Abstract:
The development of an adaptive pilot model for rotorcraft tracking tasks is useful to understand and replicate human pilot behavior under varying vehicle dynamics and environmental conditions. This paper presents a Model-Reference Adaptive Control (MRAC)-based pilot model designed to emulate the adaptability of human pilots during attitude and position tracking tasks. The model leverages wavelet analysis to characterize pilot behavior and employs a closed-loop system identification approach to derive baseline pilot parameters. MRAC methodology using state-feedback is implemented and validated through simulations involving time-varying vehicle dynamics, such as changes in control sensitivity and added phase delays. Results demonstrate the model's ability to maintain consistent tracking performance despite dynamic modifications, though discrepancies with human pilot data highlight the complexity of fully capturing adaptive human control strategies. The proposed model offers a framework for integrating human adaptability into flight system design and simulation tools.