| Backstepping for partially unknown nonlinear systems using Gaussian processes A Capone, S Hirche IEEE Control Systems Letters 3 (2), 416-421, 2019 | 25 | 2019 |
| Localized active learning of Gaussian process state space models A Capone, G Noske, J Umlauft, T Beckers, A Lederer, S Hirche Learning for Dynamics and Control, 490-499, 2020 | 17 | 2020 |
| How training data impacts performance in learning-based control A Lederer, A Capone, J Umlauft, S Hirche IEEE Control Systems Letters 5 (3), 905-910, 2020 | 13 | 2020 |
| Gaussian process-based stochastic model predictive control for overtaking in autonomous racing T Brüdigam, A Capone, S Hirche, D Wollherr, M Leibold arXiv preprint arXiv:2105.12236, 2021 | 11 | 2021 |
| Smart forgetting for safe online learning with Gaussian processes J Umlauft, T Beckers, A Capone, A Lederer, S Hirche Learning for dynamics and control, 160-169, 2020 | 11 | 2020 |
| Parameter optimization for learning-based control of control-affine systems A Lederer, A Capone, S Hirche Learning for Dynamics and Control, 465-475, 2020 | 8 | 2020 |
| Data selection for multi-task learning under dynamic constraints A Capone, A Lederer, J Umlauft, S Hirche IEEE Control Systems Letters 5 (3), 959-964, 2020 | 8 | 2020 |
| An opc ua-based energy management platform for multi-energy prosumers in districts D Bytschkow, A Capone, J Mayer, M Kramer, T Licklederer 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 1-5, 2019 | 8 | 2019 |
| The impact of data on the stability of learning-based control A Lederer, A Capone, T Beckers, J Umlauft, S Hirche Learning for Dynamics and Control, 623-635, 2021 | 6 | 2021 |
| Gaussian process uniform error bounds with unknown hyperparameters for safety-critical applications A Capone, A Lederer, S Hirche arXiv preprint arXiv:2109.02606, 2021 | 3 | 2021 |
| Anticipating the long-term effect of online learning in control A Capone, S Hirche 2020 American Control Conference (ACC), 3865-3872, 2020 | 3 | 2020 |
| Interval observers for a class of nonlinear systems using gaussian process models A Capone, S Hirche 2019 18th European Control Conference (ECC), 1350-1355, 2019 | 1 | 2019 |
| Sector Coupling with Optimization: A comparison between single buildings and combined quarters L Heidemann, D Bytschkow, A Capone, T Licklederer, M Kramer Abstracts from the 8th DACH+ Conference on Energy Informatics, 2019 | 1 | 2019 |
| Dext-Gen: Dexterous Grasping in Sparse Reward Environments with Full Orientation Control M Schuck, J Brüdigam, A Capone, S Sosnowski, S Hirche arXiv preprint arXiv:2206.13966, 2022 | | 2022 |
| Backstepping Tracking Control Using Gaussian Processes with Event-Triggered Online Learning J Jiao, A Capone, S Hirche IEEE Control Systems Letters, 2022 | | 2022 |
| Structure-Preserving Learning Using Gaussian Processes and Variational Integrators J Brüdigam, M Schuck, A Capone, S Sosnowski, S Hirche Learning for Dynamics and Control Conference, 1150-1162, 2022 | | 2022 |
| Abschlussbericht „Multi-Energie Management und Aggregationsplattform (MEMAP)“der TU München T Licklederer, J Mayer, D Bytschkow, V Ahuir, D Birkeneder, A Capone, ... Technische Universität München, Munich School of Engineering …, 2022 | | 2022 |
| The Impact of Data on the Stability of Learning-Based Control-Extended Version A Lederer, A Capone, T Beckers, J Umlauft, S Hirche arXiv preprint arXiv:2011.10596, 2020 | | 2020 |
| The Value of Data in Learning-Based Control for Training Subset Selection A Lederer, A Capone, T Beckers, J Umlauft, S Hirche | | 2020 |
| Confidence Regions for Predictions of Online Learning-Based Control A Capone, A Lederer, S Hirche IFAC-PapersOnLine 53 (2), 1007-1012, 2020 | | 2020 |
Armin LedererTechnical University of MunichBestätigte E-Mail-Adresse bei tum.de
