Artificial Intelligence
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Linear fractional representations of uncertain systems
Automatica (Journal of IFAC)
Remote Agent: to boldly go where no AI system has gone before
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Diagnosis and Fault-Tolerant Control
Diagnosis and Fault-Tolerant Control
Dynamic multiple fault diagnosis: mathematical formulations and solution techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools
Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
Diagnosis of Complex Systems: Bridging the Methodologies of the FDI and DX Communities
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Integrated Model-Based and Data-Driven Diagnosis of Automotive Antilock Braking Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This paper deals with the design of robust model-based fault detection and isolation (FDI) systems for atmospheric reentry vehicles. This work draws expertise from actions undertaken within a project at the European level, which develops a collaborative effort between the University of Bordeaux, the European Space Agency, and European Aeronautic Defence and Space Company Astrium on innovative and robust strategies for reusable launch vehicles (RLVs) autonomy. Using an H∞/H- setting, a robust residual-based scheme is developed to diagnose faults on the vehicle wing-flap actuators. This design stage is followed by an original and specific diagnosis-oriented analysis phase based on the calculation of the generalized structured singular value. The latter provides a necessary and sufficient condition for robustness and FDI fault sensitivity over the whole vehicle flight trajectory. A key feature of the proposed approach is that the coupling between the in-plane and out-of-plane vehicle motions, as well as the effects that faults could have on the guidance, navigation, and control performances, are explicitly taken into account within the design procedure. The faulty situations are selected by a prior trimmability analysis to determine those for which the remaining healthy control effectors are able to maintain the vehicle around its center of gravity. Finally, some performance indicators including detection time, required onboard computational effort, and CPU time consumption are assessed and discussed. Simulation results are based on a nonlinear benchmark of the HL-20 vehicle under realistic operational conditions during the autolanding phase. The Monte Carlo results are quite encouraging, illustrating clearly the effectiveness of the proposed technique and suggesting that this solution could be considered as a viable candidate for future RLV programs.