PRED-MPPI: Disturbance-Preview and Efficient MPPI for Robust Quadrotor Tracking with Hardware Validation

Abstract

We propose PRED-MPPI, the first MPPI variant that seamlessly integrates real-time disturbance preview and adaptive discretization for quadrotor tracking control under significant model inaccuracies and time-varying disturbances. Unlike prior MPPI variants (e.g., mathcal{L}_1-MPPI, DA-MPPI), which assume constant or matched disturbances, PRED-MPPI leverages a high-order Generalized Extended State Observer for disturbance preview and a Variable Discretization Grid (VDG) to reduce computation and control variance. The synergy enables real-time (50 Hz) quadrotor control under time-varying and mismatched disturbances. Extensive comparative simulation and real-world Crazyflie experiments demonstrate substantial performance gains. In AirSim simulation, PRED-MPPI reduces computation time by over 30%, and mean RMSE by 10.3%, 13.5%, and 14.6% compared to baseline MPPI, and by 2.59%, 3.62%, and 5.80% compared to DA-MPPI across three representative scenarios. In real-world Crazyflie experiments, for ground-effect-disturbed hovering, PRED-MPPI reduces mean and standard deviation (Std) of X–Y plane error by 14.2%/17.9% and 6.03%/21.6% compared to MPPI and DA-MPPI; for fan-induced wind experiments, PRED-MPPI yields improvements of 23.4%/36.8% and 13.8%/25.0% in RMSE and tracking error Std. These results establish PRED-MPPI as the first disturbance-preview MPPI achieving real-world UAV robustness and efficiency, paving the way for deployment on resource-limited robotic platforms. GitHub page with videos is at https://pred-mppi.github.io/