Published: Apr 4, 2022 by Daning Huang
We hosted Dr. Yi Wang from University of South Carolina for a CAV talk via the AI/ML technical group. Dr. Wang is an expert in aeroelasticity, reduced-order modeling and control.
Abstract Recently, many data-driven reduced order modeling (ROM) techniques for flexible aircraft have been proposed for fast aeroelasticity (AE) analysis and control synthesis. However, a foremost challenge to constructing parametric state-space AE ROMs is how to retain their state consistence at different flight conditions, and the inconsistent ROMs could lead to physically meaningless ROM interpolation and failure to accommodate the entire flight parameter space. This seminar presents a new system identification method based on state consistence enforcement (SCE) to build parametric AE ROMs. A new regularization term is proposed to modify the traditional AutoRegressive eXogenous (ARX) formulation, which specifically penalizes state inconsistence between ROMs at different flight conditions. The new formulation cast in the form of the generalized Tikhonov regularization problem can be computed efficiently and produce fundamentally state-consistent ROMs. The state-consistent AE ROM is compared with the full order model and the ROM without state consistence, and the results demonstrate that the proposed approach significantly improves state consistence and interpolation accuracy of ROMs at varying flight conditions. To the best of our knowledge, this research also represents the first effort of developing parametric state-space ROMs that allow aeroelasticity evaluation in a broad flight envelop.
The video recording is here.