Detection and estimation of icing in unmanned aerial vehicles using a bank of unknown input observers

Icing is regarded as a severe structural alteration affecting unmanned aerial vehicles (UAVs), since ice accretion on wings and control surfaces modifies the aircraft shape resulting in altered controllability and performance of the vehicle. We study the problem of detection of icing and estimation of its `severity' factor in longitudinal control of UAVs. We propose to employ a bank of unknown input observers (UlOs), each designed to match a model of the aircraft under a particular level of icing taken from a quantisation of the icing's severity factor range of variation. The UIO design exploits the change in equilibrium conditions, caused by the icing effect, to identify a direction in the observer estimation error space that allows for aircraft icing detection and estimation. By selecting at each time the observer from the bank that yields the smallest value of a suitable residual signal, the icing severity factor can be estimated with an accuracy that is inversely proportional to the size of the quantisation level.