A Balanced Mode Decomposition Approach for Equation-Free Reduced-Order Modeling of LPV Aeroservoelastic Systems

The paper proposes a novel approach to data-driven reduced-order modeling which com-bines the Dynamic Mode Decomposition technique with the concept of balanced realization. The information on the system comes from input, state, and output trajectories, and the goal is to derive a linear low-dimensional input-output model approximation. Since the dynamics of aerospace systems markedly changes when some parameters are varied, it is desirable to capture this feature in the system’s description. Therefore, a Linear Parameter-Varying representation made of a collection of state-consistent linear time-invariant reduced-order models is sought. The main technical novelty of the proposed algorithm consists of replacing the orthogonal projection onto the POD modes, typical of Dynamic Mode Decomposition techniques, with a balancing oblique projection. The advantages are that the input-output information in the lower-dimensional representation is maximized, and that a parameter-varying projection is possible while also achieving state-consistency. The validity of the proposed approach is demonstrated on a morphing wing for airborne wind energy applications by comparing its prediction capabilities with those of a recent algorithm from the literature.