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

TL;DR

This paper introduces a data-driven, balanced mode decomposition algorithm for creating low-order, linear, parameter-varying models of aeroservoelastic systems, capturing time-varying dynamics for control and prediction.

Contribution

It develops a novel balanced mode decomposition method that constructs state-consistent, low-dimensional LPV models from input-output data, improving accuracy over existing approaches.

Findings

Enhanced prediction accuracy demonstrated on a morphing wing system.

Improved model performance in model predictive control tasks.

Outperforms recent algorithms in capturing system dynamics.

Abstract

A novel approach to reduced-order modeling of high-dimensional time varying systems is proposed. It leverages the formalism of the Dynamic Mode Decomposition technique together with the concept of balanced realization. It is assumed that the only information available on the system comes from input, state, and output trajectories generated by numerical simulations or recorded and estimated during experiments, thus the approach is fully data-driven. The goal is to obtain an input-output low dimensional linear model which approximates the system across its operating range. Since the dynamics of aeroservoelastic systems markedly changes in operation (e.g. due to change in flight speed or altitude), time-varying features are retained in the constructed models. This is achieved by generating a Linear Parameter-Varying representation made of a collection of state-consistent linear time-invariant reduced-order models. The algorithm formulation hinges on the idea of replacing the orthogonal projection onto the Proper Orthogonal Decomposition modes, used in Dynamic Mode Decomposition-based approaches, with a balancing oblique projection constructed entirely from data. As a consequence, the input-output information captured in the lower-dimensional representation is increased compared to other projections onto subspaces of same or lower size. Moreover, 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 the performance against two algorithms recently proposed in the literature. Comparisons cover both prediction accuracy and performance in model predictive control applications.