Course detail

Optimalization of Controllers

FEKT-MPC-OPRAcad. year: 2020/2021

The course is focused on modern methods of analysis and design of control systems. In the centre of interest are adaptive systems,
design of optimal control, predictive controllers and using artificial intelligence in control algorithms.

Learning outcomes of the course unit

Students are able to design a complex control system and transfer it into a real technological process.

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Pivoňka, P.: Optimalizace regulátorů. Skriptum, VUT FEKT, Brno, 2005. (CS)
Havlena, V.-Štecha, J.: Moderní teorie řízení, ČVUT Praha, 2000 (CS)
Camacho, E. at all: Advanced control, Springer, 1997 (EN)
Astrom, K.,J.,-WittenmarkB.:Adaptive Control, Addison Wesley, 1995 (EN)

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations. Materials for lectures and exercises are available for students from web pages of the course. Students have to write a single project/assignment during the course.

Assesment methods and criteria linked to learning outcomes

Project realization: Max. 30 points.
Combined exam includes a written part and an oral examination: Max. 70 points.

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

Lecture:
Physical background of control.
Discrete analogy of continuous PID algorithms and their variants as a basic reference for comparing the regulators.
Self-tuning Controller (STC)
State controller
Discrete quadratic optimal control LQG methods for design controller
Artificial intelligence in controls algorithms. Fuzzy Logik, fuzzy controllers
Artificial neural networks, learning methods
Adaptive optimal controller with identification by neural networks (quantisation effect).
Control algorithms with using of neural networks
Predictive control
Digital and continuous filtration
Optimal filtration (Kalman filter)

Computer exercise:
Introductory lesson (organisation, instructions, safety). Demonstration. Introduction to Automation Studio for direct implementation of real-time control algorithms in MATLAB/Simulink- PLC B&R-physical models.
Programing S-function in MATLAB.
Realisation of discrete variants of continuous PID controllers, optimizing of setting parameters.
Identification of parameters ARX model in real time.
Submission of projects.
Realisation of self-tuning controller
A proposal of LQ controller
Methods of solving algorithms LQ controllers
Realisation of fuzzy controller
Control of physical models.
Control of heating tunnel.
Control of synchronous motors.
Presentation of protocols, credit.

Aims

Familiarize students with modern approaches from the field of automatic control, signal processing and decision-making. Students adopt the methodology of the optimal controller design, adaptive controller; build models and perform diagnosis from the experimentally measured data.

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

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Classification of course in study plans

  • Programme MPC-KAM Master's, 1. year of study, winter semester, 6 credits, compulsory-optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Fundamentals seminar

26 hours, compulsory

Teacher / Lecturer