Course detail

Control Theory

FSI-VVF-KAcad. year: 2016/2017

The course is aimed to modern methods in design and synthesis of control circuits using methods of artificial intelligence. Presented are selected methods of artificial intelligence, optimal and adaptive methods of control, fuzzy control and neural controller. Students will adopt theoretical and practical implementation of these methods and RT control. The course broadens knowledge of specific parts of applied informatics in the field of advanced control. Used is the most advanced software and hardware technology of companies B&R Automation and Mathworks (Matlab/Simulink) and substantial know-how of course's authors.

Language of instruction

Czech

Number of ECTS credits

5

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 by means of artificial intelligence methods.
Analysis and design of modern feedback control systems. Students will obtain the basic knowledge of optimal control, adaptive control, fuzzy control and ANN control.

Prerequisites

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

Co-requisites

Not applicable.

Planned learning activities and teaching methods

The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures. Teaching is suplemented by practical laboratory work.

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

Not applicable.

Work placements

Not applicable.

Aims

The basic aim of the course is to provide students with the knowledge
of optimal control, adaptive control, fuzzy control and artificial neural network 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

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, ISBN 0-12-507660-6
Vegte, V.D.J.: Feedback Control Systems, Prentice-Hall, New Jersey 1990, ISBN 0-13-313651-5

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 M2I-K 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

 

Guided consultation

13 hod., optionally

Teacher / Lecturer

doc. Ing. Ivan Švarc, CSc.
prof. Ing. Pavel Ošmera, CSc.

Syllabus

Lectures are divided to 6 topic blocks:
Block 1: Technology: B&R Automation, Mathworks (Matalab/Simulink and selected toolboxes: TT, RTW, Fuzzy, ANN), dSpace and other technologies used in course.
Block 2: Adaptive control and regulation (self-tuning controller, options of artificial intelligence, recursive methods of mean square error, regression model, pole placement based controllers, delta models).
Block 3: Optimal control and auto-generation of control law (applied grammar evolution, genetic programming, methods of nonlinear optimization, HC12 algorithm)
Block 4: Fuzzy controllers (fuzzy set theory, inference principles, fuzzification and defuzzification, PI/PD/PID controllers, fuzzy supervisor, fuzzy switch, fuzzy controller with multiple inputs).
Block 5: Neuron nets in control technology (theory of selected neuron nets, neuron PID controller, controllers with model, adaptive forms, adaptive control of nonlinear systems).
Block 6: Modern trends in artificial intelligence and autonomous control. (end of course)

Laboratory exercise

4 hod., compulsory

Teacher / Lecturer

prof. Ing. Pavel Ošmera, CSc.
doc. Ing. Ivan Švarc, CSc.

Syllabus

1L: Matlab/Simulink and tools Data Acquisition, Real-Time Toolbox, Real-Time Workshop (control of heat system)
2L: Automation Studio and technology B+R Automation 1/2 (control of heat system)
3L: Project: Motion control (ACOPOS, IclA, Maxon)
4L: Project: Magnetic levitation, Helicopter
5L: Project: Stabilization of platform
6L: Presentation of projects