Modeling of Pilot Landing Approach Control Using Stochastic Switched Linear Regression Model

摘要

Human pilot control during the landing approach is modeled using a stochastic switched model, which is a combination of the conventional linear regression model and a hidden Markov model. Using this model, the time histories of pilot control can be categorized in several states, such as pitch stabilizing mode. First, the selection of model inputs is discussed. Based on data obtained in flight simulator experiments, the pilot approach control model is constructed for several cases, with different wind conditions applying an expectation-maximization algorithm. The obtained models are analyzed with respect to the timing of state transitions and the stochastic-switched-model gam parameters. The current findings suggest that the proposed analysis method has a great potential to reveal a pilot's decision-making process.