Flight control oriented bottom-up nonlinear modeling of aeroelastic vehicles

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 ν-gap metric with respect to the full order model and by time domain simulations.

Grid-Based and Polytopic Linear Parameter-Varying Modeling of Aeroelastic Aircraft with Parametric Control Surface Design

“…All data and information related to flight tests, ground tests, the aerodynamic design of the aircraft, software for aerodynamics modeling and control design tools were made open source by the UMN research group and can be downloaded from their website [34]. Papers on the topic of system identification [35,36], ground vibration testing [37,38] and nonlinear modeling of aeroelastic aircraft [32,39] are also available.…”

“…However, time domain aeroelastic simulations require a continuous model. To achieve the continuous model, Roger's rational function approximation (RFA) [48] is applied [32,46]. Using RFA, additional lag states are introduced representing the lag behaviour of the aerodynamic model.…”

“…The second dimension of the scheduling parameters is µ. The model developed is a quasi-LPV system [32], because ρ depends on rigid body velocities u, v and w. The pole migration of the nominal LPV model (µ = 1) is given in Figure 15…”

“…Such high order LPV model is unsuitable for control synthesis. Either LPV model order reduction techniques [26][27][28][29][30][31], or a bottom-up modeling approach can be applied as presented in Reference [32] to obtain a control oriented, reduced-order model. The present paper focuses on the latter.…”

“…Model order reduction of LPV systems is still not a straightforward task [26][27][28][29][30][31]. In order to overcome these challenges, the ASE models are built using the "bottom-up" modeling approach [32]. In this case the subsystems of the ASE model, that have simpler structure when constructed separately, are reduced before their integration into the overall nonlinear model.…”