Longitudinal Landing Risk Situation Model and Suppression Based on Cloud Model and MPC

Abstract

A longitudinal landing risk situation modeling and rejecting method is proposed, which provides a novel way to analyze the landing safety for the carrier-based aircraft. First of all, a linear longitudinal landing model is established based on landing equilibrium states. Second, the approach risk, subjective risk, and waveoff risk models are constructed depending on statistical data from the landing simulation platform, which extract the risk characteristics at different landing stages. Third, the landing risk situation is established based on the cloud model theory, in which the approach risk, subjective risk, and waveoff risk are integrated so that the risk level can be quantified to be a scalar. Finally, the landing risk restrained algorithm is proposed on the basis of model predictive control (MPC), in which the landing situation and states deviations can be rejected simultaneously. The method in this paper is verified on a semi-physical landing platform.