Autonomous failure detection, identification and fault-tolerant estimation for spacecraft guidance, navigation and control

Details Autonomous failure detection, identification and fault-tolerant estimation for spacecraft guidance, navigation and control Autonomous failure detection, identification and fault-tolerant estimation for spacecraft guidance, navigation and control

Jan 1998

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1998aipc..420..134m&link_type=abstract

Space technology and applications international forum - 1998. AIP Conference Proceedings, Volume 420, pp. 134-140 (1998).

Computer Science

Performance

2

Spaceborne And Space Research Instruments, Apparatus, And Components, Microcircuit Quality, Noise, Performance, And Failure Analysis, Electrical And Electronic Instruments And Components

Scientific paper

In this paper, we propose a novel approach for Failure Detection and Identification (FDI) in nonlinear systems based on the Interacting Multiple Model (IMM) Extended Kalman Filter (EKF) approach. In the nonlinear-system FDI application, the main idea consists of representing each failure mode by a model and combining the outputs of EKF's based on different models in a near-optimal way. This IMM-FDI filter provides not only failure detection and identification but also a near-optimal estimate of the system state (even during a failure). The approach has been applied successfully to a problem of spacecraft autonomy for the detection and identification of sensor (gyro, star tracker) and actuator failures. The results of this application show that IMM-EKF detects and identifies failures much more rapidly and reliably than the multi-hypothesis EKF. Furthermore, it handles satisfactorily both permanent and transient failures.

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