Unless a segregated airspace and the corresponding clearances can be afforded, flight testing of remotely piloted aircraft is often done near the ground and within visual line-of-sight. In addition to the increased exposure to turbulence, this setup also limits the available time for test manoeuvres on each pass, especially for subscale demonstrators with a relatively high wing loading and flight speed. A suitable testing procedure, efficient excitation signals and a robust system identification method are therefore fundamental. Here, the authors use ground-based flight control augmentation to inject multisine signals with low correlation between the different inputs. Focusing on initial flight-envelope expansion, where linear regression is common, this paper also describes the improvement of an existing frequency-domain method by using an instrumental variable (IV) approach to better handle turbulence and measurement noise and to enable real-time identification analysis. Both simulations and real flight tests on a subscale demonstrator are presented. The results show that the combination of multisine input signals and the enhanced frequency-domain method is an effective way of improving flight testing of remotely piloted aircraft in confined airspace.
The demand to get maximum flight performance with minimum risk for accidents drives the model-based engineering approach when designing flight control laws. The accuracy of the models used needs to be high in order to be able to make realistic simulations to meet these demands. Making models from numerical calculations (CFD) and wind tunnel tests is fairly simple because data are given in a structured way, but data from flight tests are unstructured since control surfaces move due to pilot commands and control system feedback. Additional complications are unstable nonlinear dynamics, noise from measurements and atmospheric turbulence. In this paper, a relatively easy to use engineering method, which gives promising and robust results, is presented.
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