Application of the Predictor-Based Subspace Identification Method to Rotorcraft System Identification

Abstract

In this paper, the optimized predictor-based subspace identification (PBSIDopt) method is applied to identify linear models of DLR's research helicopter ACT/FHS and to evaluate its usage to enhance existing physics based models in the future. For this effort, dedicated identification flight test data is used. This paper first describes the well known Maximum Likelihood frequency domain output error method and the applied physical model briefly. Then, the PBSIDopt method is presented and parameters, which influence the identification process, are discussed. Results from both methods using the same flight test data of the ACT/FHS are compared; model accuracy, order and missing dynamics are investigated. Advantages and disadvantages of both methods are evaluated and the applicability of the PBSIDopt method to rotorcraft system identification and its usage to improve the existing physical model structure is discussed.