On Time-Domain Parameter Estimation Techniques Applicable to First-Principle Rotorcraft Models

In this paper, we consider the problem of parameter estimation from flight test data for rotorcraft vehicle models. We describe two alternative parameter estimation classes of methods in the time domain, namely the recursive filtering and the batch optimization methods. Both classes of methods are formulated so as to be applicable to complex first-principle models of rotorcraft vehicles. In the recursive approach, we formulate a novel version of the Extended Kalman Filter which specifically accounts for the presence of unobservable states in the model. An important highlight of the proposed approach is that it does not require a reduced-order model of the system. In the case of the batch optimization methods, we present a formulation based on a new single-multiple shooting approach specifically designed for vehicle models with slow and fast solution components. The paper is concluded by a preliminary assessment of the performance of the proposed procedures with the help of applications regarding manned rotorcraft vehicles.