Publications.

The wings of transport jets are becoming high aspect ratio to reduce induced drag. Because of the high aspect ratio configuration with lightweight, the wings may undergo nonlinear large deformations induced by aerodynamic forces. To alleviate excessive large deformation under gust conditions, flared hinged wings have been developed. This study develops a new multibody simulation framework for the flared hinged wings based on absolute nodal coordinate formulation (ANCF). In this framework, the constraint equation to describe the flared hinge joint can be written in a simple linear equation. We show that the simulation results of ANCF simulation framework is in good agreement with those of a second simulation framework, namely SHARPy, based on a conventional geometrically-exact beam formulation (GEBF).

Highly flexible wings may exhibit significant geometrical nonlinearities and prediction of their dynamic aeroelastic characteristics poses a substantial computational challenge for wing and control design. To address this, we present a novel data-driven method for nonlinear reduced-order modelling of such wings. Our approach extends the Dynamic Mode Decomposition with Control (DMDc) framework by introducing a conic optimization problem that embeds physical constraints, namely stability and steady-state deformation, directly into the model. Using time-domain nonlinear aeroelastic simulation data of the Pazy wing, a data-driven model is generated that accurately captures the nonlinear aerodynamics forcing across a wide range of angles of attack. Our method ensures adherence to known aerodynamic behaviour and eliminates the need for interpolation across linearized models. Validation has been carried out and demonstrates the effectiveness of the method in modelling aerodynamic forces and moments both within and beyond the training range, providing a compact, robust representation suitable for control applications.

We present a systematic method of selecting vibration modes with which to build reduced-order models of geometrically nonlinear flexible structures. Our approach is a recursive algorithm which selects modes based on their ability to capture the nonlinear energy transfer between vibration modes. Furthermore, we formulate an optimization problem which can give rigorous bounds on the time-averaged energy contained in a predefined set of modes. This enables a precise and rigorous quantification of the difference in behavior between reduced-order models derived from linear beam theory, and those derived using Geometrically Exact Beam Theory.

In this paper, an Event-triggered Image-based Visual Servoing Nonlinear Model Predictive Controller (ET-IBVS-NMPC) for multirotor aerial vehicles is presented. The proposed scheme is developed for the autonomous surveillance of contour-based areas with different characteristics (e.g. forest paths, coastlines, road pavements). For this purpose, an appropriately trained Deep Neural Network (DNN) is employed for the accurate detection of the contours. In an effort to reduce the remarkably large computational cost required by an IBVS-NMPC algorithm, a triggering condition is designed to define when the Optimal Control Problem (OCP) should be resolved and new control inputs will be calculated. Between two successive triggering instants, the control input trajectory is applied to the robot in an open-loop fashion, which means that no control input computations are required. As a result, the system's computing effort and energy consumption are lowered, while its autonomy and flight duration are increased. The visibility and input constraints, as well as the external disturbances, are all taken into account throughout the control design. The efficacy of the proposed strategy is demonstrated through a series of real-time experiments using a quadrotor and an octorotor both equipped with a monocular downward looking camera.