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Event Triggered Cubature Kalman Filter
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- Author / Creator
- Kooshkbaghi,Marzieh
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The Event-triggered state estimation problem has been at the forefront of systems research
for several decades and has seen multiple successful applications in diverse areas such as
signal processing, target tracking, and navigation systems. Event-triggered state estimation
offers a promising solution to data traffic congestion, in which information between sensors
and estimators, takes place aperiodically in an event-based manner. In this research, we
tackle some practical problems encountered in this field and endeavor to improve the state
of the art.
In the first part, we develop the necessary theory to develop a discrete-time event-triggered
cubature Kalman filter for nonlinear systems with noisy measurements. We show
that the proposed filter offers excellent performance in the state estimation of the high
dimensional nonlinear systems compared to the other previously proposed nonlinear filters
which typically suffer from possible divergence, or curse of dimensionality. In addition,
the proposed filter has bounded state estimation error while reducing the communication
burden.
In the next part of this research, we study the effect of the packet dropout in the
transmission of information on the state estimator performance. Packet dropouts are caused
by imperfect communication channels, and are therefore unavoidable when information is
received by the filter via a communication network. We first develop the nonlinear filter to
reduce the estimation error. Then we show that if the packet arrival rate is lower bounded,
then the error covariance matrix is bounded. In addition, by properly tuning the value of
the event-triggered threshold, one can guarantee the boundedness of the estimation error.
Then, we consider the effect of transmission delay in the triggered measurement from
the sensors to the remote nonlinear estimator. We first discuss the difficulties involved in
dealing with time-delays in the context of state estimation and formulate the need for a new
algorithm. Then we develop a proper nonlinear filter and show that by using the proposed
event-triggered cubature Kalman filter, accurate estimates of the states can be achieved
despite time delays, while reducing transmission of information between system and the
filter. To show the advantages of the proposed filters, we evaluate the performance of the
proposed filters applied to a synchronous machine.
In the next part, we turn our attention to the developing of a nonlinear filter for more
realistic scenario. We develop a nonlinear event-triggered adaptive filter for high dimensional
nonlinear systems. The adaptive mechanism is important whenever there are sudden
changes in the system states. We show that although the upper bound of the error covariance
matrix and the estimation error could be affected, one can guarantee the convergence
and the boundedness of the state estimation error by properly designing the nonlinear filter
and tuning the event-triggered threshold value and the rate of the packet arrival.
Finally, the effect of the transmission delay and the sudden changes of the states are
considered and we develop a nonlinear filter for high dimensional nonlinear systems which
could tackle these issues while reducing the amount of data transferring between the sensors
and the remote state estimator. -
- Subjects / Keywords
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- Graduation date
- Spring 2020
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- Type of Item
- Thesis
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- Degree
- Doctor of Philosophy
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- License
- Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.