Course detail
Intelligent Control Systems
FSI-RIR Acad. year: 2023/2024 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
Teacher / Lecturer
Syllabus
1. Fundamentals of control theory and an overview of advanced control design methods. Inner and outer dynamic system description in continuous and discrete domain.
2. State feedback control.
3. State controller design with disturbance compensation.
4. State controller design with state estimator.
5. State control design generalization, appropriate structures for state control design.
6. Case study of technical problem.
7. Fuzzy sets, linguistic variable.
8. Inference rules, fuzzification, defuzzification.
9. Rule systems, fuzzy controllers.
10. Rule base creation of fuzzy controller by empiric knowledge on system behavior. Rule base creation of fuzzy controller by general metarules.
11. Basics of controller design using evolutionary methods.
12. Linearization methods in control problems.
13. Overview of system identification methods. Case study of technical problem.
Computer-assisted exercise
26 hours, compulsory
Teacher / Lecturer
Syllabus
1. Basics of work with Matlab/Simulink/Control System Toolbox.
2. Dynamic properties of the system.
3. Case study: controller classic solution.
4. Case study: state controller I.
5. Case study: state controller II (with disturbance compensation).
6. Case study: state controller III (with state estimator).
7. Case study: state controller IV (with state estimator and disturbance compensation).
8. Basics of work with Matlab/Simulink/Fuzzy Logic Toolbox.
9. Case study: fuzzy controller I (intuitively).
10. Case study: fuzzy controller II (by empiric knowledge and by meta rules).
11. Controller design using evolutionary methods.
12. Case study: linearization and identification of systems.
13. Accreditation test.