An improved aeroservoelastic modeling approach for state-space gust analysis
Chengyu Yue, Yonghui Zhao
Abstract: As is well known, gust responses of an elastic aircraft can be predicted by both time-domain and frequency-domain methods. The frequency-domain method can produce accurate predictions as long as the obtained aerodynamic data in frequency-domain is accurate. The time-domain method might involve the problem of degraded accuracy due to the modeling errors resulting from the rational function approximation (RFA) of the frequency-domain aerodynamic forces. However, the state-space time-domain model can efficiently deal with a large number of calculations needed for determining the worst-case gust loads, and is very convenient for the design of an aeroelastic controller. In view of the disadvantages of traditional RFA, this paper proposes an aerodynamic subsystem realization technique, which is used to build time-domain aeroservoelastic state-space model with a high precision. Firstly, the Loewner framework is introduced to generate an accurate descriptor system using frequency-domain aerodynamic data. Then, model decomposition and reduction are applied to the descriptor system in order to generate a reduced order system with guaranteed stability. Finally, the aerodynamic subsystem is expressed in state-space form after extracting the additional direct term. The proposed modeling method of the aerodynamic subsystem does not need iterative calculations and artificial selection of aerodynamic poles, and has the advantages in accuracy, efficiency and numerical stability. The proposed method is verified by an example of a transport aircraft flying into the fields of discrete and continuous gusts. The simulation results demonstrate that the developed state-space aerodynamic model is sufficiently accurate, and the time-domain method established in this paper can achieve the same accuracy as the frequency-domain method.
原文链接: https://www.sciencedirect.com/science/article/pii/S0889974620306174?casa_token=iMtW10qSagsAAAAA:2qWnJr4Rkg656AfdK261sXC_AA2mJwaGo6A82hWwTrNQtrY2HdPePSxUyM05cdZOLMkSL5JXZws
