Course detail

Optimalization of controllers

FEKT-NOPRAcad. year: 2012/2013

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

English

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

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.
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.

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 EECC-MN Master's

    branch MN-KAM , 1 year of study, winter semester, elective specialised

Type of course unit

 

Lecture

26 hod., 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
Discrete quadratic optimal control
Continuous quadratic optimal control, the properties of LQ controllers
Continuous and digital filters
Optimal filtration of the system (Kalman filter)
Artificial neural networks
Identification by neural networks
Adaptive optimal controller with identification using neural networks
Neural controllers
Predictive and feedback control strategies

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

prof. Ing. Petr Pivoňka, CSc.

Syllabus

Assignment of laboratory tasks,
overview of used MATLAB toolboxes.
Assignment with S-function in MATLAB
Identification by ARX model
Design of LQ controller
Solution of LQ controller
Design and solution discrete filters
Design and verification of Kalman filter
Verification of neural networks in identification and control
Validation of predictive LQ controller
Evaluation of results, credit.