Autonomous precision airdrop systems are widely used to de- liver supplies to remote locations. Payloads that are delivered far from their intended targets 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 on- line 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.
How to Cite
Model-based, parafoil, FDI, observer-based, residual
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