SA-MPPI: Sensitivity-Aware Model Predictive Path Integral Control for Robust and Agile Quadrotor Flight

Abstract

Reliable quadrotor control in dynamic environments remains challenging due to external disturbances and internal uncertainties. While Model Predictive Path Integral (MPPI) control enables agile maneuvers through samplingbased optimization, its performance often degrades under such unmodeled uncertainties, leading to brittle and unsafe behavior. To address this, we propose SA-MPPI, a novel robust MPC framework that integrates asynchronous guidance with a novel open-loop sensitivity metric. The asynchronous module leverages a slower auxiliary controller to generate an informed sampling distribution, improving convergence without introducing latency. The sensitivity metric penalizes high-variance trajectories under sampled disturbances via nested Monte Carlo rollouts, embedding robustness directly into the optimization. Extensive simulations and real-world quadrotor experiments demonstrate that SA-MPPI outperforms adaptive baselines, reducing tracking errors by up to 47% under significant wind disturbances while achieving over 2× higher computational efficiency. These results highlight SA-MPPI’s ability to deliver low-latency, safe, and predictable control in uncertain, dynamic environments.