Beam-like flexible structures are of interest in many fields of engineering, particularly aeronautics, where wings are frequently modelled and represented as such. Experimental modal analysis is commonly used to characterise the wing’s dynamical response. However, unlike other flexible structure applications, no benchmark problems involving high-aspect-ratio flexible wings have appeared in the open literature. To address this, this paper reports on ground vibration testing results for a flexible wing and its sub-assembly and parts. The experimental data can be used as a benchmark and are available to the aeronautical and structural dynamics community. Furthermore, non-linearities in the structure, where present, were detected. Tests were performed on the whole wing as well as parts and sub-assembly, providing four specimens. These were excited with random vibration at three different amplitudes from a shaker table. The modal properties of a very flexible high-aspect-ratio wing model, its sub-assembly and parts, were extracted, non-linear behaviour was detected and the experimental data are shared in an open repository.

Data-driven structural health monitoring (SHM) requires precise estimates of the target system behaviour. In this sense, SHM by means of modal parameters is strictly linked to system identification (SI). However, existing frequency-domain SI techniques have several theoretical and practical drawbacks. This paper proposes using an input-output system identification technique based on rational interpolation, known as the Loewner framework (LF), to estimate the modal properties of mechanical systems. Pioneeringly, the Loewner framework mode shapes and natural frequencies estimated by LF are then applied as damage-sensitive features for damage detection. To assess its capability, the Loewner framework is validated on both numerical and experimental datasets and compared to established system identification techniques. Promising results are achieved in terms of accuracy and reliability.

Flow over an aircraft at high angles of attack is characterized by a combination of separated and vortical flows that interact with each other and with the airframe. As a result, there is a set of phenomena negatively affecting the aircraft’s performance, stability and control, namely, degradation of lifting force, nonlinear variation of pitching moment, positive damping, etc. Wind tunnel study of aerodynamic characteristics of a prospective transonic aircraft, which is in a canard configuration, is discussed in the paper. A three-stage experimental campaign was undertaken. In the first stage, a steady aerodynamic experiment was conducted. The influence of a reduced oscillation frequency and angle of attack on unsteady aerodynamic characteristics was studied in the second stage. In the third stage, forced large-amplitude oscillation tests were carried out for the detailed investigation of the unsteady aerodynamics in the extended flight envelope. The experimental results demonstrate the strongly nonlinear behavior of the aerodynamic characteristics because of canard vortex effects on the wing. The obtained data are used to design and test mathematical models of unsteady aerodynamics via different popular approaches, namely the Neural Network (NN) technique and the phenomenological state space modeling technique. Different NN architectures, namely feed-forward and recurrent, are considered and compared. Thorough analysis of the performance of the models revealed that the Recurrent Neural Network (RNN) is a universal approximation tool for modeling of dynamic processes with high generalization abilities.

Increasing levels of autonomy impose more pronounced performance requirements for unmanned ground vehicles (UGV). Presence of model uncertainties significantly reduces a ground vehicle performance when the vehicle is traversing an unknown terrain or the vehicle inertial parameters vary due to a mission schedule or external disturbances. A comprehensive mathematical model of a skid steering tracked vehicle is presented in this paper and used to design a control law. Analysis of the controller under model uncertainties in inertial parameters and in the vehicle-terrain interaction revealed undesirable behavior, such as controller divergence and offset from the desired trajectory. A compound identification scheme utilizing an exponential forgetting recursive least square, generalized Newton–Raphson (NR), and Unscented Kalman Filter methods is proposed to estimate the model parameters, such as the vehicle mass and inertia, as well as parameters of the vehicle-terrain interaction, such as slip, resistance coefficients, cohesion, and shear deformation modulus on-line. The proposed identification scheme facilitates adaptive capability for the control system, improves tracking performance and contributes to an adaptive path and trajectory planning framework, which is essential for future autonomous ground vehicle missions.