Course detail
Optimalization of Controllers
FEKT-MPC-OPRAcad. year: 2022/2023
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
6
Mode of study
Not applicable.
Guarantor
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.
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.
Combined exam includes a written part and an oral 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.
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
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.
Recommended optional programme components
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
Not applicable.
Recommended reading
Not applicable.
Elearning
eLearning: currently opened course
Classification of course in study plans
- Programme MPC-KAM Master's 1 year of study, winter semester, compulsory-optional
Type of course unit
Lecture
26 hod., optionally
Teacher / Lecturer
Syllabus
Discrete variants of a PID controller. Some controller modifications designed for implementation in real computers and their tuning will be presented.
The basic principles of optimization theory: necessary and sufficient conditions of minima, convex analysis, solving optimization tasks with both equality and inequality constraints (Karush–Kuhn–Tucker conditions), solving a non-linear problems by globally convergent algorithms, introduction into the theory of probability.
Formulating the task of optimal control. The implementation of the optimal state-space controller will be discussed.
Formulating the task of optimal control. The implementation of the optimal state-space controller will be discussed - continuation.
Formulating the task of predictive control. The implementation of the predictive controller will be discussed.
Estimation of linear regression model parameters. Some practical aspects such as the model structure selection, numerical filters, estimation in the close-loop feedback system will be discussed.
Tracking of time-varying model parameters by adaptive estimation algorithms.
Introduction of the Kalman filter and its deployment in the tasks of the state electrical drive estimation.
Fault detection and isolation based on the information carried by measured data.
Nonlinear parametric estimation and state filtering.
Data-driven model merging strategy making the system predictor more refined will be shown. The use of the bank of models for control will be studied.
Optimal decision-making in the discrete event systems.
Review of the curriculum.
The basic principles of optimization theory: necessary and sufficient conditions of minima, convex analysis, solving optimization tasks with both equality and inequality constraints (Karush–Kuhn–Tucker conditions), solving a non-linear problems by globally convergent algorithms, introduction into the theory of probability.
Formulating the task of optimal control. The implementation of the optimal state-space controller will be discussed.
Formulating the task of optimal control. The implementation of the optimal state-space controller will be discussed - continuation.
Formulating the task of predictive control. The implementation of the predictive controller will be discussed.
Estimation of linear regression model parameters. Some practical aspects such as the model structure selection, numerical filters, estimation in the close-loop feedback system will be discussed.
Tracking of time-varying model parameters by adaptive estimation algorithms.
Introduction of the Kalman filter and its deployment in the tasks of the state electrical drive estimation.
Fault detection and isolation based on the information carried by measured data.
Nonlinear parametric estimation and state filtering.
Data-driven model merging strategy making the system predictor more refined will be shown. The use of the bank of models for control will be studied.
Optimal decision-making in the discrete event systems.
Review of the curriculum.
Fundamentals seminar
26 hod., compulsory
Teacher / Lecturer
Syllabus
Discrete PID controller.
MATLAB/Simulink – PLC B&R.
Optimal state-space controller
Optimal state-space controller – continuation
Predictive controller.
Recursive leas-squares method with a square-root filter.
Adaptive variants of the recursive-least squares methods.
Kalman filter as the state estimator.
Working on a project.
Working on a project.
Working on a project.
Working on a project.
Exercises evaluation.
MATLAB/Simulink – PLC B&R.
Optimal state-space controller
Optimal state-space controller – continuation
Predictive controller.
Recursive leas-squares method with a square-root filter.
Adaptive variants of the recursive-least squares methods.
Kalman filter as the state estimator.
Working on a project.
Working on a project.
Working on a project.
Working on a project.
Exercises evaluation.
Elearning
eLearning: currently opened course