Course detail

Control Theory

FSI-VVF Acad. year: 2020/2021 Winter semester

The course is aimed to modern methods in design and synthesis of control circuits using methods of artificial intelligence. Presented are selected methods of artificial intelligence, optimal and adaptive methods of control, fuzzy control and neural controller. Students will adopt theoretical and practical implementation of these methods and RT control. The course broadens knowledge of specific parts of applied informatics in the field of advanced control. Used is the most advanced software and hardware technology of companies B&R Automation and Mathworks (Matlab/Simulink) and substantial know-how of course's authors.

Language of instruction

Czech

Number of ECTS credits

6

Learning outcomes of the course unit

To prepare students for solving complicated tasks of automatic control by means of artificial intelligence methods.
Analysis and design of modern feedback control systems. Students will obtain the basic knowledge of optimal control, adaptive control, fuzzy control and ANN control.

Prerequisites

Fundamental concepts of the methods used in the analysis and design of linear continuous feedback control systems. Fundamental concepts of the methods used in the analysis and design of nonlinear continuous feedback control systems and discrete control systems. Essential principles of PLC systems. The differential equations of control systems, transient response, frequency analysis, stability of systems. Mathematical programming and optimization.

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. Teaching is suplemented by practical laboratory work.

Assesment methods and criteria linked to learning outcomes

In order to be awarded the course-unit credit students must prove 100% active participation in laboratory exercises and elaborate a paper on the presented themes. The exam is written and oral. In the written part a student compiles two main themes which were presented during the lectures and solves three examples. The oral part of the exam will contain discussion of tasks and possible supplementary questions.

Aims

The basic aim of the course is to provide students with the knowledge of physical principles of control, optimal control, adaptive control, fuzzy control and identification of dynamic systems.

Specification of controlled education, way of implementation and compensation for absences

Attendance and activity at the seminars are required. One absence can be compensated for by attending a seminar with another group in the same week, or by the elaboration of substitute tasks. Longer absence can be compensated for by the elaboration of compensatory tasks assigned by the tutor.

The study programmes with the given course

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

The lectures are divided into 5 topic blocks.
Block 1: Physical nature of regulation
Block 2: PID controller (continuous and discrete, andi-windup, bumpless switching, advanced structural modifications)
Block 3: Identification of dynamic systems, Adaptive control and regulation (self-adjusting controller, possibilities of artificial intelligence, recursive least squares methods, regression model, controllers based on the field placement method).
Block 4: Optimal Control and Automatic Control Algorithm Generation (Applied Grammar Evolution, Genetic Programming, Nonlinear Optimization Methods)
Block 5: Fuzzy controllers (theory of fuzzy sets, principles of inference, fuzzification and defuzzification, PI / PD / PID controllers, standardized universe forms, fuzzy supervisor, fuzzy switch, fuzzy controller with multiple inputs).

Laboratory exercise

12 hours, compulsory

Teacher / Lecturer

Syllabus

1L: Matlab / Simulink and Data Acquisition, Real-Time Toolbox, Real-Time Workshop
2-3L: Project: Automation Studio and B+R Automation (Thermal Control / Drive Control)
4-5L: Project: D-Space (Magnetic Levitation / Helicopter / Platform Stabilization)
6L: Final project presentations.

Computer-assisted exercise

14 hours, compulsory

Teacher / Lecturer

Syllabus

1C: PID controller properties, implementation methods.
2C: Optimization of PID controller parameters (classical and modern approaches).
3C: Automatic generation of control law (algorithms).
4C: Identification of dynamic systems (non-parametric methods).
5C: Identification of dynamic systems (parametric methods).
6C: Fuzzy Controller.
7C: Neural Controller.