arXiv AI June 9, 2026 By Ali Kafili Gavgani, Amin Talaeizadeh, Aria Alasty, Hossein Nejat Pishkenari

Hybrid Neural Network and Conventional Controller Approach for Robust Control of Highly Unstable Systems: Application to Tilt-Rotor Control

arXiv:2606. 08714v1 Announce Type: cross Abstract: Multirotors are widely used in applications ranging from surveillance to precision agriculture, yet conventional designs remain limited by their under-actuation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv AI

Jul 21

Generalist AI Control: Towards Multi-purpose Adaptive Algorithms

arXiv:2607. 16313v1 Announce Type: new Abstract: Traditional controllers are designed for specific systems and do not transfer across different system orders and dynamics.

By Klinsmann Agyei, Pouria Sarhadi

agents

arXiv Machine Learning

Jul 1

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

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 Machine Learning

Sep 15

A Machine Learning Framework for Fault Detection, Isolation, and Severity Prediction of Autonomous VTOL Aircraft

arXiv:2609.14180v1 Announce Type: new Abstract: Fault detection in autonomous VTOL aircraft is critical because even minor component degradations can rapidly destabilize multirotor vehicles operating...

By Ripon C. Sarker, Pedram H. Dabaghian, Raman Goyal, Atanu Halder

agents safety

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

reinforcement-learning benchmarks

arXiv AI

Sep 25

Temporal Learning for End-Effector Position Estimation under Aerodynamic Disturbances in Aerial Continuum Manipulation

This study explores temporal neural networks for estimating the end‑effector position of an aerial continuum manipulator (ACM) affected by aerodynamic disturbances from a UAV. An experimental dataset covering stationary and free‑hovering conditions across various robot configurations and altitudes was used to evaluate strain‑parameterized kinematic models and to benchmark a closed‑form continuous‑time (CfC) neural network against an MLP and a GRU. The CfC network achieved a 22 mm RMSE, outperforming the MLP (36 mm) and GRU (28 mm) by 39.5 % and 20.6 %, respectively, demonstrating the advantage of continuous‑time learning for this task.

By Niloufar Amiri, Houman Masnavi, Farrokh Janabi-Sharifi

robotics

arXiv AI

Jun 15

An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts

arXiv:2606. 13794v1 Announce Type: cross Abstract: Nonlinear dynamics and the strong couplings that arise between multiple effectors undermine the assumptions behind conventional, linear control allocation techniques.

By Umut Demir, Aamir Ahmad, Walter Fichter

benchmarks safety

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Generalist AI Control: Towards Multi-purpose Adaptive Algorithms

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

A Machine Learning Framework for Fault Detection, Isolation, and Severity Prediction of Autonomous VTOL Aircraft

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

Temporal Learning for End-Effector Position Estimation under Aerodynamic Disturbances in Aerial Continuum Manipulation

An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts