Course detail

Selected Chaps From Automatic Control

FEKT-DKA-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

English

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

prof. Ing. Pavel Václavek, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

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

Prerequisites

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

Co-requisites

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.

Aims

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

Goodwin G.C., Seron M.M. , Doná J.A. Constrained Control and Estimation, Springer, 2005 (EN)
Gu D.-W., Petkov P. H.: Robust Control Design with MATLAB, Springer, 2013 (EN)
Hermann R. ,Krener A., Nonlinear controllability and observability, IEEE Transactions on Automatic Control, vol. 22, no. 5, pp. 728–740, 1977 (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)

Classification of course in study plans

  • Programme DKA-EKT Doctoral 0 year of study, winter semester, compulsory-optional
  • Programme DKA-KAM Doctoral 0 year of study, winter semester, compulsory
  • Programme DKA-MET Doctoral 0 year of study, winter semester, compulsory-optional
  • Programme DKA-SEE Doctoral 0 year of study, winter semester, compulsory-optional
  • Programme DKA-TEE Doctoral 0 year of study, winter semester, compulsory-optional
  • Programme DKA-TLI Doctoral 0 year of study, winter semester, compulsory-optional

Type of course unit

 

Guided consultation

39 hod., optionally

Teacher / Lecturer

prof. Ing. Pavel Václavek, Ph.D.

Syllabus

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