| Course Unit Code | 450-8707/01 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | Second Year |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| SLA77 | Ing. Zdeněk Slanina, Ph.D. |
| Summary |
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| The course is based on selected methods of artificial intelligence applied in the synthesis of non-conventional controllers of mechatronic systems. Approaches are used Mamdani and Takagi-Sugeno fuzzy controllers and fuzzy-neural controllers. To adapt and optimize the structure and controller parameters are used genetic algorithms. Computer simulations are carried out in Matlab-Simulink. |
| Learning Outcomes of the Course Unit |
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| To acquaint students with modern and efficient control methods, which use non-numeric descriptions of the control law in the science of artificial intelligence. Students will acquire knowledge of fuzzy set mathematics and fuzzy logic, artificial neural networks and evolutionary algorithms. Acquires skills in the design, debugging and application of fuzzy controllers, neural controllers and special intelligent controllers and advanced optimization using genetic algorithms. For the controller design and simulation is used Matlab-Simulink. |
| Course Contents |
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1. Artificial intelligence principles, non-conventional description of complex systems
2. Principles and approaches of knowledge based non-numerical modelling and control
3. Principles of fuzzy sets and fuzzy linguistic logic
4. Rule based modelling, aproximative reasoning and results interpretation, expert systems
5. Fuzzy logic based controller analysis and synthesis, structures and their properties in comparison with conventional controllers
6. Fuzzy control and its application in mechatronics
7. Artificial neural networks, structures and self-learning principles
8. Neural controller synthesis an their properties discussion
9. Combined fuzzy-neural controllers
10. Evolution and genetic algorithms in tasks of optimization of structures and parameters of conventional controllers
11. Genetic algorithms application issues, advanced genetic algorithms and their properties
12. Structural and parameter optimization of non-conventional controllers using genetic algorithms
13. Computational intelligence, integrated fuzzy-neuro-genetic structures in control
14. Intelligent controllers, their structure and application
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| Recommended or Required Reading |
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| Required Reading: |
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GANG,F. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach. CRC Press, 2010. ISBN 9781420092646
MATA,F., MARICHAL,G.N., JIMNEZ,E. Fuzzy Modeling and Control: Theory and Applications. Atlantis Publishing Corporation, 2014 ISBN 9462390819.
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POKORNÝ,M.,SROVNAL,V. Znalostní systémy řízení – Učební text a návody do cvičení. CZ.1.07/2.2.00/15.0113. VŠB - Technická univerzita Ostrava. Ostrava. 2012
JURA, P. Základy fuzzy logiky pro řízení a modelování. Brno: Nakladatelství VUTIUM, 2003, ISBN 80-214-2261-0. |
| Recommended Reading: |
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RUSSEL,S., NORVIG,P.: Artificial Intelligence, Prentice-Hall, Inc., 2003, ISBN 0-13-080302-2
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VESELÝ,A. Úvod do umělé inteligence. ČZU Praha, 2005. ISBN 80-213-1361-7
NOVÁK,V.,KNYBEL,J. Fuzzy modelování. Ostravská univerzita v Ostravě, Přírodovědecká fakulta. Ostrava 2005.
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| Planned learning activities and teaching methods |
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| Lectures, Tutorials, Project work |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 40 | 21 |
| Examination | Examination | 60 (60) | 30 |
| Písemná zkouška | Written examination | 40 | 10 |
| Ústní zkouška | Oral examination | 20 | 6 |