Model-Reference Adaptive Pilot Model of Rotorcraft Position Tracking Tasks with Time-Varying Vehicle Dynamics

This paper extends a previously developed adaptive pilot model framework for inner-loop roll-attitude tracking [1] to outer-loop position tracking tasks. Pilot Model identification is performed for two command signal types - a discrete step-like signal and a continuous Sum-of-Sines (SOS) signal - yielding distinct parameter signatures that reflect the different anticipatory and tracking demands of each signal type. An adaptive pilot model for the outer-loop position tracking task is formulated using a model-reference neural network (MRNN) architecture with a linearly parameterized neural network updated by a Lyapunov-stable adaptive law. Simulation results for both discrete and continuous tasks demonstrate that the adaptive pilot model remains stable and maintains position tracking performance under both a doubling and a halving of the nominal control sensitivity. Preliminary results are also presented for a multi-axis maritime task, extending the framework to simultaneous lateral and vertical position tracking.