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Robust Filtering for State and Fault Estimation of Linear Stochastic Systems with Unknown Disturbance
This paper presents a new robust filter structure to solve the simultaneous state and fault estimation problem of linear stochastic discrete-time systems with unknown disturbance. The method is based on the assumption that the fault and the unknown disturbance affect both the system state and the ou...
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Published in: | Mathematical problems in engineering 2010, Vol.2010 (2010), p.1-24 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Online Access: | Get full text |
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Summary: | This paper presents a new robust filter structure to solve the simultaneous state and fault estimation problem of linear stochastic discrete-time systems with unknown disturbance. The method is based on the assumption that the fault and the unknown disturbance affect both the system state and the output, and no prior knowledge about their dynamical evolution is available. By making use of an optimal three-stage Kalman filtering method, an augmented fault and unknown disturbance models, an augmented robust three-stage Kalman filter (ARThSKF) is developed. The unbiasedness conditions and minimum-variance property of the proposed filter are provided. An illustrative example is given to apply this filter and to compare it with the existing literature results. |
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ISSN: | 1024-123X 1563-5147 |