Robust Trajectory Inference for Quadrotor UAVs Under External Disturbances via a Sliding-Mode-Enhanced CLOE Approach

This article examines the challenge of trajectory inference for an unknown system affected by external disturbances, with the objective of reconstructing the trajectory of a quadrotor using a reference model. The proposed methodology extends the Closed-Loop Output Error (CLOE) scheme through two complementary contributions: an identified gain, incorporated into the reference model to guarantee the Hurwitz condition of the closed-loop error dynamics, and a set of sliding-mode correction terms that further accelerate error convergence and enhance robustness against bounded disturbances. The stability of both contributions is formally established via Lyapunov-based analysis. The proposed approach is validated through realistic simulations carried out in the CoppeliaSim robotics environment, considering both constant and time-varying trajectory scenarios. Results show that the hybrid approach improves trajectory inference accuracy and convergence speed, maintaining resilience under adverse conditions, making it a promising alternative for autonomous quadrotor monitoring.