Course detail

Selected chaps from automatic control

FEKT-DKC-AM1Acad. year: 2022/2023

The subject focuses at studies of the methods of design of advanced control algorithms including classical control structures as well as algorithms of robust, adaptive and predictive control. Attention is also paid to information processing algorithms and state observers for realization of so called virtual sensors and algorithms of sensorless control. Traditional methods for systems control and processing information are complemented by artificial intelligence-based approaches. In addition to the theoretical aspects of the given topic, sample algorithms for advanced drives, mechatronic systems and mobile robots are also solved.

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Learning outcomes of the course unit

The graduate should be able to design and tune sophisticated control systems with elements of AI


Principle of continuous control theory. Principle of discrete control theory. State control.


Not applicable.

Planned learning activities and teaching methods

Techning methods include lectures and selfstudy. Course is taking advantage of e-learning (Moodle) system

Assesment methods and criteria linked to learning outcomes

Elaboration of 2-3 projects (30 points). Final exam (70 points).

Course curriculum

1. Modern approaches in automatic control
2. Robust control of dynamic systems with uncertainty considerations
3. Specific Adaptive Control Problems
4. State controller as the basic structure for model based predictive control
5. State observability theory of nonlinear dynamic systems
6. Principles of using virtual sensors for sensorless control, example of control applications for actuators with asynchronous and synchronous motors
7. Artificial neural networks (NS) and their learning methods.
8. Control theory and artificial intelligence, NS-based control algorithms.
9. Identification of systems using NS, adaptive optimal controller based on NS identification.
10. Modern methods of autonomous outdoor and indoor self-localisation.
11-12. Advanced 3D mapping - sensors, data fusion methods, data representation, practical use.
13. Summary

Work placements

Not applicable.


The objective of the course is to develop students' knowledge and competences in the field of design of advanced control algorithms and algorithms for information processing based on classical mathematical approaches as well as artificial intelligence, including possible advanced applications such as mobile robotics.

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

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Skogestad S., Postletwaite I.: Multivariable Feedback Control, John Wiley & Sons, 2007. (EN)
Slotine J. J. E., Li W.: Applied Nonlinear Control, Prentice-Hall, 1991 (EN)

Recommended reading

Gu D.-W., Petkov P. H.: Robust Control Design with MATLAB, Springer, 2013 (EN)
Russell S., Norvig P.: Artificial Intelligence a Modern Approach. Prentice Hall 2010 (EN)
Voseelman G., Mass H-G. Airborne and Terrestrial Laser Scanning, CRC Press, 2010 (EN)
Goodwin G.C., Seron M.M. , Doná J.A. Constrained Control and Estimation, Springer, 2005 (EN)
Hermann R. ,Krener A., Nonlinear controllability and observability, IEEE Transactions on Automatic Control, vol. 22, no. 5, pp. 728–740, 1977 (EN)

Classification of course in study plans

  • Programme DKC-KAM Doctoral, any year of study, winter semester, compulsory
  • Programme DKC-EKT Doctoral, any year of study, winter semester, compulsory-optional
  • Programme DKC-MET Doctoral, any year of study, winter semester, compulsory-optional
  • Programme DKC-SEE Doctoral, any year of study, winter semester, compulsory-optional
  • Programme DKC-TLI Doctoral, any year of study, winter semester, compulsory-optional
  • Programme DKC-TEE Doctoral, any year of study, winter semester, compulsory-optional

Type of course unit


Guided consultation

39 hours, optionally

Teacher / Lecturer