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Disturbance-aware predictive control for quadrotors with online correction
US & West ·
The DA-KCF framework keeps an offline-identified nominal quadrotor model calibrated with limited hardware data and learns only a lightweight online correction for disturbances from causal state-command-state transitions. These corrections are evaluated along the prediction horizon inside MPC to optimize position and yaw references.
Why it matters
Illustrates how a fixed system model can be paired with online disturbance correction inside a predictive control loop.
arXiv:2610.29559v1 Announce Type: new Abstract: Model predictive control is a high-performance control method, but it incurs comparatively high online computational costs across many control tasks and is challenging to realize in low-level hardware settings.
arXiv:2610.29377v1 Announce Type: cross Abstract: Learning-based path planners based on diffusion and flow matching can generate diverse trajectories from demonstrations but typically lack mechanisms for constraint satisfaction and collision avoidance during deployment.
arXiv:2604.04455v3 Announce Type: replace Abstract: Nonlinear underactuated systems such as two-wheeled inverted pendulums (TWIPs) exhibit a limited region of attraction (RoA), which defines the set of initial conditions from which the closed-loop system converges to the equilibrium.
