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In this work, we consider an optimal control problem for Networked Control Systems (NCS), where the loop is closed via an unreliable network. In order to inform the controller about the fates of the control packets, the actuator sends acknowledgements to the controller. In this work, different types of acknowledgements are considered. Since all packets are send over a network, we consequently assume,...
Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filters (extended and unscented) and particle filters. Key components of each Bayes filter are probabilistic prediction and observation models. Recently, Gaussian processes have been introduced as a non-parametric technique for learning such...
This paper considers the use of non-parametric system models for sequential state estimation. In particular, motion and observation models are learned from training examples using Gaussian process (GP) regression. The state estimator is an unscented Kalman filter (UKF). The resulting GP-UKF algorithm has a number of advantages over standard (parametric) UKFs. These include the ability to estimate...
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