This research direction is concerned with identification and control of uncertain systems using Bayesian decision-making theory. The main advantage of this theory is consistency of the generated decision (i.e. estimates and control actions). However, solution of the implied recursive Bayesian relations is often available only approximately.
Sampling methods provide a traditional approximation methodology for Bayesian statistics. Any complex probability density function can be approximated by a set of samples generated from it. This method is computationally expensive, however research effort to increase efficiency of sampling methods and increasing performance of computers improved applicability of these methods in such a way that they bring significant improvement in many application areas and represent a strong alternative to traditional approximation methods.
Sequential Monte Carlo is a way to apply sampling methods for on-line estimation and filtering. It is an established methodology with many practical applications. The advanatage of the methodology is it universality with a possibility to tailor the algorithm for a particular problem via proposal density or Rao-Blackwellization.
A new research direction is based on application of particle filtering methods in control. This is based on duality between estimation and control. Particle filters can be applied to both dynamic programming and model predictive control formalization of the control task.
Various specific features of the approch are being elaborated under nationally funded projects listed below.
|Control and Parameter Identification of AC Electric Drives under Critical Operating Conditions||2011-2014|
|Stochastic sequential sampling for identification and control of distributed systems||2008-2010|