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.
arXiv:2607. 16313v1 Announce Type: new Abstract: Traditional controllers are designed for specific systems and do not transfer across different system orders and 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.
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...
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.
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.
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.
arXiv:2606. 23444v2 Announce Type: replace-cross Abstract: Accurate dynamics models are critical for informed decision-making in robotic systems, particularly for agile aerial vehicles operating under uncertainty.
arXiv:2606. 02278v1 Announce Type: cross Abstract: State-space models are traditionally based on physical knowledge, but multi-step predictions from these physical models can be poor due to model inaccuracy.
arXiv:2504. 01250v2 Announce Type: replace Abstract: This paper presents the Robust Recurrent Deep Network (R2DN), a scalable parameterization of robust recurrent neural networks for machine learning and data-driven control.
A modular deep Recurrent Neural Network (RNN) is presented that enables easy deployment of various RNN architectures and automatic derivative computation for gradient-based learning. The modular design introduces new architectures, notably one with feedforward inter‑layer connections, which markedly improves the RNN’s ability to learn high‑order dynamics and nonlinearities while mitigating vanishing/exploding gradients. These advantages are illustrated through a quadrotor altitude dynamics case study, where the proposed network learns the model more quickly and generalizes better than existing methods.
arXiv:2607. 11734v1 Announce Type: cross Abstract: Differentiable simulators have advanced policy learning and model-based control, yet actuator dynamics remain an important source of sim-to-real error.
arXiv:2601. 23225v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (RL) is increasingly deployed in resource-constrained environments, yet go-to function approximators - multilayer perceptrons (MLPs) - are often parameter-inefficient due to an imperfect inductive bias for the smooth structure of many value functions.