PRED-MPPI: Disturbance-Preview and Efficient MPPI for Robust Quadrotor Tracking with Hardware Validation
Haodi Zhang, Junwei Ge, Jinya Su, Yongping Pan, Jun Yang, Wen-Hua Chen, Shihua Li
AI summary
Problem
Standard MPPI control degrades under time-varying, mismatched disturbances and unmodeled dynamics, while existing disturbance-aware variants either assume constant disturbances or lack the computational efficiency required for real-time UAV deployment.
Approach
The framework combines a high-order Generalized Extended State Observer for real-time disturbance preview with a Variable Discretization Grid that allocates finer temporal resolution early in the prediction horizon to reduce computation and control variance.
Key results
- Reduces simulation computation time by over 30%
- Lowers mean RMSE by up to 14.6% compared to baseline MPPI in AirSim
- Cuts X-Y hovering error mean/Std by 14.2%/17.9% under ground-effect disturbances on Crazyflie hardware
- Improves wind-disturbance tracking RMSE and Std by 23.4% and 36.8% respectively in real-world tests
Why it matters
Provides a deployable, computationally efficient control framework for resource-constrained UAVs operating in complex, disturbance-rich environments.
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., L1-MPPI, DA-MPPI), which assume constant or matched disturbances, PRED-MPPI lever- ages 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 ex- periments, 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/