Abstract
Human pilot control during the landing approach is modeled using a stochastic switched autoregressive model with exogenous inputs (SS-ARX), which is a combination of the conventional ARX model and a hidden Markov model (HMM). Using this model, the time histories of pilot control can be categorized to several states, such as pitch stabilizing mode. First, the selection of model inputs is discussed, and flight and control data are obtained using a flight simulator. From this data, the pilot approach control model is constructed for several cases with different wind conditions based on an expectation-maximization (EM) algorithm. The obtained models are analyzed with respect to the timing of state transitions and the ARX model gain parameters. The preliminary findings suggest that the proposed analysis method has a great potential to reveal a pilot’s decision making process.
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