Within the FaUSt (Fast Unmanned Scout) project, an existing UAV with intermeshing rotors was enhanced by two electric pusher propellers to extend the speed range for future manned-unmanned teaming research. The modeling of such a compound UAV has not yet been covered in the literature. To extract models for simulation and flight control design, dedicated flight tests were performed using a multi-axis binary noise signal for system identification with activated base flight control system. Two different system identification methods are applied to estimate bare-airframe models of the modified UAV at hover condition: one in the time domain, the other in the frequency domain. The time domain method can inherently be applied to correlated closed-loop data, while input correlations have to be accounted for when using the frequency domain method. The resulting models are analyzed in both time and frequency domain and the model eigenvalues and modes are compared in detail. The results indicate that the time-domain model matches low frequency modes of the vehicle more accurately, but both models show good model performance.
Abstract. In this paper we consider the following problem, known as implicit Lagrange problem: nd the trajectorywhere the constraints are de ned by an implicit di erential equationwith dim F = n ;q < dim x = n. W e de ne the geometric framework of a q--submanifold in the tangent bundle of a surrounding manifold X, which is an extension of the -submanifold geometric framework de ned by Rabier and Rheinboldt for control systems. With this geometric framework, we de ne a class of well-posed implicit di erential equations for which w e obtain locally a controlled vector eld on a submanifold W of the surrounding manifold X by means of a reduction procedure.We then show that the implicit Lagrange problem leads locally to an explicit optimal control problem on the submanifold W, for which t h e Pontryagin maximum principle is naturally apply.
It is foreseen that in the upcoming application of (electric) urban air taxis, the comfort of ride and especially the experience of motion sickness will play a vital role in acceptance among passengers and therefore economic success of these vehicles. For this reason, accurate motion sickness prediction models are needed, which later can be employed for, for example, kinetosis-low trajectory generation. Established motion sickness models like the ISO 2631 standard, however, only take into account the vertical translational axis and no rotational axis. For this reason, the 6-degrees-of-freedom Kamiji motion sickness model is selected and modified in order to circumvent unsatisfactory prediction results with this model. Subsequently, the parameters of this model are retuned by employing an optimization approach based on published experimental data. It is then shown that with this approach, the modified Kamiji model is better suited for predicting the motion sickness results of this dataset. In the future, this model shall be tested and validated via a series of flight tests with test subjects in DLR's BO-105 helicopter.
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