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Linear, Parameter Varying Model Reduction for Aeroservoelastic Systems

Authors: Claudia Moreno; Peter Seiler; Gary Balas;

Linear, Parameter Varying Model Reduction for Aeroservoelastic Systems

Abstract

This paper applies a model reduction method for linear parameter-varying (LPV) systems based on parameter-varying balanced realization techniques to a body freedom utter (BFF) vehicle. The BFF vehicle has a coupled short period and rst bending mode with additional structural bending and torsion modes that couple with the rigid body dynamics. These models describe the BFF vehicle dynamics with considerable accuracy, but result in high-order state space models which make controller design extremely dicult. Hence, reduced order models for control synthesis are generated by retaining a common set of states across the ight envelope. Initially the full order BFF models of 148 states are reduced to 43 states using standard truncation and residualisation techniques. The application of balanced realization techniques at individual point designs result in 20 state models. Unfortunately, the application of balanced realization techniques at individual operating conditions results in dierent states being eliminated at each operating condition. The objective of LPV model reduction is to further reduce the model state order across the ight envelope while retaining consistent states in the LPV model. The resulting reduced order LPV models with 26 states capture the dynamics of interest and can be used in the synthesis of active utter suppression controllers.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
15
Average
Top 10%
Top 10%
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