Models and Algorithms for Monitoring and Control

Course guarantor:

Tomáš Vyhlídal

Lecturer:

Tomáš Vyhlídal

Tutor:

Tomáš Vyhlídal

Supervisor:

Department of Instrumentation and Control Engineering

Synopsis:

The subject provides background in methods of using algorithms and mathematical models for design of control and estimation of internal states of systems on which routine methods fail. For successful solution of these problems, model based methods and methods based on advanced processing and evaluation of measured data are outlined. Next to the analysis and control synthesis based on state-space and algebraic models, optimization methods are introduced for solving design tasks for systems with constraints. Attention is also paid to the design of control and monitoring systems using methods of artificial intelligence, neural networks in particular.

Requirements:

Syllabus of lectures:

Stability and dynamic properties of systems, spectral and frequency domain methods

Stability analysis of nonlinear systems in the time domain

System controllability, state feedback, pole placement design for single and multi-input systems

System observability, state observer design and its application in the control and fault detection

Design of affine parameterization controller, Youla-Kučera parameterization

Internal model control

Linear and quadratic programming, algorithms and tools for nonlinear optimization

Application of optimization methods for control synthesis under constraints

Neural Networks architecture overview.

Optimization gradient methods for learning neural networks, activation function. Software and hardware tools for neural network learning and implementation.

Categorization and classification of data, time series format and their pre-processing

Neural networks in industrial applications

Syllabus of tutorials:

Study Objective:

Study materials:

Ogata K.: Modern Control Engineering. Prentice-Hall, Inc. Englewood Cliffs,N. Jersey, 1990

Goodwin G.C., Graebe S.F. and Salgado M.E.: Control System Design Prentic-Hall, Inc., Upper Saddle River, New Jersey, 2001

Goodwin, Graham, María M. Seron, and José A. De Doná. Constrained control and estimation: an optimisation approach. Springer Science & Business Media, 2006

I. Goodfellow, Y. Bengio, a A. Courville, Deep Learning. MIT Press, 2016

Study material available at https://moodle-vyuka.cvut.cz/

Note:

Time-table for winter semester 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans: