Thruster Fault Detection, Isolation and Accommodation for an Autonomous Spacecraft - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

Thruster Fault Detection, Isolation and Accommodation for an Autonomous Spacecraft

(1) , (1) , (2) , (3)
1
2
3

Abstract

The presented work is a result of a research collaboration between European Space Agency, Thales Alenia Space and IMS Laboratory with the aim of promoting fault-tolerant control strategies to advance spacecraft autonomy. A multiple observer based scheme is proposed jointly with an online constrained allocation algorithm to detect, isolate and accommodate a single thruster fault affecting the propulsion system of an autonomous spacecraft. Robust residual generator with enhanced robustness to time delays induced by the propulsion drive electronics and uncertainties on thruster rise times is used for fault detection purposes. A decision test on the residual of the fault detector triggers a bank of nonlinear unknown input observers which is in charge of confining the fault to a subset of possible faults. The faulty thruster isolation is achieved by matching the residual and the thruster force directions using the direction cosine approach. Finally, the fault is accommodated by redistributing the desired forces and torques among the remaining (healthy) thrusters and closing the isolated thruster. Simulation results from the "high-fidelity" industrial simulator, provided by Thales Alenia Space, demonstrate the fault-tolerance capabilities of the proposed scheme.
Fichier principal
Vignette du fichier
IFAC2014.pdf (542.92 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00989422 , version 1 (13-09-2014)

Identifiers

Cite

Robert Fonod, David Henry, Eric Bornschlegl, Catherine Charbonnel. Thruster Fault Detection, Isolation and Accommodation for an Autonomous Spacecraft. 19th IFAC World Congress, Aug 2014, Cape Town, South Africa. pp.10543-10548, ⟨10.3182/20140824-6-ZA-1003.02144⟩. ⟨hal-00989422⟩
283 View
380 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More