Course detail
Intelligent Control Systems
FSI-RIR Acad. year: 2025/2026 Winter semester
The course gives a brief overview of selected parts of control theory with accent on their practical application. An applicability of introduced resources to tasks of technical systems and processes control is discussed.
Language of instruction
Czech
Number of ECTS credits
5
Supervisor
Entry knowledge
The orientation in basic knowledge of dynamic systems and classic controller design methodology is supposed. The orientation in control theory and fuzzy logic is suggested.
Rules for evaluation and completion of the course
Course-unit credit is conferred on the base of active participation assessment in seminars and results of test in the form of application of the methods learned to the assigned problem. The evaluation is fully in competence of a tutor according to the valid directives of BUT.
The attendance at lectures is recommended while at seminars it is obligatory. Education runs according to week schedules. The form of compensation of missed seminars is fully in the competence of a tutor.
Aims
The aim is to master the basics of state controller design methodology, fuzzy controllers, controller design using evolutionary methods, system identification and model linearization.
Students will learn the basics of controller design methods using advanced methods.
The study programmes with the given course
Programme B-MET-P: Mechatronics, Bachelor's, compulsory
Type of course unit
Lecture
26 hours, optionally
Syllabus
1. Fundamentals of control theory and an overview of advanced control design methods. External and internal description of a dynamic system in continuous and discrete domains.
2. State feedback control. Controllability. Observability. Continuous and discrete representations.
3. State feedback controller design, effect of disturbance.
4. Concept and design of state observers. Pole-placement method.
5. Generalization of state control design, suitable structures for state control design.
6. Example of a solution to a technical control problem.
7. LQR and LQG controllers.
8. Model Predictive Control
9. Basics of controller design using evolutionary methods.
10. Fuzzy sets, linguistic variables, inference rules, fuzzification, defuzzification.
11. Rule systems, fuzzy controllers, Creating rule base of fuzzy controller using empirical knowledge of system behavior. Formation of fuzzy controller rule base using general meta-rules.
12. Overview of system identification methods. Methods of linearization in control problems.
13. Case study.
Computer-assisted exercise
26 hours, compulsory
Syllabus
1. Basics of working with Matlab/Simulink/Control System Toolbox.
2. Dynamic properties of the system, stability. System properties. PID controller.
3. Example of solution: state controller I.
4. Example of solution: state controller II.
5. Example solution: state controller III
6. Example solution: state controller IV
7. LQR control
8. Solution example: MPC I
9. Example of solution: MPC II
10. Controller design using genetic algorithm.
11. Fundamentals of working with Matlab/Simulink/Fuzzy Logic Toolbox.
12. Example solution: fuzzy controller
13. Example solution: linearization and identification of systems, assignment for credit