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.
The subject knowledge on the Bachelor´s degree level is requested.
Recommended optional programme components
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
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)
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
Digital and continuous filtration
Optimal filtration (Kalman filter)
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.
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