Course detail

Optimalization of controllers

FEKT-MOPRAcad. year: 2011/2012

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.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

prof. Ing. Petr Pivoňka, CSc.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

Become familiar with different approaches used by theoretical and especially practical solution in modern control theory.

Prerequisites

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

Co-requisites

Not applicable.

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.

Assesment methods and criteria linked to learning outcomes

Lesson. Project max. 30 points.
Examination. Max. 70 points.

Course curriculum

Lecture:
Physical background of control.
Design and realisation of continuous PID controllers, bump-less swithing, anti-windup.
Different types of PID controllers, realisation, setting of parameters, comparison, anti-windup and switching between algorithms.
Design and realisation of discrete analogy of continuous PID algorithms.
Philosophy of the process identification and design of controller's algorithm.
Optimum settings of controller's parameters, adaptive controllers, self tuning controllers, specific problems of adaptive control.
Artificial intelligence in controls algorithms. Real-time operating system. Programming in real-time, synchronisation methods, Software for computer control. Implementation of heterogenous algorithms in real-time.
A/D and D/A converters, binary outputs and inputs, galvanic isolation, isolations amplifiers.
Sensors and normalisation circuits, influence of disturbances.
Digital and continuous filtration.
Computer exercise:
Introductory lesson (organisation, instructions, safety). Demonstration.
Programing S-function, realization of a discrette filter.
Realisation of continuous PID controller, verification on the simulated model.
Discrete analogies of continuous PID algorithms, verification on the simulated model.
Simulation in real-time in the program MATLAB.
Verification of PID controllers on physical models. Anti-windup.
Various PID controllers, switching between algorithms.
Submission of projects.
Control of physical models.
Control of heating tunnel.
Contol of synchronous motors.
Presentation of protocols, credit.

Work placements

Not applicable.

Aims

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.

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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme EEKR-M Master's

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

  • Programme EEKR-CZV lifelong learning

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

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

prof. Ing. Petr Pivoňka, CSc.

Syllabus

Physical background of control
Design and realisation of PID controllers like ground controller for comparison
Methods of adaptive control, ARX identification
Self tuning controller
Optimal control
State controller
Discrete quadratic optimal control
Continuous quadratic optimal control, the properties of LQ controllers
Fuzzy controllers
Artificial neural networks
Identification by neural networks
Adaptive optimal controller with identification using neural networks
Neural controllers
Predictive and feedback control strategies
Continuous and digital filters

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer

Ing. Jakub Dokoupil, Ph.D.
Ing. Martin Dvořáček
Ing. Vlastimil Lorenc

Syllabus

Assignment of laboratory tasks
Real-time control with MATLAB/Simulink
Assignment with S-function in MATLAB
Discrette PID controllers and its variants
Identification by ARX model
Design of STC controller
Design of LQ controller
Solution of LQ controller
Verification of neural networks in identification and control
Validation of predictive LQ controller
Design and solution discrete filters
Design and verification of Kalman filter
Evaluation of results, credit.