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In this paper, a minimum entropy filter is presented for estimating states in networked control systems with multiple-packet transmission mechanism and non-Gaussian time-delay and noises. The filter is designed for nonlinear NCSs via information theoretic learning approach based on stochastic gradient algorithm. A numerical example is given to illustrate the effectiveness of the proposed scheme.
Minimum variance control is an established method in control of systems corrupted by noise. In these cases, as it is not possible to directly control the actual value of the system variables, one aims to reduce the variations instead. However, when the system noises are non-Gaussian, this approach fails because non-Gaussian noise cannot be characterised by simple measures such as variance. In these...
In order to measure the uncertainty of the stochastic systems subjected to arbitrary noise disturbance instead of Gaussian white noise, the minimum entropy control of tracking errors for dynamic stochastic systems is presented in this paper. Different from conventional hypothesis, it is assumed that the system output and noise obey multi-to-one mapping, which is more general in the practical application...
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