Model Reference Adaptive Gain-Scheduled PID control for Robust Heading Stabilization in UAS under Wind Disturbances

Unmanned Aircraft Systems (UAS) are increasingly deployed in diverse missions, and maintaining heading stability in the presence of unpredictable wind disturbance is a significant challenge. This paper proposes a novel model reference adaptive gain-scheduled PID (Proportional-Integral-Derivative) control framework tailored for the heading control of flapping-wing UAS (ornithopter) operating under dynamic wind conditions. The control architecture integrates an estimated wind disturbance value and adaptively tunes the PID gains by minimizing the error between the actual system response and a desired reference model. Gain scheduling mechanism uses airspeed, yaw rate, and estimated wind magnitude to ensure stability. The proposed method is validated on a 6-DOF UAS simulation model subjected to dynamic wind and temperature variation profiles. Comparative results show improved heading accuracy, responsiveness, and robustness over conventional fixed-gain and static gain-scheduled PID controllers, paving the way for safer and more efficient autonomous UAS missions. Also, the approach can be adapted to other platforms in future applications.