SubjectsSubjects(version: 965)
Course, academic year 2019/2020
  
Predictive Control - N445085
Title: Prediktivní řízení
Guaranteed by: Department of Computing and Control Engineering (445)
Faculty: Faculty of Chemical Engineering
Actual: from 2019 to 2020
Semester: summer
Points: summer s.:5
E-Credits: summer s.:5
Examination process: summer s.:
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Level:  
Is provided by: M445019
Guarantor: Mareš Jan prof. Ing. Ph.D.
Hrnčiřík Pavel doc. Ing. Ph.D.
Is interchangeable with: M445019
Examination dates   Schedule   
Annotation -
Predictive control is a course which summarises advanced control techniques. Continuous or discrete mathematical model is the basics for all these methods. Introduction to diofantic equatoins is ana essential part of the lectures. The course contains also application examples in order for the students to apply the acquired knowledge to the solution of engineering problems.
Last update: TAJ445 (14.12.2013)
Literature -

R: Mareš, J. Hrnčiřík, P., Základy prediktivního řízení, VŠCHT Praha, 2012, 9788070808238

A: Balátě, J. Automatické řízení, BEN, Praha, 2004, 8073001489

Last update: TAJ445 (30.09.2013)
Requirements to the exam - Czech

Předmět je zakončen zápočtem a zkouškou, získání zápočtu je přitom podmíněno aktivní účastí na cvičení a vypracování zadaných samostatných úloh.

Last update: Hrnčiřík Pavel (04.04.2014)
Syllabus -

1) Continuous and discrete domain, difference equations, Z-transform.

2) Stability in discrete domain, delta models, discrete PID, IMC controller.

3) State space description.

4) Nonlinear and MIMO systems.

5) Adaptive control techniques.

6) Standard form of predictive control. Cost function, prediction model

7) Prediction model derivation

8) Numerical solution of control law.

9) Application example I

10) MATLAB Multi Parametric Toolbox.

11) Predictive control of MIMO systems.

12) Predictive control of nonlinear systems.

13) Application example II.

14) Application example III.

Last update: TAJ445 (22.08.2013)
Learning resources -

https://www.honeywellprocess.com/en-us/explore/products

Last update: TAJ445 (22.08.2013)
Learning outcomes -

Students will be able to:

  • use advanced and predictive control techniques (Pole Placement, Predictive Functional Control, Model Predictive Control)
  • Use optimization algorithms (linear programming, quadratic programming)
Last update: TAJ445 (22.08.2013)
Registration requirements -

Mathematics I, Control theory

Last update: TAJ445 (22.08.2013)
Teaching methods
Activity Credits Hours
Účast na přednáškách 1 28
Práce na individuálním projektu 1 28
Příprava na zkoušku a její absolvování 2 56
Účast na seminářích 1 28
5 / 5 140 / 140
Coursework assessment
Form Significance
Regular attendance 20
Report from individual projects 20
Continuous assessment of study performance and course -credit tests 20
Oral examination 40

 
VŠCHT Praha