Publikationen
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Ewering, J.-H., Volkmann, B., Ehlers, S. F. G., Seel, T., & Meindl, M. B. (2024). Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline. In IEEE Conference on Decision and Control Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2409.09331
Meindl, M., Bachhuber, S., & Seel, T. (2024). AI-MOLE: Autonomous Iterative Motion Learning for unknown nonlinear dynamics with extensive experimental validation. Control engineering practice, 145, Artikel 105879. https://doi.org/10.1016/j.conengprac.2024.105879
Meindl, M., Mönkemöller, R., & Seel, T. (2024). Autonomous Iterative Motion Learning (AI-MOLE) of a SCARA Robot for Automated Myocardial Injection. IFAC-PapersOnLine, 58(24), 380-385. https://doi.org/10.48550/arXiv.2409.06361, https://doi.org/10.1016/j.ifacol.2024.11.067
Meindl, M., Campe, F., Lehmann, D., & Seel, T. (2024). Solving Motion Tasks with Challenging Dynamics by Combining Kinodynamic Motion Planning and Iterative Learning Control. In 2024 European Control Conference, ECC 2024 (S. 1208-1213). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ECC64448.2024.10590944
Pawluchin, A., Meindl, M., Weygers, I., Seel, T., & Boblan, I. (2024). Gaussian process-based nonlinearity compensation for pneumatic soft actuators. At-Automatisierungstechnik, 72(5), 440-448. https://doi.org/10.1515/auto-2023-0237
Meindl, M., Lehmann, D., & Seel, T. (2023). Bridging reinforcement learning and iterative learning control: Autonomous reference tracking for unknown, nonlinear dynamics. (Authorea Preprints). Vorabveröffentlichung online.
Pawluchin, A., Meindl, M., Seel, T., & Boblan, I. (2023). Accurate and agile control of a pneumatic robotic actuator by GP-based feedforward learning. Proceedings on Automation in Medical Engineering, 2(1), Artikel 753. https://www.journals.infinite-science.de/index.php/automed/article/view/753
Halt, L., Meindl, M., Bayer, V., Kraus, W., & Seel, T. (2022). Autonomous Cycle Time Reduction of Robotic Tasks Using Iterative Learning Control. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (S. 12405-12411). ( Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems). IEEE. https://doi.org/10.1109/IROS47612.2022.9981042
Meindl, M., Lehmann, D., & Seel, T. (2022). Bridging Reinforcement Learning and Iterative Learning Control: Autonomous Motion Learning for Unknown, Nonlinear Dynamics. Frontiers in Robotics and AI, 9, Artikel 793512. https://doi.org/10.3389/frobt.2022.793512
Meindl, M., Molinari, F., Lehmann, D., & Seel, T. (2022). Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks. IEEE Transactions on Control Systems Technology, 30(4), 1390-1402. https://doi.org/10.1109/TCST.2021.3109646, https://doi.org/10.48550/arXiv.2104.07620
Meindl, M., Molinari, F., Raisch, J., & Seel, T. (2020). Overcoming output constraints in iterative learning control systems by reference adaptation. IFAC-PapersOnLine, 53(2), 1480-1486. https://doi.org/10.1016/j.ifacol.2020.12.1938