Robust Estimation for Discrete Time-Varying Uncertain Systems with Observation Losses in Networked Control System

Abstract:

In this paper, we deal with the robust minimum variance filtering problem for discrete time-varying systems with observation losses. The system under consideration is subjected to time-varying norm-bounded parameter uncertainties in both the state and output matrices, and the observation losses are described by a Bernoulli process with a known probability. An upper bound on the variance of the state estimation error is first found under certain probability of missing observations and admissible parameter uncertainties. Then, a robust filter is derived by minimizing the prescribed upper bound in the sense of the matrix norm. It is shown that the desired filter can be obtained in terms of the solutions to two discrete Riccati difference equations.

• Periodical: Applied Mechanics and Materials (Volumes 336-338)
• Pages: 628-632
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.336-338.628
• Online since: July 2013
• Authors: Bao Feng Wang
• Keywords: Kalman Filter (KF), Norm-Bounded Uncertainty, Observations Loss, Robust Filter

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