Robust fault and icing diagnosis in unmanned aerial vehicles using LPV interval observers

Abstract

This paper proposes a linear parameter varying (LPV) interval unknown input observer (UIO) for therobust fault diagnosis of actuator faults and ice accretion in unmanned aerial vehicles (UAVs) describedby an uncertain model. The proposed interval observer evaluates the set of values for the state which arecompatible with the nominal fault-free and icing-free operation, and can be designed in such a way that someinformation about the nature of the unknown inputs affecting the system can be obtained, thus allowing thediagnosis to be performed. The proposed strategy has several advantages. First, the LPV paradigm allowstaking into account operating point variations. Second, the noise rejection properties are enhanced by thepresence of the integral term. Third, the interval estimation property guarantees the absence of false alarms.Linear matrix inequality (LMI)-based conditions for the analysis/design of these observers are provided inorder to guarantee the interval estimation of the state and the boundedness of the estimation. The developedtheory is supported by simulation results, obtained with the uncertain model of a Zagi Flying Wing UAV,which illustrate the strong appeal of the methodology for identifying correctly unexpected changes in thesystem dynamics due to actuator faults or icing