450-4017/02 – Knowledge Based Control Systems (ZSR)
| Gurantor department | Department of Cybernetics and Biomedical Engineering | Credits | 4 |
| Subject guarantor | Ing. Zdeněk Slanina, Ph.D. | Subject version guarantor | Ing. Zdeněk Slanina, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Choice-compulsory |
| Year | 2 | Semester | winter |
| | Study language | English |
| Year of introduction | 2015/2016 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The course is aimed on fuzzy set theory, fuzzy logic, fuzzy control theory and their application in development of knowledge based fuzzy controllers.
After passing the course students have the main knowledge and skills with development, debugging and testing of fuzzy controllers in engineering.
Teaching methods
Lectures
Individual consultations
Tutorials
Experimental work in labs
Project work
Summary
The course is focused on the area of modern artificial intelligence methods application in the information and control systems. Introduces the complex systems modelling problems and linguistic describing of its behaviour using the human knowledge. Basic procedures of fuzzy mathematics and fuzzy logic are presented including the fuzzy control systems development methodology.
The subject appears to be introduction to artificial intelligence methodologies and their application inengineering..
Compulsory literature:
Recommended literature:
C. R. REEVES, J. E. ROW,. Genetic Algorithms: Principles and Perspectives. Kluwer Academic Publishers, New York, 2002.
GRAUPE,D. Principles of Artificial Neural Netvorks. World Scientific. 2013. ISBN: 978-981-4522-73-1
Way of continuous check of knowledge in the course of semester
Verification of study:
The current activity of student is done through his laboratory activities.
Conditions for credit:
Student can gain up to 40 (min 21) points from the laboratory excercises and individual semestral project.
Passing the course:
To pass the course student has to pass both of the laboratory part of the course and the final exam. The final exam consists of writing part 40 (min 10) points and oral part 20 (min 6) points. Student have to be succeed in all parts of examination.
E-learning
Other requirements
There are not defined other requirements for student
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Lectures
1. Artificial intelligence, knowledge importance, vagueness and its formalization, knowledge properties, knowledge processing, knowledge systems
2. Principles of fuzzy set theory, fuzzy sets operations, Zadeh´s extesional principle
3. Linguistic variable and its fuzzy sets reperesentation
4. Principles of fuzzy logic, fuzzy-logical operations
5. Linguistic model aproximation using fuzzy function, conditional IF-THEN rules
6. Principles of fuzzy modelling, Mamdani fuzzy models
7. Inference engines, Zadeh´s extrapolation principle
8. Takagi-Sugeno fuzzy models and their properties
9. Basic structure and paramemers of fuzzy controllers FLC, types of fuzzy controllers
10. Types of fuzzy controllers and their control rules
11. Operations of fuzzification and deffuzification
12. Fuzzy control systems stability investigation
Fuzzy controllers design
14. Fuzzy state controllers
Laboratory works
1. Fuzzy control using microprocessor NXP iMX6
2. Fuzzy control using PLC Siemens, B&R
Computer works
1. Principles of programme system Matlab, FuzzTool Box (FTB), programme tool Simulink
2. Fuzzy sets - editing, parameters of fuzzy sets approximation, fuzzy sets operations (FTB), examples - Zadeh´s extesional principle
3. Linguistic values of linguistic variables editing (FTB), teaching programme Fuzzy Logic Motorola
4. Effect of type of fuzzy logic function on results of fuzzy sets operations (FTB)
5. Example of linguistic model approximation using a fuzzy function (FTB)
6. Mamdani models editing (FTB)
7. Examples of Zadeh´s intrpolation principle utilization (FTB)
8. Takagi-Sugeno model editing, non-linear function approximation (FTB)
9. Takagi-Sugeno model identification using ANFIS procedure (FTB)
10. Fuzzy controller Mamdani, teaching programme FL Mortorola
11. Fuzzy controller Mamdani, design and debugging
12. Fuzzy controller Takagi-Sugeno, design and debugging
Semestral project
Design a fuzzy controller Mamdani with sheduled system using programme environment Matlab and Simulink !
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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