Subject description - AE3M35OFD

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AE3M35OFD Estimation, filtering and detection Extent of teaching:3P+1C
Guarantors:  Roles:PO,V Language of
teaching:
EN
Teachers:  Completion:Z,ZK
Responsible Department:13135 Credits:6 Semester:Z

Anotation:

This course will cover description of the uincertainty of hidden variables (parameters and state of a dynamic system) using the probability language and methods for their estimation. Based on bayesian prblem formulation principles of rational behsavour under uncertainty will be analysed and used to develp algorithms for estimation of parameters of ARX models and Kalman filtering including the extensions. We will demonstrate numerically robust implementation of the algorithms applicable in real life problems for the areas of industrial process control, robotics and avionics. We will extend the methods for linear gaussian systems to a more generic problems using Monte Calro approach. The course will also cover multimodel approach and its use for the fault detection and isolation and introduction to adaptive control.

Course outlines:

1. Problem formulation, estimation methods
2. Bayesian approach to uncertainty description
3. Dynamic system model, probabilistic state definition
4. Identification of ARX model parameters
5. Tracking of time varuing parameters, forgetting, role of prior informaiton.
6. Numerically robust implementaiton for real time parameter tracking
7. Stochastic system, Kalman filter.
8. Kalman filtr for colour noise, extended Kalman filter, adaptive Kalman filter.
9. Stochastic dynamic programming, certainty equivalence principle.
10. Adaptive control, cautious and certainty equivalent strategies, dual control.
11. Probabilistic method for fault detection and isolation
12. Utilizaiton of multiple models
13. Nonlinear estimation, local approximation
14. Global aproximation, Monte Carlo Kalman filter

Exercises outline:

Laboratory covers work on individual assignments/projects. .

Literature:

Literatura:
1. Frank L. Lewis, Lihua Xie, and Dan Popa : Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, CRC Press, 2005

Requirements:

Webpage:

http://dce.fel.cvut.cz/studium/estimation-filtering-and-detection

Subject is included into these academic programs:

Program Branch Role Recommended semester
MEKME1 Wireless Communication V 3
MEKME5 Systems of Communication V 3
MEKME4 Networks of Electronic Communication V 3
MEKME3 Electronics V 3
MEKME2 Multimedia Technology V 3
MEKYR3 Systems and Control PO 3
MEOI1 Artificial Intelligence V 3
MEOI5NEW Software Engineering V 3
MEOI5 Software Engineering V 3
MEOI4 Computer Graphics and Interaction V 3
MEOI3 Computer Vision and Image Processing V 3
MEOI2 Computer Engineering V 3
MEEEM1 Technological Systems V 3
MEEEM5 Economy and Management of Electrical Engineering V 3
MEEEM4 Economy and Management of Power Engineering V 3
MEEEM3 Electrical Power Engineering V 3
MEEEM2 Electrical Machines, Apparatus and Drives V 3


Page updated 13.12.2019 17:52:09, semester: Z,L/2020-1, L/2018-9, Z,L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)