Course detail

Control Theory

FSI-VVFAcad. year: 2020/2021

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.

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.


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.


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Švarc,I.:: Automatizace-Automatické řízení, skriptum VUT FSI Brno, CERM 2002, ISBN 80-214-2087-1
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
Zelinka Ivan, Oplatková Zuzana, Šeda Miloš, Ošmera Pavel, Včelař František; Evoluční výpočetní techniky - principy a aplikace; BEN - technická literatura, Praha 2009; ISBN 978-80-7300-218-3
Morris,K.: Introduction to Feedback Control, Academic Press, San Diego, California 2002.
Morris,K.: Introduction to Feedback Control, Academic Press, San Diego, California 2002.
Franklin, G.F., Powell, J.D., Emami-Naeini, A. Feedback Control of Dynamic Systems, Prentice Hall 2002.
Nguyen, H.T., Prasad, N.R., Walker, C.L., Walker, E.A. A First Course in Fuzzy and Neural Control. Chapman & Hall/CRC 2002.
Nguyen, H.T., Prasad, N.R., Walker, C.L., Walker, E.A. A First Course in Fuzzy and Neural Control. Chapman & Hall/CRC 2002.

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.

Language of instruction


Work placements

Not applicable.

Course curriculum

Physical background of control.
Discrete analogy of continuous PID algorithms and their variants as a basic reference for comparing the regulators.
Self-tuning Controller (STC)
State controller
Discrete quadratic optimal control LQG methods for design controller
Artificial intelligence in controls algorithms. Fuzzy Logik, fuzzy controllers
Artificial neural networks, learning methods
Adaptive optimal controller with identification by neural networks (quantisation effect).
Control algorithms with using of neural networks
Predictive control
Digital and continuous filtration
Optimal filtration (Kalman filter)

Computer exercise:
Introductory lesson (organisation, instructions, safety). Demonstration. Introduction to Automation Studio for direct implementation of real-time control algorithms in MATLAB/Simulink- PLC B&R-physical models.
Programing S-function in MATLAB.
Realisation of discrete variants of continuous PID controllers, optimizing of setting parameters.
Identification of parameters ARX model in real time.
Submission of projects.
Realisation of self-tuning controller
A proposal of LQ controller
Methods of solving algorithms LQ controllers
Realisation of fuzzy controller
Control of physical models.
Control of heating tunnel.
Control of synchronous motors.
Presentation of protocols, credit.


The basic aim of the course is to provide students with the knowledge of physical principles of control, optimal control, adaptive control, fuzzy control and identification of dynamic systems.

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.

Classification of course in study plans

  • Programme M2I-P Master's

    branch M-AIŘ , 2. year of study, winter semester, 6 credits, compulsory
    branch M-AIŘ , 2. year of study, winter semester, 6 credits, compulsory

Type of course unit



26 hours, optionally

Teacher / Lecturer


The lectures are divided into 5 topic blocks.
Block 1: Physical nature of regulation
Block 2: PID controller (continuous and discrete, andi-windup, bumpless switching, advanced structural modifications)
Block 3: Identification of dynamic systems, Adaptive control and regulation (self-adjusting controller, possibilities of artificial intelligence, recursive least squares methods, regression model, controllers based on the field placement method).
Block 4: Optimal Control and Automatic Control Algorithm Generation (Applied Grammar Evolution, Genetic Programming, Nonlinear Optimization Methods)
Block 5: Fuzzy controllers (theory of fuzzy sets, principles of inference, fuzzification and defuzzification, PI / PD / PID controllers, standardized universe forms, fuzzy supervisor, fuzzy switch, fuzzy controller with multiple inputs).

Laboratory exercise

12 hours, compulsory

Teacher / Lecturer


1L: Matlab / Simulink and Data Acquisition, Real-Time Toolbox, Real-Time Workshop
2-3L: Project: Automation Studio and B+R Automation (Thermal Control / Drive Control)
4-5L: Project: D-Space (Magnetic Levitation / Helicopter / Platform Stabilization)
6L: Final project presentations.

Computer-assisted exercise

14 hours, compulsory

Teacher / Lecturer


1C: PID controller properties, implementation methods.
2C: Optimization of PID controller parameters (classical and modern approaches).
3C: Automatic generation of control law (algorithms).
4C: Identification of dynamic systems (non-parametric methods).
5C: Identification of dynamic systems (parametric methods).
6C: Fuzzy Controller.
7C: Neural Controller.


eLearning: opened course