Intelligent Control Systems
FSI-RIRAcad. year: 2017/2018
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.
Learning outcomes of the course unit
Students will learn basics of these methodologies.
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.
Recommended optional programme components
Recommended or required reading
Hangos, K. M.: Intelligent Control Systems
Planned learning activities and teaching methods
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes
Course-unit credit is conferred on the base of active participation assessment in seminars and results of written test with four questions. The evaluation is fully in competence of a tutor according to the valid directives of BUT.
Language of instruction
The goal is to master the basic design methodologies of state controllers and fuzzy controllers.
Specification of controlled education, way of implementation and compensation for absences
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.
Type of course unit
26 hours, optionally
Teacher / Lecturer
1. 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.
11. Case study of technical problem.
12. Rule base creation of fuzzy controller by general metarules.
13. Case study of technical problem.
26 hours, compulsory
Teacher / Lecturer
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 (intuitively).
11. Case study: fuzzy controller III (by empiric knowledge).
12. Case study: fuzzy controller IV (by application metarules).
13. Accreditation test.