Fault Detection and Isolation for Autonomous Parafoils

Matthew R. Stoeckle, Amer Fejzic, Louis S. Breger, and Jonathan P. How
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Full Paper
phmc_13_036.pdf528.64 KBSeptember 12, 2013 - 6:31am

Autonomous precision airdrop systems are widely used to deliver
supplies to remote locations. Payloads that are delivered
far from their intended target or with high impact velocity
may be rendered unusable. Faults occurring during flight can
severely degrade vehicle performance, effectively nullifying
the value of the guided system, or worse. Quickly detecting and identifying
faults enables the choice of an appropriate recovery
strategy, potentially mitigating the consequences of an
out-of-control vehicle and recovering performance. This paper
presents a multi-observer, multi-residual fault detection
and isolation (FDI) method for an autonomous parafoil system.
The detection and isolation processes use residual signals
generated from observers and other system models. Statistical
methods are applied to evaluate these residuals and
determine whether a fault has occurred, given a priori knowledge
of system uncertainty characteristics. Several examples
are used to illustrate the detection and isolation algorithm online
using available navigation and telemetry outputs. Tests
of this FDI method on a large number of high-fidelity simulations
indicate that it is possible to detect and isolate some
common faults with a high percentage of success and a very
small chance of raising a false alarm.

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Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
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