Flight control oriented bottom-up nonlinear modeling of aeroelastic vehicles

Béla Takarics, Bálint Vanek, Aditya Kotikalpudi, Peter Seiler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The fuel efficiency of future aircraft can be improved by reducing the weight and structure and by increasing the wingspan. This makes the aircraft structure more flexible and results in increased aeroservoelastic (ASE) effects. The use of active control systems to suppress ASE effects is an important aspect for future flight control systems. The basis of active control system design is an appropriate control oriented model, usually given in the linear parameter-varying (LPV) framework. The ASE model is based on the integration of aerodynamics, structural dynamics and flight dynamics. These subsystems can be developed separately and combined to form the ASE model. The dynamic order of such ASE models is usually too large for control synthesis and implementation. Thus, model order reduction is required. However, model order reduction of LPV systems can still lead to challenges. The aim of the paper is to overcome this reduction step by using a 'bottom-up' modeling approach. The main idea is to use low order, simple subsystems and/or reduce them before integrating them into the nonlinear model. Therefore, a low order control oriented model is created that captures the key ASE dynamics of the aircraft. An important benefit of this modeling approach is that the physical meaning of the states is retained. The specific flexible aircraft example is the mini MUTT (Multi Utility Technology Testbed) vehicle. The bottom-up modeling approach, by reducing the linear structural dynamics and the parameter dependent aerodynamics subsystems, resulted in a 33 state low order nonlinear model (LOM). The nonlinear model is then linearized about a family of 'trim points' by Jacobian linearization leading to a grid based LPV model. A full order model (FOM) is developed in order to evaluate the accuracy of the 33 state LOM. The FOM is developed in the same way as the LOM. However, the subsystems are not reduced in this case, leading to a 97 states model. The accuracy of the low order model is confirmed by evaluating the v-gap metric with respect to the full order model and by time domain simulations.

Original languageEnglish (US)
Title of host publication2018 IEEE Aerospace Conference, AERO 2018
PublisherIEEE Computer Society
Pages1-10
Number of pages10
ISBN (Electronic)9781538620144
DOIs
StatePublished - Jun 25 2018
Event2018 IEEE Aerospace Conference, AERO 2018 - Big Sky, United States
Duration: Mar 3 2018Mar 10 2018

Publication series

NameIEEE Aerospace Conference Proceedings
Volume2018-March
ISSN (Print)1095-323X

Other

Other2018 IEEE Aerospace Conference, AERO 2018
CountryUnited States
CityBig Sky
Period3/3/183/10/18

Bibliographical note

Funding Information:
The research leading to these results is part of the FLEXOP project. This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 636307.

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