The balanced mode decomposition algorithm for data-driven LPV low-order models of aeroservoelastic systems
File(s)
Published version
Author(s)
Iannelli, Andrea
Fasel, Urban
Smith, Roy S
Type
Journal Article
Abstract
A novel approach to reduced-order modeling of high-dimensional systems with time-varying properties is proposed. It combines the problem formulation of the Dynamic Mode Decomposition method with the concept of balanced realization. It is assumed that the only information available on the system comes from input, state, and output trajectories, 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. Time-varying features of the system are retained by means of 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 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 recent algorithms. Analyses account for both prediction accuracy and closed-loop performance in model predictive control applications.
Date Issued
2021-08
Date Acceptance
2021-05-11
Citation
Aerospace Science and Technology, 2021, 115, pp.1-15
ISSN
1270-9638
Publisher
Elsevier BV
Start Page
1
End Page
15
Journal / Book Title
Aerospace Science and Technology
Volume
115
Copyright Statement
© 2021 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.sciencedirect.com/science/article/pii/S127096382100331X?via%3Dihub
Publication Status
Published
Article Number
106821
Date Publish Online
2021-05-20