26 hod., optionally

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.