The urgent need to further reduce fuel burn and climate impact for next-generation aircraft drives wings to become lighter and with higher aspect ratios to reduce induced drag. The resulting increase in wing flexibility presents both an opportunity and numerous challenges, as such wings experience increased structural loads at the wing root and are more prone to aeroelastic instabilities such as flutter. Passive and active flutter suppression and load alleviation techniques therefore provide a promising solution to enable these wings without the subsequent weight increase. For active flutter suppression and load alleviation, it is particularly important to express the free-flying aeroservoelastic model as an interpretable state-space model with manageable order.

In this regard, the aircraft loads model in the DLR Loads Kernel is formally derived and refined to make it suitable for free-flying aeroservoelasticity, constituting several improvements over the original model. The model is strictly expressed as a closed-form, symbolic, monolithic state-space representation, rigorously derived in this work. Besides improved interpretability and extensibility, this also allows for easily porting the nonlinear state-space model from Python to MATLAB/Simulink, and therefore effectively bridges NASTRAN and Simulink, the industry standards for finite-element modeling and control design respectively. To further improve suitability for free-flying aeroservoelasticity, the spiral nature of the Sears function is approximated using cascaded gust zones using a Padé approximation, significantly reducing the number of disturbance inputs. Physical RFA is employed, with the resulting aerodynamic lag dynamics projected to lag force and moment dynamics, achieving a substantial reduction in lag states. Additionally, the model is augmented with actuator dynamics and an accelerometer sensor model.

As the state-space model requires a representative aircraft, the Embraer Benchmark Wing, used for research on aeroelastic tailoring, is systematically transformed into a free-flying finite-element model, enabling the generation of free-flying aeroservoelastic state-space models with different wing mass and stiffness distributions at various mass cases and in varying flight conditions. Using this aircraft and in the considered simulations, the derived model achieves a 100–400x improvement in simulation time and a 458x reduction in input file size, and thus eliminates the need to save model output. Gust inputs are reduced from n to 2, and lag states from n x p to (6 + m + s) x p, with n aerodynamic panels, p RFA poles, m flexible modes, and s monitoring forces and/or moments. At the same time, these refinements are shown to not compromise model behaviour, as results show excellent agreement. These improvements, in conjunction with the added actuator dynamics and sensor model, make the state-space model well-suited for future work in free-flying active flutter suppression, active load alleviation, and on the longer-term, control co-design.