Variational System Identification of Aircraft

TL;DR

This paper introduces variational system identification as a robust alternative to traditional methods for estimating aircraft parameters from flight data, demonstrating improved convergence and practical applicability.

Contribution

It presents a new variational formulation for system identification that avoids Riccati equations and enhances convergence in aircraft parameter estimation.

Findings

Better convergence properties than filter-error method

Effective with null initial guesses

Applicable to real flight-test data

Abstract

Variational system identification is a new formulation of maximum likelihood for estimation of parameters of dynamical systems subject to process and measurement noise, such as aircraft flying in turbulence. This formulation is an alternative to the filter-error method that circumvents the solution of a Riccati equation and does not have problems with unstable predictors. In this paper, variational system identification is demonstrated for estimating aircraft parameters from real flight-test data. The results show that, in real applications of practical interest, it has better convergence properties than the filter-error method, reaching the optimum even when null initial guesses are used for all parameters and decision variables. This paper also presents the theory behind the method and practical recommendations for its use.