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of 4 911
pro vyhledávání: '"Leibold A"'
In this work, an adaptive predictive control scheme for linear systems with unknown parameters and bounded additive disturbances is proposed. In contrast to related adaptive control approaches that robustly consider the parametric uncertainty, the pr
Externí odkaz:
http://arxiv.org/abs/2409.01955
Autor:
Leibold, Joachim P., von Grafenstein, Nick R., Chen, Xiaoxun, Müller, Linda, Briegel, Karl D., Bucher, Dominik B.
Optically active solid-state spin systems play an important role in quantum technologies. We introduce a new readout scheme, termed Time to Space (T2S) encoding which decouples spin manipulation from optical readout both temporally and spatially. Thi
Externí odkaz:
http://arxiv.org/abs/2408.14894
Trajectory planning for autonomous driving is challenging because the unknown future motion of traffic participants must be accounted for, yielding large uncertainty. Stochastic Model Predictive Control (SMPC)-based planners provide non-conservative
Externí odkaz:
http://arxiv.org/abs/2406.13396
Physical Human-Machine Interaction plays a pivotal role in facilitating collaboration across various domains. When designing appropriate model-based controllers to assist a human in the interaction, the accuracy of the human model is crucial for the
Externí odkaz:
http://arxiv.org/abs/2405.03502
Publikováno v:
IEEE Transactions on Automatic Control, 2024
Handling uncertainty in model predictive control comes with various challenges, especially when considering state constraints under uncertainty. Most methods focus on either the conservative approach of robustly accounting for uncertainty or allowing
Externí odkaz:
http://arxiv.org/abs/2402.10538
In automated driving, predicting and accommodating the uncertain future motion of other traffic participants is challenging, especially in unstructured environments in which the high-level intention of traffic participants is difficult to predict. Se
Externí odkaz:
http://arxiv.org/abs/2402.00697
We present a stochastic constrained output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances. The approach uses data-driven predictors based on an extension of Willems' fundament
Externí odkaz:
http://arxiv.org/abs/2402.00681
Autor:
Hartig, Florian, Abrego, Nerea, Bush, Alex, Chase, Jonathan M., Guillera-Arroita, Gurutzeta, Leibold, Mathew A., Ovaskainen, Otso, Pellissier, Loïc, Pichler, Maximilian, Poggiato, Giovanni, Pollock, Laura, Si-Moussi, Sara, Thuiller, Wilfried, Viana, Duarte S., Warton, David I., Zurell, Damaris, Yu, Douglas W.
Publikováno v:
Trends in Ecology & Evolution, 2024
New technologies for acquiring biological information such as eDNA, acoustic or optical sensors, make it possible to generate spatial community observations at unprecedented scales. The potential of these novel community data to standardize community
Externí odkaz:
http://arxiv.org/abs/2401.10860
Autor:
Benciolini, Tommaso, Tang, Chen, Leibold, Marion, Weaver, Catherine, Tomizuka, Masayoshi, Zhan, Wei
Autonomous racing creates challenging control problems, but Model Predictive Control (MPC) has made promising steps toward solving both the minimum lap-time problem and head-to-head racing. Yet, accurate models of the system are necessary for model-b
Externí odkaz:
http://arxiv.org/abs/2311.01993
Model Predictive Control (MPC) has been widely applied to the motion planning of autonomous vehicles. An MPC-controlled vehicle is required to predict its own trajectories in a finite prediction horizon according to its model. Beyond this, the vehicl
Externí odkaz:
http://arxiv.org/abs/2310.02843