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Institute of Information Theory and Automation
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BSc./Mgr. Topic: Estimation and control under bounded uncertainty (Kuklišová Pavelková)
Type of Work:
bachelor
diploma
Supervisor:
Affiliation/Phone:
266 052 337
Theses will focus on the parameter and/or state estimation of stochastic models where the noise is described by the probability distribution with a bounded support. These models will be used for prediction and model-based control.
The mentioned models are suitable for a description of real systems where some of the involved quantities are physically bounded, eg. inherent nonnegativity, maximal allowed speed etc.
There is a menu of varying options in this topic such as comparing and testing different approaches to the bounded quantities modelling, designing estimation algorithms or utilizing the bounded state estimators in model predictive control.
Algorithms will be created in Matlab or C++.
The mentioned models are suitable for a description of real systems where some of the involved quantities are physically bounded, eg. inherent nonnegativity, maximal allowed speed etc.
There is a menu of varying options in this topic such as comparing and testing different approaches to the bounded quantities modelling, designing estimation algorithms or utilizing the bounded state estimators in model predictive control.
Algorithms will be created in Matlab or C++.
Bibliography:
[1] A. d'Onofrio, Bounded noises in physics, biology, and engineering. New York, Springer, 2013
[2] M. Kárný etal.: Optimized Bayesian Dynamic Advising: Theory and Algorithms, Springer, 2006
[3] M. S. Grewal, A. P. Andrews: Kalman Filtering - Theory and Practice Using MATLAB, 2008, Wiley-IEEE Press
[4] G. C. Goodwin, M. M. Seron and J. A. De Dona: Constrained Control and Estimation - An Optimisation Approach, Springer, 2005
[5] S. Kotz, J. R. Van Dorp: Beyond Beta - Other Continuous Families of Distributions with Bounded Support and Applications, World Scientific Publishing, 2004
[6] Další literatura dle konkrétního zaměření práce