Aircraft Mass Properties Estimation During Airdrop Maneuver: A Nonlinear Filtering Approach

Abstract

Unlike a single-body approach, modeling based on a two-body approach has been employed to prepare the required system dynamic model as a time update equation in the applied filtering technology and measurement data for the estimation process. This more precise mathematical model enabled better understanding about the dynamics of the change in the aircraft mass properties during the airdropping operation. The problem is defined as estimation of the optimal mass properties parameters for the best possible fit of the model output to the real data. The parameter estimation problem is investigated by a nonlinear filtering methodology in two sequential steps. In the first step, the single extended Kalman filter is used to estimate the aircraft total mass and moment of inertia before entering an airdrop maneuver. In the second step, both the aircraft dynamic states and mass parameters are estimated during an airdrop maneuver using a joint extended Kalman filter. Monte Carlo simulations have then been performed to assess the performance of the algorithm in the presence of measurement errors and model uncertainties. The results indicate that time-varying aircraft mass properties estimation, as a technological requirement for the aircraft adaptive flight control system to reduce the risks involved with the airdrop operation, is satisfactorily possible.