Real-Time Monitoring and Prediction of the Pilot Vehicle System (PVS) Closed-Loop Stability

Abstract

Understanding human control behavior is an important step for improving the safety of future aircraft. Considerable resources are invested during the design phase of an aircraft to ensure that the aircraft has desirable handling qualities. However, human pilots exhibit a wide range of control behaviors that are a function of external stimuli, aircraft dynamics, and human psychological properties (such as workload, stress, confidence, and sense of urgency). This variability is difficult to address comprehensively during the design phase and may lead to undesirable pilot-aircraft interaction, such as pilot-induced oscillations (PIO). This creates the need to keep track of human pilot performance in real-time to monitor the pilot vehicle system (PVS) stability. This work focused on studying human control behavior using human-in-the-loop (HuIL) experiments to obtain information about the human controlled system (HCS) stability. The main focus of the dissertation is human control model parameter estimation To replicate different flight conditions, this study included time delay and actuator rate limiting phenomena, typical of actuator dynamics, during the experiments. To study human control behavior, this study employed the McRuer model for single-input singleoutput manual compensatory tasks. The McRuer model is a lead-lag controller with time delay which has been shown to adequately model manual compensatory tasks. This dissertation presents a novel technique to estimate McRuer model parameters in real-time and associated validation using HuIL experiments to correctly predict HCS stability. The McRuer model parameters were estimated in real-time using an unscented Kalman filter (UKF) approach. The estimated parameters were then used to analyze the stability of the closed-loop HCS and verify them against the experimental data. I would like to thank my fellow researchers in Interactive Robotics Laboratory for assistance with the flight testing proram. The discussions, colaborative environment and constant encouragement to become a better group member and researcher, thank you. I would also like to thank my committee members Dr. Marcello R. Napolitano, Dr. Mario Perhinschi, Dr. Xiaopeng Ning, and Dr. Sergiy Yakovenko. I appreciate your time and effort to review my thesis and provide helpful comments. I would like to acknowledge the contribution of Stéphane D'Urso for helping me with 3D printing of numerous parts for my experiments. Finally, I would like to thank my family who always taught me to be a better person.