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A novel approach to reduced-order modeling of high-dimensional time varyingsystems is proposed. It leverages the formalism of the Dynamic ModeDecomposition technique together with the concept of balanced realization. Itis assumed that the only information available on the system comes from input,state, and output trajectories generated by numerical simulations or recordedand estimated during experiments, thus the approach is fully data-driven. Thegoal is to obtain an input-output low dimensional linear model whichapproximates the system across its operating range. Since the dynamics ofaeroservoelastic systems markedly changes in operation (e.g. due to change inflight speed or altitude), time-varying features are retained in theconstructed models. This is achieved by generating a Linear Parameter-Varyingrepresentation made of a collection of state-consistent linear time-invariantreduced-order models. The algorithm formulation hinges on the idea of replacingthe orthogonal projection onto the Proper Orthogonal Decomposition modes, usedin Dynamic Mode Decomposition-based approaches, with a balancing obliqueprojection constructed entirely from data. As a consequence, the input-outputinformation captured in the lower-dimensional representation is increasedcompared to other projections onto subspaces of same or lower size. Moreover, aparameter-varying projection is possible while also achievingstate-consistency. The validity of the proposed approach is demonstrated on amorphing wing for airborne wind energy applications by comparing theperformance against two algorithms recently proposed in the literature.Comparisons cover both prediction accuracy and performance in model predictivecontrol applications.

The Balanced Mode Decomposition Algorithm for Data-Driven LPV Low-Order Models of Aeroservoelastic Systems