Future-Trend-Aware Filter-Based PD-MRAC Method for Quadrotors With Unknown Strong Disturbances

Robust flight in complex and windy environments is critical for both single and multiple quadrotors. Existing methods either learn disturbance model at high computational cost or use error-based adaptive control with a speed-stability tradeoff that makes tuning difficult. To address these issues, this article proposes a future-trend-aware filter-based proportional-derivative model reference adaptive control (PD-MRAC) for single quadrotor and a distributed PD-MRAC for multiple quadrotor formation. By embedding a trend-aware derivative term in the adaptive update laws, the controller obtains anticipatory information about the error evolution, enabling rapid adaptation while mitigating oscillations. For more disturbance-sensitive multiquadrotors, we design a robust distributed protocol under a directed graph, improving resilience to disturbances. The approach maintains low computational cost and supports fast adaptive updates. Extensive simulations and real-world experiments validate improvement. For single quadrotor, the root mean square error (RMSE) reduced by around 57% versus the baselines and by around 12% versus the DJI Mavic 2. For multiquadrotors, formation results show enhanced robustness in simulation and effective real-world indoor/outdoor experiments under strong winds.