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Summary:
Abstract--In this paper Model Based Networked Control
Systems (MB-NCS) are considered and on-line identification of
system parameters in state space representation is used to
upgrade the model and the controller of the system. The
updated model is used to control the real system when feedback
information is unavailable. The Extended Kalman Filter (EKF)
is analyzed in the context of parameter identification and
implemented in the MB-NCS framework. Emphasis is placed on
global asymptotic estimators for the case when sensors provide
noiseless measurements of the state of a linear system; it can be
shown that the identification of parameters in this case is a
linear problem, in contrast to the nonlinear combined state-
parameter estimation problem. We propose new estimation
models that offer better convergence properties than the EKF
in this case. This estimation strategy is also applied to the MB-
NCS framework resulting in a better usage of the network by
allowing longer intervals without need for a measurement
update.
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