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• W2915611394 abstract "Sensor fault estimation and isolation is significant for an attitude control systems model of a satellite, as it works in a complex environment. The standard unscented Kalman filter algorithm may lose its accuracy when the noise is considerable. Therefore, an adaptive filtering algorithm is proposed based on the sampled-data descriptor model. The performance of the unscented Kalman filter in sensor fault estimation is improved by the adaptive algorithm depending on innovation and the measurement residual, and its convergence is guaranteed. Combining the adaptive unscented Kalman filter with the multiple-model adaptive estimation, a sensor fault isolation method is proposed. Finally, simulation examples show that this algorithm has better estimating accuracy and isolation results." @default.
• W2915611394 created "2019-03-02" @default.
• W2915611394 creator A5043819202 @default.
• W2915611394 creator A5070132430 @default.
• W2915611394 date "2019-02-25" @default.
• W2915611394 modified "2023-10-16" @default.
• W2915611394 title "Adaptive Kalman filtering for sensor fault estimation and isolation of satellite attitude control based on descriptor systems" @default.
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• W2915611394 doi "https://doi.org/10.1177/0142331218787605" @default.
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