Multi-Objective Parameter Identification For Helicopters Using Auxiliary Trim Information

Abstract

Classic rotorcraft parameter identification techniques estimate the unknown parameters by minimizing the mismatch between simulation model predictions and observed data. Various types of data can be used for this purpose, including local linear approximations of the system (e.g., in the form of frequency responses or state-space models), time series of input and output variables, and the states and inputs of the aircraft at trim. Current parameter identification methods essentially consider only one type of information at a time. This paper proposes a novel method that exploits all three types of information simultaneously, by embedding them in a multi-objective optimization framework. Although there are no examples of this approach in the aerospace literature, it is theoretically grounded in regularization theory and has been successfully applied to other fields of engineering. A detailed description of the method's implementation for rotorcraft parameter identification is provided. Preliminary results are presented for the identification of physical parameters in a nonlinear helicopter model, using a bi-objective approach that incorporates time-series and trim data.