Course detail
Optimalization of Controllers
FEKT-MPC-OPRAcad. year: 2023/2024
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
Entry knowledge
Rules for evaluation and completion of the course
Combined exam includes a written part and an oral examination: Max. 70 points.
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.
Aims
Students are able to design a complex control system and transfer it into a real technological process.
Study aids
Prerequisites and corequisites
Basic literature
Recommended reading
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.
Laboratory exercise
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.