Zobrazeno 1 - 10
of 5 917
pro vyhledávání: '"Zeilinger, A"'
Autor:
Degner, Maximilian, Soloperto, Raffaele, Zeilinger, Melanie N., Lygeros, John, Köhler, Johannes
We consider the problem of optimizing the economic performance of nonlinear constrained systems subject to uncertain time-varying parameters and bounded disturbances. In particular, we propose an adaptive economic model predictive control (MPC) frame
Externí odkaz:
http://arxiv.org/abs/2412.13046
Autor:
Lahr, Amon, Näf, Joshua, Wabersich, Kim P., Frey, Jonathan, Siehl, Pascal, Carron, Andrea, Diehl, Moritz, Zeilinger, Melanie N.
Incorporating learning-based models, such as Gaussian processes (GPs), into model predictive control (MPC) strategies can significantly improve control performance and online adaptation capabilities for real-world applications. Still, despite recent
Externí odkaz:
http://arxiv.org/abs/2411.19258
This paper presents a robust moving horizon estimation (MHE) approach with provable estimation error bounds for solving the simultaneous localization and mapping (SLAM) problem. We derive sufficient conditions to guarantee robust stability in ego-sta
Externí odkaz:
http://arxiv.org/abs/2411.13310
We propose integrating an explicit approximation of a predictive control barrier function (PCBF) in a safety filter framework. The approximated PCBF is implicitly defined through an optimal control problem and allows guaranteeing invariance of an imp
Externí odkaz:
http://arxiv.org/abs/2411.11610
Rank collapse, a phenomenon where embedding vectors in sequence models rapidly converge to a uniform token or equilibrium state, has recently gained attention in the deep learning literature. This phenomenon leads to reduced expressivity and potentia
Externí odkaz:
http://arxiv.org/abs/2410.10609
We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements. Our approach leverages multi-step predictors to efficiently propagate uncertainty, ensuring chance constraint sa
Externí odkaz:
http://arxiv.org/abs/2409.10405
Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-world applications. Yet, for computational tractability, most approach
Externí odkaz:
http://arxiv.org/abs/2409.08616
One of the most challenging tasks in control theory is arguably the design of a regulator for nonlinear systems when the dynamics are unknown. To tackle it, a popular strategy relies on finding a direct map between system responses and the controller
Externí odkaz:
http://arxiv.org/abs/2409.05685
Autor:
Sturm, E., Davies, R., Alves, J., Clénet, Y., Kotilainen, J., Monna, A., Nicklas, H., Pott, J. -U., Tolstoy, E., Vulcani, B., Achren, J., Annadevara, S., Anwand-Heerwart, H., Arcidiacono, C., Barboza, S., Barl, L., Baudoz, P., Bender, R., Bezawada, N., Biondi, F., Bizenberger, P., Blin, A., Boné, A., Bonifacio, P., Borgo, B., Born, J. van den, Buey, T., Cao, Y., Chapron, F., Chauvin, G., Chemla, F., Cloiseau, K., Cohen, M., Collin, C., Czoske, O., Dette, J. -O., Deysenroth, M., Dijkstra, E., Dreizler, S., Dupuis, O., van Egmond, G., Eisenhauer, F., Elswijk, E., Emslander, A., Fabricius, M., Fasola, G., Ferreira, F., Schreiber, N. M. Förster, Fontana, A., Gaudemard, J., Gautherot, N., Gendron, E., Gennet, C., Genzel, R., Ghouchou, L., Gillessen, S., Gratadour, D., Grazian, A., Grupp, F., Guieu, S., Gullieuszik, M., de Haan, M., Hartke, J., Hartl, M., Haussmann, F., Helin, T., Hess, H. -J., Hofferbert, R., Huber, H., Huby, E., Huet, J. -M., Ives, D., Janssen, A., Jaufmann, P., Jilg, T., Jodlbauer, D., Jost, J., Kausch, W., Kellermann, H., Kerber, F., Kravcar, H., Kravchenko, K., Kulcsár, C., Kunkarayakti, H., Kunst, P., Kwast, S., Lang, F., Lange, J., Lapeyrere, V., Ruyet, B. Le, Leschinski, K., Locatelli, H., Massari, D., Mattila, S., Mei, S., Merlin, F., Meyer, E., Michel, C., Mohr, L., Montargès, M., Müller, F., Münch, N., Navarro, R., Neumann, U., Neumayer, N., Neumeier, L., Pedichini, F., Pflüger, A., Piazzesi, R., Pinard, L., Porras, J., Portaluri, E., Przybilla, N., Rabien, S., Raffard, J., Raggazoni, R., Ramlau, R., Ramos, J., Ramsay, S., Raynaud, H. -F., Rhode, P., Richter, A., Rix, H. -W., Rodenhuis, M., Rohloff, R. -R., Romp, R., Rousselot, P., Sabha, N., Sassolas, B., Schlichter, J., Schuil, M., Schweitzer, M., Seemann, U., Sevin, A., Simioni, M., Spallek, L., Sönmez, A., Suuronen, J., Taburet, S., Thomas, J., Tisserand, E., Vaccari, P., Valenti, E., Kleijn, G. Verdoes, Verdugo, M., Vidal, F., Wagner, R., Wegner, M., van Winden, D., Witschel, J., Zanella, A., Zeilinger, W., Ziegleder, J., Ziegler, B.
MICADO is a first light instrument for the Extremely Large Telescope (ELT), set to start operating later this decade. It will provide diffraction limited imaging, astrometry, high contrast imaging, and long slit spectroscopy at near-infrared waveleng
Externí odkaz:
http://arxiv.org/abs/2408.16396
Direct volume rendering using ray-casting is widely used in practice. By using GPUs and applying acceleration techniques as empty space skipping, high frame rates are possible on modern hardware. This enables performance-critical use-cases such as vi
Externí odkaz:
http://arxiv.org/abs/2407.21552