arXiv Machine Learning By Amos Alwala, Gabriel da Silva Lima, Wallace Moreira Bessa

Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics

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arXiv:2606. 31199v1 Announce Type: cross Abstract: The control of agile quadrotors in dynamic and uncertain environments remains an open area of investigation to this day, particularly when the complete system dynamics are partially known or highly nonlinear.

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arXiv Machine Learning
Jul 3

Wind-Aware Reinforcement Learning Control of a Small Quadrotor Using Learned Onboard Wind Estimation in Simulated Atmospheric Turbulence

arXiv:2607. 01528v1 Announce Type: new Abstract: Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's airspeed degrade trajectory tracking and can defeat conventional feedback control.

By Abdullah Al Tasim, Wei Sun
arXiv Machine Learning
Sep 10

A robust and adaptive MPC formulation for Gaussian process models

The paper introduces a robust and adaptive model predictive control framework for uncertain nonlinear systems with bounded disturbances and unmodeled nonlinearities, leveraging Gaussian Processes to learn dynamics from noisy measurements. It derives robust predictions for GP models using contraction metrics, integrating them into the MPC formulation to ensure recursive feasibility, robust constraint satisfaction, and convergence to a reference state with high probability. A numerical example involving a planar quadrotor experiencing challenging ground effects demonstrates significant performance gains from the robust prediction method and online learning.

By Mathieu Dubied, Amon Lahr, Melanie N. Zeilinger, Johannes K\"ohler