olga Davidová, Ph.D.
Ing. Vladimír Tůma

Syllabus

Seminars in the laboratory:
-------------------------------
Week 2: Simulation of results given by algebraic theory.
Week 4: Simulation of MRAC system.
Week 6: Simulation of time and energy optimality.
Week 8: Simulation of quadratic optimal control.
Week 10: MATLAB toolboxes for optimal control.
Week 12: Model of fuzzy controller.