Adaptive Kalman filtering for sensor fault estimation and isolation of satellite attitude control based on descriptor systems. (April 2019)

• Record Type: Journal Article
• Authors: Wang, Mao; Liang, Tiantian
• Other Names: Li Kang guest-editor.
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 41:Number 6(2019)
• Page Start: 1686
• Page End: 1698
• Publication Date: 2019-04
• Subjects: Adaptive algorithm -- descriptor system -- fault estimation and isolation -- Kalman filter -- sampled-data system; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331218787605
• Languages: English
• ISSNs: 0142-3312

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.