Course detail

Optimalization of controllers

FEKT-MOPRAcad. year: 2015/2016

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

Become familiar with different approaches used by theoretical and especially practical solution in modern control theory. The student of the course can handle implementation of discrete variants of PID controllers with two degrees of freedom, adaptive controllers, optimal controllers, fuzzy controllers, and the use of neural networks in control algorithms. Also he is able to optimize their parameters settings.


The subject knowledge on the Bachelor´s degree level is requested in BC-AMT Automation and Measurement.


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

Techning methods include lectures, computer laboratories and practical laboratories. Course is taking advantage of e-learning (Moodle) system. Students have to write a single project/assignment during the course.

Assesment methods and criteria linked to learning outcomes

Lesson: Max. 30 points.
Examination: Max. 70 points.
Combined test -written part and oral evaluations written processing. Max. 70 points

Language of instruction


Work placements

Not applicable.

Course curriculum

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.


The aim of this subject is to formulate engineering problem as an optimization task, to find a solution and correctly interpret formulated problem. This process will be outlined using the classical and modern methods which are employed in the theory of automatic control. The basis for comparison are the discrete variants of PID controllers and their optimal settings of parameters. Modern methods represent adaptive, optimal and predictive controllers and the methodology for their implementation and settings. Artificial intelligence methods include fuzzy controllers and neural networks and their implementation methodology of settings his parameters.

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

Laboratory exercises are mandatory, properly excusable exercises can be replaced in consultation with lecturer.

Classification of course in study plans

  • Programme EEKR-M Master's

    branch M-KAM , 1. year of study, winter semester, 5 credits, optional specialized

  • Programme EEKR-M1 Master's

    branch M1-KAM , 1. year of study, winter semester, 5 credits, optional specialized

  • Programme EEKR-CZV lifelong learning

    branch ET-CZV , 1. year of study, winter semester, 5 credits, optional specialized

Type of course unit



26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer