Czech

6

Not applicable.

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.

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.

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.

Lecture:
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.

Not applicable.

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.

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.

Not applicable.

Not applicable.

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.
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.

Švarc,I.:: Automatizace-Automatické řízení, skriptum VUT FSI Brno, CERM 2002, ISBN 80-214-2087-1
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.
Nguyen, H.T., Prasad, N.R., Walker, C.L., Walker, E.A. A First Course in Fuzzy and Neural Control. Chapman & Hall/CRC 2002.

eLearning: currently opened course

14 hours, compulsory

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: currently opened course