{"ID":2861008,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03100","arxiv_id":"2510.03100","title":"A Dimension-Decomposed Learning Framework for Online Disturbance Identification in Quadrotor SE(3) Control","abstract":"Quadrotor stability under complex dynamic disturbances and model uncertainties poses significant challenges. One of them remains the underfitting problem in high-dimensional features, which limits the identification capability of current learning-based methods. To address this, we introduce a new perspective: Dimension-Decomposed Learning (DiD-L), from which we develop the Sliced Adaptive-Neuro Mapping (SANM) approach for geometric control. Specifically, the high-dimensional mapping for identification is axially ``sliced\" into multiple low-dimensional submappings (``slices\"). In this way, the complex high-dimensional problem is decomposed into a set of simple low-dimensional tasks addressed by shallow neural networks and adaptive laws. These neural networks and adaptive laws are updated online via Lyapunov-based adaptation without any pre-training or persistent excitation (PE) condition. To enhance the interpretability of the proposed approach, we prove that the full-state closed-loop system exhibits arbitrarily close to exponential stability despite multi-dimensional time-varying disturbances and model uncertainties. This result is novel as it demonstrates exponential convergence without requiring pre-training for unknown disturbances and specific knowledge of the model.","short_abstract":"Quadrotor stability under complex dynamic disturbances and model uncertainties poses significant challenges. One of them remains the underfitting problem in high-dimensional features, which limits the identification capability of current learning-based methods. To address this, we introduce a new perspective: Dimension...","url_abs":"https://arxiv.org/abs/2510.03100","url_pdf":"https://arxiv.org/pdf/2510.03100v1","authors":"[\"Tianhua Gao\"]","published":"2025-10-03T15:29:05Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.RO\",\"math.OC\"]","methods":"[]","has_code":false}