Event Triggered Cubature Kalman Filter

• Author / Creator

  Kooshkbaghi,Marzieh

• 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

  • event-triggered
  • packet dropout
  • delay
  • state estimation
  • Cubature Kalman filter

• Graduation date

  Spring 2020

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-zqpp-6t41

• License

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