Control Theory (FSI-VVF)

Academic year 2018/2019
Supervisor: prof. Ing. Pavel Ošmera, CSc.  
Supervising institute: ÚAI all courses guaranted by this institute
Teaching language: Czech
Aims of the course unit:
The basic aim of the course is to provide students with the knowledge
of optimal control, adaptive control, fuzzy control and artificial neural network control.
Learning outcomes and competences:
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.
Course contents:
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.
Teaching methods and criteria:
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.
Controlled participation in lessons:
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.
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  7 × 2 hrs. compulsory                  
    Labs and studios  6 × 2 hrs. compulsory                  
Course curriculum:
    Lecture Lectures are divided to 6 topic blocks:
Block 1: Technology: B&R Automation, Mathworks (Matalab/Simulink and selected toolboxes: TT, RTW, Fuzzy, ANN), dSpace and other technologies used in course.
Block 2: Adaptive control and regulation (self-tuning controller, options of artificial intelligence, recursive methods of mean square error, regression model, pole placement based controllers, delta models).
Block 3: Optimal control and auto-generation of control law (applied grammar evolution, genetic programming, methods of nonlinear optimization, HC12 algorithm)
Block 4: Fuzzy controllers (fuzzy set theory, inference principles, fuzzification and defuzzification, PI/PD/PID controllers, fuzzy supervisor, fuzzy switch, fuzzy controller with multiple inputs).
Block 5: Neuron nets in control technology (theory of selected neuron nets, neuron PID controller, controllers with model, adaptive forms, adaptive control of nonlinear systems).
Block 6: Modern trends in artificial intelligence and autonomous control. (end of course)
    Computer-assisted exercise 1C: Matlab/Simulink (variants of PID/PSD controllers, methods of tuning)
2C: Matlab/Simulink (optimal control and distribution of simulation model)
3C: Automation Studio (concept and environment for real-time implementations)
4C: Optimal control (nonlinear optimization, HC12 optimization, pole-placement)
5C: Automatic generation of control law
6C: Fuzzy controller
7C: Neuron controller
    Labs and studios 1L: Matlab/Simulink and tools Data Acquisition, Real-Time Toolbox, Real-Time Workshop (control of heat system)
2L: Automation Studio and technology B+R Automation 1/2 (control of heat system)
3L: Project: Motion control (ACOPOS, IclA, Maxon)
4L: Project: Magnetic levitation, Helicopter
5L: Project: Stabilization of platform
6L: Presentation of projects
Literature - fundamental:
1. Vegte, V.D.J.: Feedback Control Systems, Prentice-Hall, New Jersey 1990, ISBN 0-13-313651-5
2. Levine, W.S. (1996) : The Control Handbook, CRC Press, Inc., Boca Raton, Florida 1996 , ISBN 0-8493-8570-9
3. Morris,K.: Introduction to Feedback Control, Academic Press, San Diego, California 2002.
4. Franklin, G.F., Powell, J.D., Emami-Naeini, A. Feedback Control of Dynamic Systems, Prentice Hall 2002.
5. Nguyen, H.T., Prasad, N.R., Walker, C.L., Walker, E.A. A First Course in Fuzzy and Neural Control. Chapman & Hall/CRC 2002.
Literature - recommended:
1. Švarc,I.:: Automatizace-Automatické řízení, skriptum VUT FSI Brno, CERM 2002, ISBN 80-214-2087-1
2. Zelinka Ivan, Oplatková Zuzana, Šeda Miloš, Ošmera Pavel, Včelař František; Evoluční výpočetní techniky - principy a aplikace; BEN - technická literatura, Praha 2009; ISBN 978-80-7300-218-3
3. Morris,K.: Introduction to Feedback Control, Academic Press, San Diego, California 2002.
4. Nguyen, H.T., Prasad, N.R., Walker, C.L., Walker, E.A. A First Course in Fuzzy and Neural Control. Chapman & Hall/CRC 2002.
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
M2I-P full-time study M-AIŘ Applied Computer Science and Control -- Cr,Ex 6 Compulsory 2 2 W
M2I-P full-time study M-AIŘ Applied Computer Science and Control P linked to branch B-AIR Cr,Ex 6 Compulsory 2 2 W