Complementary Use of Black-Box and Physics-Based techniques in Rotorcraft System identification

Abstract

Accurate linear helicopter models are needed for control system development and simulation and can be determined by system identification when appropriate test data are available. Standard methods for rotorcraft system identification are the frequency domain maximum likelihood method and the frequency response method that are used to derive physics-based linear state-space models. But also the optimized predictor-based subspace identification method (PBSIDopt), a time domain system identification method that yields linear black-box state-space models, has been successfully applied to rotorcraft data. As both methods have their respective strengths and weaknesses, it was tried to combine both techniques. The paper demonstrates the successful complementary use of physics-based frequency domain methods and the black-box PBSIDopt method in the areas of database requirements, accuracy metrics, and model structure development using flight test data of DLR's ACT/FHS research rotorcraft.