Supplementary dataset for paper: "Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling"
Fuente:
Zenodo
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Recurso digital |
| Lingua: | inglese |
| Pubblicazione: |
Zenodo
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866901685587148800 |
|---|---|
| author | Clement, Mihaela-Larisa Farsang, Mónika Poks, Agnes Edelmann, Johannes Plöchl, Manfred Grosu, Radu Bartocci, Ezio |
| author_facet | Clement, Mihaela-Larisa Farsang, Mónika Poks, Agnes Edelmann, Johannes Plöchl, Manfred Grosu, Radu Bartocci, Ezio |
| contents | <p>Supplementary datasets and pretrained models for the paper "Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling" (Clement et al., 2026).</p> <p>This record contains the data and models used in the Sequential-AMPC framework available at the GitHub repository <a href="https://github.com/clementlarisa/seq-ampc">seq-ampc</a><br>(1) vehicle kinematic-obstacle and vehicle dynamic-obstacle datasets generated for this work; and<br>(2) pretrained neural network models used in the experiments.</p> <p>The original SOEAMPC supplementary dataset is available at <a href="https://zenodo.org/records/7846094">DOI: 10.5281/zenodo.7846094</a> from which the quadcopter dataset was used.</p> <p>The code used to generate, train, and evaluate the models in this record is available at the associated GitHub repository for Sequential-AMPC.</p> <p>File organization:</p> <ul> <li><code>vehicle_obs_N_55000.tar.lz</code>: kinematic bicycle model with static obstacle avoidance</li> <li><code>vehicle_8state_obs_N_116000.tar.lz</code>: dynamic single-track vehicle model with static obstacle avoidance</li> <li><code>models.zip</code>: pretrained models for the experiments in this work</li> </ul> <p>For each dataset, files contain initial conditions, MPC input trajectories, predicted state sequences, and associated parameters as documented in the repository README.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19242484 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Supplementary dataset for paper: "Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling" Clement, Mihaela-Larisa Farsang, Mónika Poks, Agnes Edelmann, Johannes Plöchl, Manfred Grosu, Radu Bartocci, Ezio nonlinear model predictive control neural control machine learning recurrent neural networks <p>Supplementary datasets and pretrained models for the paper "Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling" (Clement et al., 2026).</p> <p>This record contains the data and models used in the Sequential-AMPC framework available at the GitHub repository <a href="https://github.com/clementlarisa/seq-ampc">seq-ampc</a><br>(1) vehicle kinematic-obstacle and vehicle dynamic-obstacle datasets generated for this work; and<br>(2) pretrained neural network models used in the experiments.</p> <p>The original SOEAMPC supplementary dataset is available at <a href="https://zenodo.org/records/7846094">DOI: 10.5281/zenodo.7846094</a> from which the quadcopter dataset was used.</p> <p>The code used to generate, train, and evaluate the models in this record is available at the associated GitHub repository for Sequential-AMPC.</p> <p>File organization:</p> <ul> <li><code>vehicle_obs_N_55000.tar.lz</code>: kinematic bicycle model with static obstacle avoidance</li> <li><code>vehicle_8state_obs_N_116000.tar.lz</code>: dynamic single-track vehicle model with static obstacle avoidance</li> <li><code>models.zip</code>: pretrained models for the experiments in this work</li> </ul> <p>For each dataset, files contain initial conditions, MPC input trajectories, predicted state sequences, and associated parameters as documented in the repository README.</p> |
| title | Supplementary dataset for paper: "Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling" |
| topic | nonlinear model predictive control neural control machine learning recurrent neural networks |
| url | https://doi.org/10.5281/zenodo.19242484 |