Details

Autor(en) / Beteiligte

• Takarics, Bela
• Szollosi, Alexandra
• Vanek, Balint

Titel

Tensor product type polytopic LPV modeling of aeroelastic aircraft

Ist Teil von

• 2018 IEEE Aerospace Conference, 2018, p.1-10

Ort / Verlag

IEEE

Erscheinungsjahr

2018

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• In order to improve fuel efficiency, future aircraft will have reduced weight and increased wingspan. Such aircraft require active control systems to suppress ASE effects. ASE systems are often modeled in the grid based linear parameter-varying (LPV) framework, which captures the parameter varying dynamics. The controller is generally synthesized by solving Linear Matrix Inequalities (LMIs) for the LPV model. Selecting the grid density for such control synthesis approach requires special attention. On the one hand, a too coarse grid might not capture the parameter variation of the dynamics accurately enough. On the other hand, solving LMIs for too dense grid can lead to numerical issues and computational cost. This is usually relaxed by synthesizing the controller for a coarser grid and the stability and performance are verified for a denser grid. A possible remedy for this drawback of grid-based LPV models is polytopic LPV representation. In such case the LMIs need to be solved only for the vertex systems of the convex polytopic hull. Various types of convex polytopic models can be obtained by Tensor Product (TP) model transformation. The aim of the paper is to derive polytopic models for ASE vehicles and to apply these models for flutter suppression control design. The goal is to have a small number of vertex systems and sufficient accuracy while keeping the conservativeness of polytopic modeling low. The aircraft under consideration is the mini MUTT (Multi Utility Technology Testbed) vehicle. Based on the polytopic representation a stabilizing state feedback controller and observer is synthesized. The effectiveness of the resulting control design is verified through numerical simulations.