Online Pilot Model Parameter Estimation Using Sub-Scale Aircraft Flight Data

Human pilots exhibit a wide range of control behavior changes even during the same phase of the flight. In this paper, an Unscented Kalman Filter (UKF) based real-time pilot model parameter identification algorithm is presented for longitudinal control of an aircraft. The UKF estimates the parameters of a lead-lag pilot model using pilot inputs and flight data collected using a Sub-Scale Research Aircraft (SSRA). The study is focused on the approach and landing phase of the flight. The real time estimation of the parameters can help identify changes in pilot control behavior that could lead to Loss of Control (LOC) events.

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