arXiv:2607. 09736v1 Announce Type: cross Abstract: The high-angle-of-attack flip maneuver of reusable launch vehicles presents significant challenges for robust trajectory optimization due to the combined effects of highly nonlinear dynamics, aerodynamic uncertainties, and actuator saturation.
By Liwei Chen, Tong Qin
arXiv:2505. 18201v2 Announce Type: replace-cross Abstract: Controlling flapping-wing drones requires controllers that handle time-varying, nonlinear, underactuated dynamics from incomplete, noisy sensor data.
By Romain Poletti, Lorenzo Schena, Lilla Koloszar, Joris Degroote, Miguel Alfonso Mendez
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.
By Amos Alwala, Gabriel da Silva Lima, Wallace Moreira Bessa
arXiv:2505. 15497v3 Announce Type: replace Abstract: Neural networks hold great potential to act as approximate models of nonlinear dynamical systems, with the resulting neural approximations enabling verification and control of such systems.
By Frederik Baymler Mathiesen, Nikolaus Vertovec, Francesco Fabiano, Luca Laurenti, Alessandro Abate
FALCON‑S is a modular, high‑fidelity simulator designed for fixed‑wing aerial robots operating near the ground. It models full 6DoF rigid‑body physics, semi‑empirical ground‑effect aerodynamics, actuator dynamics, sensor noise, and environmental disturbances, and supports both CPU and GPU backends via Torch and NVIDIA Warp for large‑scale reinforcement learning and optimal control. The framework offers a unified interface for various controllers, including RL and optical control algorithms, and allows cross‑validation with X‑Plane and JSBSim for engineering integration and visual fidelity.
By Matteo El Hariry, Pedro Lima, Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez
arXiv:2412. 12036v2 Announce Type: replace Abstract: System identification, the process of deriving mathematical models of dynamical systems from observed input-output data, has undergone a paradigm shift with the advent of learning-based methods.
By Arunabh Singh, Joyjit Mukherjee