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
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Combined exam includes a written part and an oral examination: Max. 70 points.
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.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Elearning
Classification of course in study plans
- Programme MPC-KAM Master's 1 year of study, winter semester, compulsory-optional
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
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
Teacher / Lecturer
Syllabus
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